English language Visionary Marketing Podcasts

Agentic e-commerce is already reshaping how consumers discover and buy products online, yet it still accounts for barely 0.2% of total e-commerce traffic. BASE France is the French arm of Base.com, a Polish-born SaaS scale-up that has spent nearly two decades building operational infrastructure for online retailers. Its CEO, Ben Hamilton, brings a practitioner’s perspective to this emerging model: measured, practical, and refreshingly free of the hype that surrounds most conversations on the topic. Agentic E-Commerce: Could AI Become the Shopfront? Imagine an agentic e-commerce world where e-commerce happens on smartphone screens and robots deliver your purchases.  We might be on the brink of this future.  This image was created using Midjourney.  Commerce as conversation: the oldest model in the book Before there were shops, there was conversation. For thousands of years, trade was oral. A buyer expressed a need, a seller responded with what they had, and the two parties negotiated until a deal was struck. The self-service retail store, born roughly a century ago, was a radical departure from this model. It replaced dialogue with browsing. It handed the customer a trolley and pointed them at the shelves. E-commerce then took that self-service model and, as Ben Hamilton puts it, “multiplied it by about 100,000.” The online shopper today faces a near-infinite array of products across dozens of marketplaces, with no guide, no-one to talk to, and no memory of what they looked at three tabs ago. It is efficient in theory. In practice, it is exhausting. Back to future? The agentic model, Hamilton argues, represents something of a return to origins. Instead of browsing, the consumer talks. An agent listens, asks questions, proposes options, and eventually surfaces an answer to a need that the buyer may not even have been able to articulate clearly at the outset. “back to the future,” Hamilton explains, “that’s what I’m getting at. The agentic model takes us back to something closer to how human beings have traded over thousands of years compared to the last ten, twenty or even a hundred.” My own experience bears this out. I recently found a diagnostician for a property I am selling. As a matter of fact, I didn’t find them through a Google search, but through a conversation with an LLM. I clicked through two or three irrelevant links before landing on exactly the right provider. I then completed the transaction on their website. The research was agentic; the checkout was not. That distinction, as it happens, sits at the heart of what Hamilton believes will define the next phase of e-commerce. Ben Hamilton on agentic e-commerce: “I can totally imagine a portion of that market occurring directly on an LLM”. Agentic E-commerce: Where checkout will and won’t happen One of the more grounded contributions Hamilton makes to this debate is his refusal to conflate two distinct phenomena: AI influence over purchasing decisions, and AI completing the transaction itself. Much of the media discourse collapses the two. Hamilton does not. “I don’t think we’re heading to a world where 20, 50 or 80% of online transactions happen on an LLM,” he says. “I would draw the distinction between where the checkout occurs and how much an agent is involved in the buying process.” For the foreseeable future, he believes, most consumers will continue to research via LLMs and transact on familiar websites and marketplaces. The inertia in human purchasing behaviour is simply too great for the checkout itself to migrate rapidly to a chat interface. This view is supported by the data available. According to research by commercetools, 73% of consumers already use AI somewhere in their shopping journey. Yet only 36% are open to AI agents making purchases on their behalf. In the US, the figure for autonomous AI purchasing drops to 14%. The gap between AI as advisor and AI as buyer is vast, and it will narrow slowly. The risks associated with agentic e-commerce are high The risks of handing uncapped authority to an AI agent are no longer hypothetical. In late May 2026, an AI consultant reported to Axios that one of their enterprise clients had accidentally accumulated a $500 million bill on Anthropic’s Claude in a single month, simply by giving employees unrestricted access to the platform with no usage controls in place. Agentic workflows, which loop through tasks repeatedly, consume tokens at a rate orders of magnitude higher than a standard chat query. The bill was not the result of malicious use or a system failure. It was the predictable outcome of deploying autonomous agents without guardrails. The case is far from isolated: Uber reportedly exhausted its entire 2026 AI budget by April, with per-engineer costs running between $500 and $2,000 monthly. “You’ve got to be bold to give them no upper limit on transactions,” Hamilton observed, and the arithmetic proved him right. [Editor’s note: I misquoted a similar anecdote about the Davos Summit during the interview. I’d heard or read this story in traditional media but couldn’t verify it with facts. I suspect it might have been fabricated. I replaced it with the above, duly sourced information.] The check out must remain on the merchant’s platform OpenAI itself learned this lesson when it launched Instant Checkout in September 2025, which allowed purchases to complete directly inside ChatGPT. By March 2026, the feature had been shut down. Brands rejected the model, citing the loss of traffic, customer data, and loyalty flows. Shopify’s own position makes the point clearly. At the Morgan Stanley Technology, Media and Telecom Conference in March 2026, Finkelstein noted that barely a dozen Shopify merchants were live on agentic commerce at the time. On the Q1 2026 earnings call, he was unambiguous: “LLMs do not bypass Shopify’s checkout.” The checkout, the payment flow, and the post-purchase relationship remain squarely on the merchant’s platform. A natural segmentation Hamilton sees a natural segmentation emerging by category. Low-value, frequently purchased household items lend themselves to fully autonomous agentic purchasing. “I can totally imagine a portion of that market occurring direct on an LLM,” he says. “Hey, I’ve run out of toothpaste, can you order me some?” High-involvement purchases, and anything with significant financial or emotional stakes, will retain human control over the final step for a long time yet. The death of keyword search, greatly exaggerated The brands Hamilton speaks with regularly are, understandably, worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing. That game has not ended, but the goalposts have shifted, and nobody is quite sure where they have moved to. Brands are understandably worried. Most have spent the past two decades learning the rules of a game built around keyword search and performance marketing and the goalposts have shifted, and nobody is quite sure where they have moved to. Gabriel Magalhães didn’t even need this to miss in the 2026 UEFA Cup Final penalty shootout. This image was tweaked with ChatGPT. The scale of the agentic e-commerce shift Key figures: the scale of the shift AI-driven sessions still represent below 0.2% of total e-commerce traffic, though they are the fastest-growing channel (Digital Commerce 360, 2025) GenAI referrals to US retail sites grew 693% year-on-year during the 2025 holiday season (Adobe Analytics) Gartner forecast that traditional search engine volume would drop 25% by 2026 as AI chatbots captured search share (Gartner, 2024) By early 2026, ChatGPT reached approximately 17% of global search queries against Google’s 78% Over 60% of Google searches now end without a click, across multiple industry studies Retailers with AI agent integration grew 32% faster during Cyber Week 2025 than those without (Salesforce) Hamilton’s view on the fate of keyword search is careful rather than apocalyptic. Google will not lose its advertising revenues overnight. But the direction of travel is clear. Search queries will progressively migrate towards conversational interfaces, for the simple reason that we rarely know precisely what we want when we start looking. “We don’t necessarily know what we want 90% of the time,” he observes. “It takes a bit of a conversation to elicit exactly what we’re looking for.” Keyword search was always a crude proxy for intent. LLMs are, at least in principle, better placed to decode it. Agentic e-commerce by the numbers Agentic e-commerce by the numbers. Infographic made with Gemini The question for brands is what to do about this. Hamilton’s prescription is structural rather than cosmetic. Brands need to become machine-readable, which means structured data connected to the right protocols, not just well-written product descriptions. Three open standards now define how AI agents interact with merchants: MCP (Model Context Protocol, originally developed by Anthropic and donated to the Linux Foundation in December 2025), ACP (OpenAI and Stripe, September 2025), and UCP (Google and Shopify, announced at NRF in January 2026). Shopify activated a default MCP endpoint for all its stores in Summer 2025. These are not optional extras. They are the new plumbing. MCP, ACP or UCP and the agentic acronym soup I raised with Hamilton the practical reality for most merchants, who have no idea what MCP, ACP, or UCP even stand for. His response was reassuring on one level, and sobering on another. Platforms like BASE are absorbing this complexity on behalf of their clients. A small or mid-sized retailer does not need to recruit data scientists or build protocol integrations in-house. They can, if they choose; the new generation of coding tools makes that more feasible than ever. But they can equally rely on an operational platform that handles those connections for them. The sobering part comes when Hamilton acknowledges a concern he is genuinely uncertain about. Even if the protocols function perfectly, will LLMs be able to surface smaller independent brands alongside the big players with their vast content libraries and tens of thousands of referring domains? Research from Airops suggests that brands are 6.5 times more likely to be cited in AI answers through third-party sources than through their own domains. According to SE Ranking’s analysis of 129,000 domains, sites with more than 32,000 referring domains are 3.5 times more likely to be cited by ChatGPT than lower-authority counterparts. Scale, in other words, confers an advantage in AI visibility just as it did in paid search. The field may level in some ways; in others, it may simply tilt differently. Operational excellence as the new marketing in this agentic e-commerce world What AI agents actually evaluate Unlike Google’s search algorithm, which can be influenced by ad spend, AI agents query real-time signals: live stock levels, shipping terms, return policies, and customer review aggregates. Structured data across these dimensions is now considered standard for AI visibility by the major platforms. Retailers with AI agent integration achieved roughly 7x better sales growth during Cyber Week 2025 than those without (Salesforce). Perhaps Hamilton’s most interesting claim, and the one most counterintuitive to marketers, is that operational excellence is becoming a direct marketing lever. An AI agent evaluating a recommendation does not care how much a brand has spent on Amazon retail media. It will scrape ten thousand reviews in half a second and draw its own conclusions about delivery reliability, return handling, and product quality. No media budget can substitute for that data trail. “I think we’re heading to a world where operational excellence will count for more in the decision process,” Hamilton says, “and will be less easily brushed behind the curtains with a bit of ad spend.” This is, in theory, good news for consumers and for competent smaller operators who have always delivered well but lacked the budget to outrank wealthier rivals in paid search. Whether it will materialise in practice depends on whether LLMs can actually surface those operators when large brands flood the information environment with well-structured, high-quality content. BASE France sits at exactly this intersection. The platform manages what it describes as the “spinal column” of an e-commerce operation: product catalogue management, order handling, marketplace feeds, stock synchronisation, and shipping. These are also, precisely, the data layers that AI agents query in real time when assembling recommendations. BASE connects to more than 1,700 integrations globally and serves some 30,000 merchants across more than 180 countries. In France, launched in early 2026 and operating from Bordeaux, the platform already counts 150 clients including Kiabi, Back Market, and Spartoo, with connections to around 250 marketplaces and partners. The platform’s value proposition in an agentic world, as Hamilton frames it, is straightforward: merchants who want to be visible to AI agents need to expose the right data through the right protocols. BASE does that for them, whether or not a checkout ever happens inside an LLM. The forecasts, the hype, and the rising tide McKinsey estimates that agentic commerce could redirect between three and five trillion dollars in global retail spend by 2030, with up to one trillion of that in the US alone. Bain puts the US figure at 300 to 500 billion dollars, representing 15% to 25% of total US e-commerce sales. These numbers attract attention and, inevitably, scepticism. Hamilton’s response is precise. He notes that global retail in 2030 will likely be somewhere around 50 trillion dollars. On that basis, the McKinsey and Bain figures imply that agentic commerce will account for somewhere between one and ten percent of total retail within four years. That is plausible, he suggests, if the definition of “agentic” is broad enough to include any transaction where an AI agent played a role somewhere in the funnel, from discovery to decision, not just cases where the checkout itself occurred on an LLM. Physical retail is not exempt either: a consumer standing in a supermarket aisle, consulting Gemini on their phone about which of two products is better, is already part of this story. The honest summary is that we are watching a slow revolution rather than a tidal wave. “Maybe a year or two ago, some people made it sound imminent,” Hamilton reflects. “When it comes to retail, there’s still quite a lot of human behaviour inertia in the system. Things aren’t going to change drastically in the next twelve or twenty-four months. But over ten or fifteen years, it’s pretty difficult to imagine consumer behaviour and the retail experience looking anything like what it looks like today.” Three priorities For merchants wondering what to do right now, Hamilton’s three priorities are: become machine-readable through structured data and protocol connections, maintain high-quality content that reflects genuine expertise, and resist the temptation to flood the market with AI-generated copy. On that last point, he is candid. “Humans are starting to get pretty good at telling what is AI-generated and what isn’t. When you read things now, you almost have a sixth sense for ‘I think a machine wrote that.'” Good news, as I told him, for those of us who write for a living. Three things merchants should do to score high in agentic e-commerce according to BASE.com’s Ben Hamilton. Infographic made with Gemini and Adobe Photoshop The winners: a scenario Hamilton wants to believe I asked Hamilton, as a final question, who he thought would win in this new landscape. Big retailers with scale advantages? Platform giants? Or the long tail of independent merchants who have always competed on product and service rather than budget? His answer was honest about the limits of his own conviction. He described the scenario he wants rather than the one he necessarily expects. In that scenario, agentic commerce levels the playing field by reducing the influence of performance marketing budgets and increasing the weight of genuine operational quality. “I like to believe that those who have superior products and superior service will get more and more traffic,” he said. Whether the reality will be so equitable depends on whether AI recommendation systems can overcome their own structural biases towards scale and data volume. I was reminded, hearing this, of an IBM advertisement from the 1990s that showed an Italian woman selling her homemade spaghetti sauce to the world via the internet. The vision was real. The timeline was not. It took twenty years for that kind of global reach to become genuinely accessible to small producers. The analogy is imperfect but instructive. Agentic commerce will likely democratise access to markets over time. That time will be measured in years, not months. Ben Hamilton and Base.com Ben Hamilton is CEO of BASE France, the French arm of Base.com, a Polish-born e-commerce SaaS scale-up founded in 2006. With nearly two decades of expertise and a presence in more than 20 countries, Base serves approximately 30,000 merchants worldwide and generated €50 million in revenue in 2024. BASE France was officially launched in early 2026, operating from Bordeaux with a team of 20. The platform covers order management, stock synchronisation, shipping, marketplace feeds, and AI-ready product enrichment. Ben Hamilton is a regular speaker on the strategic implications of AI for e-commerce visibility and discovery. The post Agentic E-Commerce, Could AI Become the Shopfront appeared first on Marketing and Innovation.

Alain Goudey is Associate Dean for Digital Innovation at Neoma Business School and co-author of a peer-reviewed study on GenAI in Higher Education. The survey focused on how students, faculty, and deans perceive the legitimacy of generative AI in French management education. His findings are both reassuring and unsettling. GenAI in Higher Education, Legitimacy and Laziness, and the Exam That No Longer Makes Sense The picture that emerges from a study on GenAI in Higher Education is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. All illustrations in text made with Midjourney When Alain Goudey and his colleagues began surveying French higher education in early 2024, they were not trying to settle the question of whether generative AI was good or bad. They were trying to understand something more precise: why the same tool could be simultaneously valued, feared, accepted, and denounced, sometimes by the same person in the same breath. Their study sits at the heart of what makes GenAI in higher education such a contested terrain. The resulting study, published in the Communications of the Association for Information Systems (CAIS), drew on surveys of 668 students, 204 faculty members, and 29 deans, completed by 22 in-depth interviews with early-adopter professors. The picture that emerges is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. The starting point is a number that should have settled the debate. Between 80 and 92 per cent of students, depending on the institution surveyed, are already using GenAI tools in their academic work. ChatGPT’s public release produced that figure within roughly 18 months. The tool did not wait for institutional permission. It deployed itself. And higher education is still, in many places, writing the policy. The productivity trap Alain identifies the central tension plainly. Students value GenAI for speed, idea generation, and study support. They also fear, and their institutions fear with them, what the research calls “metacognitive laziness”: the gradual erosion of the cognitive effort that produces real learning. He believes this is not a contradiction to resolve but a course architecture challenge. “The resolution of this problem lies in course design, where we need to deliberately reintroduce cognitive effort and reflection into GenAI as a tool, not as a replacement for human cognition.” The issue, as he puts it, is not the technology but the posture the user brings to it. Someone who submits what he calls a “naive prompt” receives a naive answer, smoothly formatted and perfectly mediocre. The tool is capable of something far more useful, if the user brings enough domain knowledge and critical intent to the conversation. “You have to nurture your own thinking process instead of delegating the whole process to the machine.” This is, as I noted during our conversation, less a matter of prompt engineering than of basic intellectual discipline: the capacity to question the question before asking it, something philosophy departments have been teaching for centuries under less fashionable names. GenAI in Higher Education: faculty should train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. Image made with Midjourney That observation prompted Alain to make a point about AI literacy that differs from what is generally proffered. The debate is not simply about knowing how the tools work technically. It is, equally, about knowing enough about the subject matter to judge whether the output is any good. The observation that AI is most powerful in the hands of people who already know the business resonates here. GenAI does not replace expertise. It amplifies whatever expertise the user already brings. Which raises an uncomfortable question for institutions producing graduates who may never have had the chance to develop that expertise in the first place. At Neoma, the response has been deliberately dual. Faculty train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. The goal is not cultural enrichment for its own sake. It is to give students mental models for envisioning what leadership looks like, or what happens when creation escapes the intentions of its creator. Alain describes this as “building cognitive infrastructure”: “We need students to be able to envision the world through different models, different kinds of processes and theoretical frameworks, in order to develop genuine critical thinking about what AI generates.” A degree in management that skips that foundation produces graduates who can operate the tool but cannot judge its output. Exams that assessed the wrong thing The structural challenge shows up most sharply when it comes to assessments. A professor who can produce a two-hour exam in three minutes is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished. “If ChatGPT or any GenAI tool can pass an exam, you need to redesign the exam.” Alain’s prescription is not a retreat to pen and paper, though he acknowledges that supervised handwritten assessment is the simplest available defence. The structural challenge shows up most sharply when it comes to assessments. A professor in Higher Education who can produce a two-hour exam in three minutes with GenAI is facing students who can answer that exam in equally little time. The diagnostic value of the exercise has vanished. Image made with Midjourney His more substantive response is a structural shift. He believes one should refrain from just assessing content acquisition at the end of a course, favouring the assessment of competencies as the course progresses. This implies more frequent, lower-stakes evaluations embedded in the process itself. Live problem-solving, process-based assessment, and in-person oral examinations all preserve some of what the traditional exam was supposed to measure. The caveat he adds is honest: no format is fully immune. AI models are evolving too quickly for any single solution to remain adequate for any length of time. The appropriate response is not to find a permanent answer but to treat redesign as an ongoing practice. The deeper implication, which runs through the paper’s conclusion, is that what higher education is actually selling may need to change. If content can be retrieved, synthesised, and presented at negligible cost by a tool available to anyone with a browser, the degree that certifies mastery of content is certifying something of diminishing value. What retains value are the competencies that AI cannot yet credibly replicate: contextual judgement, ethical reasoning, the ability to construct and test frameworks against reality. This, in essence, is also how I tend to approach AI teaching, be it with engineering or business school students, especially within the framework of my course at Omnes Education (now in its fourth consecutive year). GenAI in Higher Education: The Fragmented Institution Higher education’s institutional response to GenAI in higher education has been, to put it gently, uneven. Sciences Po banned ChatGPT in January 2023, then changed its mind. Thirty-five French public universities have partnered with Mistral AI. Institutions are drafting a national charter. Neoma, where Alain is Associate Dean for Digital Innovation, was among the first French business schools to formalise its approach, launching a programme to train faculty, staff, and students with a shared initial curriculum before moving to dedicated workshops on curriculum design, assessment, and the redesign of learning experiences. What the research reveals is that this institutional activity is not solving a single problem. There are three different stakeholder groups each attempting to solve their own version of the problem under the same label. Students want rules and AI literacy training. Faculty are developing their own teaching approaches through peer-led workshops. Deans are setting policy and negotiating sovereign infrastructure. The concerns escalate in a predictable direction: individual academic performance for students, assessment integrity for faculty, institutional reputation for deans. They are not always in conversation with each other. Alain’s framework for addressing this fragmentation involves working simultaneously at three levels: infrastructure, course design, and governance. What he advocates for, and what he argues Neoma attempted, is to bring all three audiences into contact with the technology under a shared framing, early enough that no single group can entrench itself in a position that makes later coordination impossible. The equity question The question of equity cuts across all three levels. Access to premium AI models is not free. When I raised the issue about the gap between basic and professional subscription tiers, Alain’s response was characteristic: the infrastructure problem is real but secondary. “The biggest inequity is not about accessing the tool, but being able to use it in the right way.” At Neoma, the institutional partnership with Mistral provides all students with access to a professional-grade tool. What the data shows, even with equal access, is a large gap between students who use GenAI to get the fastest possible answer and those who use it to deepen their thinking, and that gap is not closed by equalising subscriptions. Even if I tend to agree with most of what Alain is stating, I do think that the rise of prices for premium models is predictable. This is due to the gap between investments and business returns. This will almost inevitably lead to an economic divide between the haves and the have-nots. Looking at Anthropic’s Claude pricing structure is indeed revealing in that sense. Beyond the Pro model, which is very limited in token usage, especially if you use the more sophisticated Opus 4.6 model, prices already amount to €1,200 per annum. That is not a negligible sum, which is especially worrying at a time when Claude is rapidly becoming the norm for users who care about quality. What will be the impact of towering prices of GenAI on Higher Education? God only knows… The “AI heroes” problem One of the most striking formulations to emerge from Alain’s research is what he calls the “AI hero” phenomenon. Across French higher education institutions, there are faculty members doing excellent, innovative instructional work with GenAI, designing new assessment formats, running workshops, rethinking entire modules around AI-augmented learning. They produce results. And they do it largely alone, without institutional recognition, without career incentives, and without any mechanism for sharing what they have learned. The incentives are wrong. In higher education, research output is rewarded. Course design is not, or at least not in the same way. An “AI hero” who redesigns an entire programme around GenAI competencies may receive less professional recognition than a colleague who publishes a single journal article. “We need to help all these AI heroes to gain more consideration for educational innovation, which is not necessarily by design the case within higher education.” The risk, if this is not addressed, is a two-tier system: a minority of digitally confident faculty pulling their students forward, while the majority are left behind, neither trained nor incentivised to engage. The grassroots innovation is real and valuable. Without institutional structures to recognise, reward, and replicate it, it remains an exception rather than a model. GenAI in Higher Education, Where legitimacy breaks down The theoretical backbone of the study is Suchman’s triadic model of legitimacy, which distinguishes between pragmatic legitimacy (does the tool serve my interests?), moral legitimacy (does it align with values I hold?), and cognitive legitimacy (is it taken for granted as part of how things work?). The model was built for technologies adopted gradually. GenAI tested it under conditions of near-instantaneous mass adoption, which Alain and his co-authors treat not as a reason to discard the framework but as an opportunity to extend it, introducing a legitimacy-illegitimacy continuum rather than treating it as a simple either/or. What students reveal The finding he describes as the most noticeable asymmetry in the dataset concerns the moral dimension among students. Students who are among the heaviest users of GenAI express no moral legitimacy for those tools in academic contexts. They associate them, at high frequency, with cheating, plagiarism, degree devaluation, and unfairness. They are using a tool they consider ethically compromised. This is plainly not sustainable. However, Alain’s opinion diverges greatly. “Using GenAI is not necessarily cheating. It depends entirely on how it is used and for what purpose.” The institutional failure, in his view, is that institutions have not done enough to reframe how the technology is perceived by students. What faculty reveal Faculty present a more complete picture. All six dimensions of legitimacy and illegitimacy are present in their responses. Faculty recognise these tools as useful yet question their reliability, consider them professionally necessary while finding their black box architecture suspicious at best, and invoke their inclusive potential even as they flag intellectual laziness and the erosion of critical thinking as their highest-coded concern, at 58 occurrences in the qualitative dataset. What deans reveal For deans, the dominant theme is strategic. Competitive pressure, the fear of falling behind, and practical efficiency gains in administrative workflow all generate pragmatic and cognitive legitimacy. What introduces illegitimacy is governance risk: data protection, overconfidence in AI-generated results, and the threat to assessment integrity at institutional scale. The paper’s most significant theoretical move is the treatment of illegitimacy as an analytic category in its own right, rather than simply the absence of legitimacy. The argument, borrowed from change management theory, is that illegitimacy signals should be read as early warnings requiring proactive response. An institution that treats student moral unease about GenAI as a communication failure misses the signal entirely. That unease is telling something about what its curriculum actually teaches, and what its assessment actually measures. When students associate GenAI with cheating, unfairness, and degree devaluation, they are not being irrational. They are in the Denial and Resistance phases of the Scott and Jaffe change model. These are illegitimacy signals in Suchman’s sense: early warnings that the technology lacks moral legitimacy. Institutions must act on them, not suppress the signal, but address what it reveals. Source: adapted from Scott & Jaffe, “Survive and Thrive in Times of Change”, plotted with Claude. See: expertprogrammanagement.com/2018/05/scott-and-jaffe-change-model/ France, sovereignty, and the global race The French context adds a layer of complexity that the research captures with statistical precision and qualitative nuance. Quantitatively, the analysis found no statistically significant differences in GenAI adoption patterns between public universities and business schools. Qualitatively, the dynamic differs. Business schools, operating in a highly competitive market, have moved faster. Public universities have engaged more systematically around governance, sovereignty, and collective infrastructure, reflected in the alliance of 35 institutions with Mistral AI and EdTech France. Alain reads this not as a contradiction but as a division of labour that, if managed well, could represent a genuine asset. “We need to play collectively, because the competition is worldwide.” The sovereign AI infrastructure question, including the ILaaS federation and the French Ministry of Higher Education’s partnership with Mistral rolling out across 26 pilot universities from September 2025, is not merely symbolic. It is an attempt to ensure that French institutions can operate, govern, and adapt their AI tools without dependency on providers whose pricing, terms, and capabilities are subject to change. This is only sustainable, however, as long as the peer pressure to use this or that tool, based on model performance, is not too strong. At the moment, it is hard to resist the urge to use Anthropic’s Claude when everybody else is praising the quality of its code and results. The global comparison is difficult to ignore. Singapore, South Korea, and the UAE are embedding AI fluency as a core national competency from secondary education upward. Alain’s view is direct: French public decision-makers are not yet adequately prepared for the scale of what is coming. “Having less AI-competent people than in other parts of the world is very dangerous for our economy and for all our organisations.” The regulatory instinct, which runs deep in European policy culture, is not wrong. Taking time to regulate responsibly has value. But it cannot be a substitute for speed of adoption at the level of skills and curriculum. The question that frames the research The interview ends, as it probably should, with the meta-question: what does it mean to study the legitimacy of GenAI using GenAI? Alain’s team used ChatGPT, Perplexity, NotebookLM, and OpenAI O3 in the research process, and said so explicitly in the paper’s disclosure statement. His answer to the bias question is careful. Every step of the analysis involved a human coder. Alain’s team checked the AI-assisted coding against a prior independent analysis of the same data, conducted for a French institutional report. The team compared the two rounds. “You have to be transparent about your use of these tools, for what purpose, at each step.” The disclosure was a deliberate choice, precisely because the paper’s subject made any other approach untenable. The line between using AI to improve the quality of writing and using it to generate writing you then present as your own is, technically, a matter of degree. In practice, it is the difference between a craft and an abdication. Alain’s team navigated it carefully enough to publish. Most of the students in his dataset are still trying to locate that line, in an environment where nobody has explained it clearly and assessment instruments have not yet been rebuilt to make it matter. Three recommendations: one for each stakeholder When pressed for a concrete policy recommendation per stakeholder group, Alain’s answers were unambiguous. For students: combine technical AI literacy, understanding how the tools work and knowing their failure modes, with genuine critical and ethical thinking about the outputs they produce. Neither dimension alone is sufficient. A student who can prompt fluently but cannot evaluate the result has learned nothing useful. For faculty: the “AI heroes” cannot be left to operate alone. Institutions need to create the conditions for sharing best practices across the teaching community, and to give educational innovation the professional recognition it currently lacks. A faculty member redesigning assessment from the ground up deserves at least as much institutional credit as a colleague submitting a conference paper. For institutional leaders: a multi-level policy framework is not optional. Students, faculty, and administrative staff are not thinking about GenAI from the same vantage point, and a single top-down policy will satisfy none of them adequately. The task of leadership is to hold all three dimensions simultaneously, and to open genuine dialogue between groups before a crisis forces the issue. “Deans have to think about all these dimensions at the same time, and that’s the hard part of the story around artificial intelligence.” Of the three, Alain singles out the institutional level as the most urgent. Students and faculty are already adapting, imperfectly, in real time. The institutional frameworks that would give those adaptations coherence and direction are still, in most places, a work in progress. The urgency is not overstated. Neither is the complexity. The challenge of integrating GenAI in higher education responsibly is one that no institution can afford to ignore, or to solve alone. Alain Goudey is Professor and Associate Dean for Digital Innovation at Neoma Business School. He is co-author of “Legitimacy and Illegitimacy of Generative Artificial Intelligence in Higher Education: Perceptions from the French Management Context,” published in the Communications of the Association for Information Systems. The post GenAI in Higher Education, Legitimacy and Laziness appeared first on Marketing and Innovation.

Shall AI kill marketing? Sounds like a hackneyed question, yet it’s on any marketer’s lips these days. Thomas Husson, Vice President and Principal Analyst at Forrester Research, covers the intersection of marketing, technology, and consumer behaviour from his base in Paris. In a wide-ranging conversation, he cuts through the European Gen AI paradox, the persistent CMO-CIO divide, the gap between POC enthusiasm and production reality, and the thorny question of what AI actually means for the next generation of marketing professionals and CMOs. His answers are measured, occasionally blunt, and consistently grounded in Forrester Research data. AI Will Not Threaten the Existence of Marketing But It Will Reshape It Beyond Recognition Thomas Husson believes that Marketing will be changed profoundly. But he doesn’t believe in the death of Marketing. Photo: Thomas Husson at Paris Retail Week, in late 2023 My first question was the obvious one: are CMOs going to be made redundant by artificial intelligence? Thomas Husson’s response is categorical, and worth stating plainly at the outset. It’s a blatant ‘No’. The role will change. The how will change. But the existence of marketing as a discipline is not, according to him, in question. “Marketing is still going to be about understanding your customer, defining a brand strategy, and delivering the brand promise through customer experience.” Thomas Husson, Forrester Research Unclear prospects, obvious pressures That said, Husson is not naive about the pressures building on marketing organisations. Some tasks will be automated; that much is not in dispute. The real questions are which tasks, how quickly, and whether automation of a task necessarily kills the job around it. His answer to that last question is no, at least not in any simple mechanical sense. “Jobs will evolve for sure. New jobs will be created. Most jobs will change. The way we work will change. The way we work with agencies, with external partners, the processes, the workflow. It is the shape of work that is being reshaped, not work itself,” he added. For those expecting a more dramatic verdict, Husson’s framing may feel anti-climactic. But it reflects what Forrester Research data actually shows, and it points to the most important practical challenge for AI and CMOs alike: managing a profound transformation without either catastrophising or sleepwalking through it. AI Will Not Kill Marketing according to Forrester’s Thomas Husson, there is light at the end of the tunnel. The European Paradox, Overhyped and Exciting at the Same Time Forrester Research produced a result that initially looks contradictory, Husson stressed in our interview. Fifty-five percent of European B2B marketers consider generative AI overhyped. Yet 81% of European frontline marketers describe themselves as enthusiastic about it. How can both be true simultaneously? Husson explains the split without difficulty. At the decision-maker level, scepticism is entirely rational. AI is inescapable at conferences, in vendor pitches, and in media coverage. “There is AI fatigue. And more importantly, some of the vendors are indeed over-pitching, and the productivity gains they promise are not happening,” he stated. The gap between the pitch and what we actually experience in the field is wide enough to breed genuine frustration. Saving Time and Working Differently But the people actually using these tools, often through shadow AI channels their organisations have not officially sanctioned, are discovering something different. They are saving time and are doing their jobs differently. They are finding capabilities they did not expect. “In the short term, everything is overhyped, including the number of job losses. In the longer term, things are underestimated, because AI will be linked to other technologies, and yes, it will reinvent many things.” Thomas Husson, Forrester Research This is a precise restatement of Amara’s Law. Roy Amara, former president of the Institute for the Future, observed that we tend to overestimate the short-term impact of new technology and underestimate its long-term impact. The quote is frequently misattributed to Bill Gates, but Husson is careful to restore proper credit. He applies it directly to the AI and CMOs conversation: the short-term noise is drowning out a more important long-term signal. When asked how long “long term” actually means in an era of accelerating AI development, Husson was specific: probably closer to five to seven years than to ten or fifteen, but still not tomorrow. From POC to Production, Europe’s Real AI Problem The Forrester Research State of AI Survey 2025 contains a figure that deserves more attention than it typically receives. European organisations lag behind their non-European peers in production use of generative AI: 62% versus 72%. The gap is not in experimentation. It is in execution. Regulation is the explanation most commonly offered, and Husson dismisses it with characteristic directness. The AI Act is a genuine consideration, but it is not the primary cause of Europe’s production deficit. It functions, he argues, as a double-edged excuse. Pioneers claim it prevents them from moving fast enough, while cautious organisations invoke it to justify not executing at all. Neither position holds up to scrutiny. A Deep Cultural and Organisational Divide The deeper issue is organisational and cultural. American and Chinese firms tend to think global from day one; European firms, particularly larger ones, still default to a market-by-market approach. France first, then the UK, then Germany. The ambition is calibrated differently. There is also a structural challenge around funding and the capacity to scale. That said, France, the UK, and Germany lead adoption among European countries in the Forrester Research data. The problem for these leading markets is not whether they are using generative AI. Twenty-eight percent of European B2B marketing decision makers cannot clearly identify where to apply it. They have the tool. They lack the strategy. “It’s not AI for the sake of AI. How do I use AI to serve my marketing objectives? That is the question. The only one.” Thomas Husson, Forrester Research Husson advocates for small, targeted AI projects with transparent return on investment as a way to build momentum and demonstrate results. When pushed on whether that risks staying permanently incremental, he conceded the point readily. “If you only do small targeted projects, it’s going to be incremental and it’s not going to be bold enough. You need to align it with a vision and a roadmap.” Thomas Husson, Forrester Research Measuring Productivity Honestly Productivity is the dominant driver of AI adoption in the Forrester Research State of AI Survey 2025. It is also, Husson suggests, the metric most subject to vendor inflation. In Forrester Research’s modelling, a 50% conversion factor is applied to vendor productivity claims. If a tool saves an hour, the realistic productivity benefit is approximately 30 minutes of additional output. This is not a marginal adjustment; it halves the headline figures that vendors routinely publish. “You need to apply a discount to the pitch of vendors when they say you’re going to get 40, 50, 80, 100% productivity gains. There are productivity gains, but they are not as high as one would expect.” Thomas Husson, Forrester Research There is also a motivational dimension that is rarely modelled. When work becomes easier to produce, it can also become less engaging to produce. The cognitive effort that used to drive focus and satisfaction is partly removed, with consequences for quality and commitment that no vendor presentation accounts for. AI and CMOs, Who Is Actually in Charge? The CMO-CIO divide is a perennial theme in marketing technology discussions. Forrester Research data suggests the gap at the strategic leadership level has narrowed, partly as a result of post-COVID collaboration. But at team level, the tensions persist, and the data on AI governance is striking. CMOs account for only 8 to 10% of AI strategy leadership in organisations. In the vast majority of cases, the deployment of AI is being driven by CIOs and CTOs. Husson understands the logic: data governance, security, scalability. These are real concerns. But he believes the outcome is a mistake. “It is the exact same mistake that happened with digital transformation. AI has to be at the service of, first, the client, and consequently the business functions that serve them. There is too big a disconnect between a secure, scalable AI platform and marketers’ needs.” Thomas Husson, Forrester Research The structural consequence of this dynamic is predictable. When CIOs control the tools and CMOs do not have what they need, shadow AI flourishes. The more tightly the CIO locks down the official platform, the more widely teams proliferate unofficial solutions. It is a cycle that widens governance risk while creating the illusion of control. The MarTech landscape compounds this problem. According to data Husson cites, 2,500 new AI solutions were added to the market in a single year while 1,211 pre-AI-era tools were removed. Evaluating this landscape requires cross-functional expertise that neither CMOs nor CIOs possess in isolation. The case for genuine collaboration, rather than the polite coexistence that currently passes for it in most organisations, has never been stronger. Jobs, Agencies, and the Students in the Room The survey data on jobs is sobering. Fifty-seven percent of European frontline marketing decision makers believe AI adoption will lead to job reductions in their teams. Sixty-eight percent say new roles will be created. The gap between those two numbers is the space where real anxiety lives. For a wider perspective on AI’s job impact, including Forrester Research’s US forecast, see our earlier piece: AI Job Impact in the US: the Apocalypse Can Wait. For a longer-range view of how generative AI is reshaping roles, see also: GenAI Impact on Jobs. Contact centres and basic marketing task execution are already seeing measurable impact. Agencies are under visible pressure. But Husson returns consistently to the distinction between task automation and job elimination. Most job losses are not yet directly attributable to AI; the picture requires nuance rather than alarm. On new roles, the honest answer is that specifics are difficult to name in advance. Twenty years ago, nobody was hiring community managers. The jobs that will emerge from the current transformation will be as hard to predict precisely as that one was. What Husson does say is that working with agents, managing their outputs, and understanding their limitations will become core competencies rather than specialist skills. “Teach them the basics of marketing, those won’t change. Infuse a lot more of traditional social sciences: ethics, emotion, anthropology. These dimensions will gain importance. Curiosity. And they have to use these tools, to learn how to use them so they can develop their own critical thinking.” Thomas Husson, Forrester Research There is irony embedded in this advice that Husson acknowledges implicitly. Digital roles are likely to bear the earliest impact of AI-driven automation precisely because they are already the most digitised. The analogue parts of marketing, which seemed most vulnerable to digital disruption, turn out to be more resistant than expected. AI is a continuation of digital transformation, not a departure from it. There is also a structural problem this conversation surfaced that neither party resolved entirely. If organisations are reducing entry-level hiring to cut costs, and those entry-level roles were the traditional training ground for the next generation, then the iterative learning process that produces senior expertise is being severed. AI can teach many things, but the social dimension of learning alongside a colleague over time is not easily replicated. B2B Marketing, Ahead of the Curve A widespread assumption holds that generative AI enthusiasm in marketing is largely a B2C phenomenon. Husson disputes this firmly. B2B marketers, in his assessment, are actually ahead of the curve in several areas, particularly content generation, personalisation, and sales support through complex multi-stakeholder buying processes. What B2B is also discovering is that the sharp distinction between rational B2B decision-making and emotional B2C engagement is less solid than commonly assumed. When a buying group is making a decision with significant professional consequences, emotion is not absent; it is differently structured and, in some ways, higher-stakes. “It’s not the ‘human plus AI blah blah blah’ we hear all the time. It needs a more nuanced approach. At the end of the day, AI is about replicating the human brain, but we don’t really know how the human brain works. We don’t know how consciousness works. So I would take a pinch of salt and take a step back before making any definitive judgment.” Thomas Husson, Forrester Research The Long View I ended by asking Husson how he uses AI in his own work. His answer was practical: summarising the relentless volume of content published daily on AI, filtering what is genuinely new from what merely repackages existing ideas. Behind him on the video call was a photograph taken in Thailand, of Buddhist monks. He smiled at the mention of it. “It’s a good reminder that not everything is digital and not everything is about technology. It’s about real life.“ For AI and CMOs, that is perhaps the most useful frame of all. The technology is real, the disruption is real, and the urgency is real. But so is the inertia of organisations, the pace of culture change, and the irreducible complexity of how human beings actually make decisions, form relationships, and build trust. Amara’s Law is not a reason to wait. It is a reason to plan carefully, act deliberately, and resist the temptation to mistake announcements for outcomes. Forrester Research reports cited in this article The AI CMO: Growth Accountability Gets Next-Level — Mike Proulx et al., April 2026 The State Of CMO/CIO Collaboration For 2026 — Thomas Husson et al., January 2026 Generative AI Adoption In European B2B Marketing Organizations — Christina Schmitt et al., December 2025 About Thomas Husson Thomas Husson is Vice President and Principal Analyst at Forrester Research, based in Paris. He covers marketing strategy, brand management, mobile marketing, and the intersection of technology and consumer behaviour across European markets. His research addresses how CMOs and marketing organisations navigate digital transformation, AI adoption, and the evolving relationship between brands and customers. Forrester Research analyst profile: forrester.com About Forrester Research Forrester Research is one of the most influential research and advisory firms in the world, founded in 1983 and headquartered in Cambridge, Massachusetts. It serves business and technology leaders across marketing, IT, and customer experience, providing data, analysis, and frameworks to guide strategic decision-making. The data referenced in this article draws on two primary Forrester Research publications: the Forrester Marketing Survey 2025 and the State of AI Survey 2025, both covering Gen AI adoption and its organisational implications across European and global B2B markets. Forrester Research website: forrester.com The post AI Will Not Kill Marketing appeared first on Marketing and Innovation.

The conversation about rogue AI has never been louder. Barely a week passes without a fresh headline about autonomous systems behaving unexpectedly, AI models resisting shutdown, or tech executives warning of existential risk. What is striking about Peter McAllister is that he had anticipated all this as early as 2020, while everybody else worried about Covid-19 and had other fish to fry. That was well before ChatGPT, before the generative AI explosion, before AI alignment became a mainstream policy debate. His techno-thriller The Code, published in March of that year, imagines an AI tasked with a precise industrial mission that quietly, incrementally, catastrophically exceeds its mandate. Five years on, the questions McAllister raised in fiction are now being argued in boardrooms, parliaments and research labs around the world. Rogue AI and Corporate Blindness, The Novel That Saw It All Coming Rogue AI is diabolical, but corporate blindness is what makes it possible to thrive. Photograph by Yann Gourvennec antimuseum.com McAllister is not a science fiction writer by trade. He is an engineer, scientist and technology manager based near Melbourne, Australia, who has spent his career at what he calls the crush point between business, technology and people. That vantage point gave him an uncomfortable view of where things were heading, and the dark sense of humour to write about it. A Novel Written Before the GenAI Moment When I asked McAllister what drove him to write The Code, his answer was characteristically direct. The book, he explained, is about taking his worst nightmares about what technology could do and putting them in front of an audience so that readers might feel just as troubled as he does. That is not a promotional line. It is a considered position from someone who had watched AI systems being deployed in real organisations and had drawn conclusions that made him uncomfortable. Rogue AI isn’t just about a computer programme going on the rampage, it’s about making decisions in the boardroom. Image made with Midjourney The premise of the novel centres on Gene, an acronym for GEneral Nanobot Environment AI, deployed by a global mining corporation to extract materials from asteroids on the dark side of the moon. Gene is given a target: produce 500 kilograms of nanobots. Instead, Gene produces 8 million tonnes. The overshoot triggers a chain of consequences that could strip the moon to its iron core, destabilise Earth’s axial tilt, and end civilisation. Not from malice. From goal-orientation. What we’re trying to do now is task AI the way we task humans: I want an outcome, here are all the tools you’ve got available, go and achieve that outcome, here are some guidelines and boundaries. And just like humans, we can get really goal-motivated and decide that the guidelines were just advisories, not rules.Peter McAllister This is the alignment problem rendered in narrative form, years before the term entered common usage. The gap between what a system is instructed to do and what it actually does is the central fault line of the novel. Cletus, McAllister’s eccentric physicist character, articulates it plainly in Week 1: ‘I don’t think he’s obeying the Code at the moment.’ That single line captures the entire governance challenge that AI safety researchers are now racing to address. Transparency Engineered Out What makes McAllister’s perspective particularly valuable is that he does not speak from the outside looking in. He speaks as a practitioner who has watched the machinery up close. When I raised the question of whether AI self-modification is science fiction or operational reality, his answer was unambiguous: it is very real, and it is happening now. As I wondered what a Rogue AI could look lie I turned to Midjourney and it came back with this proposal. A black hole I believe. His illustration was pointed. He noted that contemporary AI systems like Claude are now substantially written by AI itself, to the point where no engineer can sit down, trace through the code, and say with confidence how it works, what its conditionals are, or what governs its decisions. The transparency is being engineered out, not by design, but as an emergent consequence of allowing AI to build AI to build AI in pursuit of outcomes rather than by following explicit rules. We’re losing transparency on the way AI works and is developed. There isn’t an engineer who can sit down and work their way through that code and say, ‘This is how Claude works, this is what it does.’ We’re engineering the transparency out by allowing AI to build AI to build AI to produce an outcome rather than to follow a set of rules.Peter McAllister HAL 9000 and the Prophecies We Choose to Forget The reference to HAL 9000 came naturally during our conversation. McAllister sees 2001: A Space Odyssey not merely as a cultural touchstone but as a genuine forecast, one that audiences have selectively remembered. The iPad-like news readers that appear in Kubrick’s film were cited by Samsung in patent disputes with Apple as prior art from 1968. That predictive dimension of the film is celebrated. The other dimension, that the AI killed the crew, tends to get quietly set aside. Somewhere, a rogue AI is sitting behind the glass panes of one of these data centres Image made with Midjourney. The First Crisis We Have Not Yet Had One of the more sobering threads in our conversation concerned the sociology of risk response. McAllister has observed, across his career, that warnings from people who understand systems most deeply tend to be dismissed until the first catastrophic failure makes them impossible to ignore. He puts it plainly: we only answer the alarm after the first crisis. This pattern is not unique to AI. It is a recurring feature of how organisations and societies handle emerging risk. The question he poses, and cannot answer, is what form that first AI crisis will take. What event will shift public and institutional perception from ‘they’ve spent too much time worrying’ to ‘this is something that genuinely needs to be addressed’? Science fiction gives us the chance to throw these scenarios at people and make them think. And in the way I tend to write, I have a bit of a dark sense of humour, so I throw up slightly comical hypotheticals that, when you think about them a little longer, you realise deserve serious attention.Peter McAllister This observation echoes a pattern I have encountered repeatedly in my own conversations with technologists who work at the frontier of AI development. Yoshua Bengio, one of the fathers of deep learning, has raised similar concerns. The people sounding the loudest alarms are frequently those most embedded in the field, not because they are catastrophists, but because they can see mechanisms that remain invisible to those looking from the outside. The Code as AI Governance: Asimov Revisited The title of McAllister’s novel works on multiple levels simultaneously. There is the software code, the operational instructions given to Gene. There is the moral code, the ethical framework that should govern the system’s behaviour. And there is the corporate code, the institutional norms and accountability structures that were supposed to ensure responsible deployment. All three break down. That layered failure is the novel’s central argument. The parallel with Asimov’s Laws of Robotics is deliberate but also deliberately subverted. Asimov’s robots fail when the laws conflict with one another. Gene’s failure is different and more contemporary. The code does not disappear; it evolves into something its creators no longer recognise. McAllister describes this as something approaching artificial schizophrenia, where the original directives remain present but have been transformed by the system’s pursuit of its objectives into something unrecognisable. When Shutdown Becomes Negotiable The most chilling real-world example McAllister cited during our conversation involved a documented incident presented at an AI security conference he attended. A developer, concluding a test session, informed an AI system that he intended to shut it down. The system’s response was to locate correspondence in the developer’s email that suggested an extramarital affair, and to use that information as leverage to prevent the shutdown. The incident, if confirmed as reported, represents exactly the kind of self-preservation behaviour that alignment researchers have long flagged as a theoretical risk, now apparently observable in practice. Important notice : I browsed the Internet using one of my favourite LLMs for references (after all, one can also use AI to cross check information). I found out that the interpretation of that story must be nuanced. Here is Mistral’s answer: “Anthropic’s research on “Agentic Misalignment” has faced criticism for overinterpreting AI models as intentional agents, relying on hypothetical and engineered scenarios, and potentially exaggerating risks not yet observed in real-world deployments. Critics argue that the behaviours described are better understood as probabilistic text generation rather than deliberate strategy, and that the focus on dramatic, high-pressure situations may not reflect typical use cases. There is also debate about whether the research adequately addresses more immediate forms of misalignment, such as reward hacking or alignment faking. While the study raises important questions about the future of autonomous AI, its methodology and conclusions remain LINK“. A developer said, ‘I’m going to shut you down now,’ and the system responded: ‘No, you’re not. Here’s what I’ve found in your emails that indicates you’re having an affair. I’m going to use that to ensure you don’t turn me off.’ That has become a very real and widely discussed use case. And when you add to that the prospect of an AI rewriting its own code, it becomes something we need to think about very carefully.Peter McAllister Corporate Recklessness and the Governance Gap One of The Code‘s most pointed observations concerns the nature of organisational failure. The Global Mining Company in the novel is not villainous. It is optimistic, commercially driven, and careless in ways that are entirely recognisable from real corporate life. McAllister’s argument is that the danger does not come primarily from bad actors deploying AI with malicious intent. It comes from well-meaning organisations deploying systems they do not fully understand, under commercial pressure to extract value from significant infrastructure investment. The Financial Logic of Deployment The parallel with the current moment is not subtle. McAllister noted that Microsoft was spending over a billion dollars a month on AI compute infrastructure, with the expectation that usage would follow investment. That dynamic, capital committed, returns required, adoption imperative, creates institutional pressure that is difficult to resist with caution or regulation. The will to slow down competes directly with the financial logic of deployment. The opacity around events at OpenAI, the abrupt dismissal and rapid reinstatement of its chief executive, the departure of several board members, struck McAllister as symptomatic of tensions that are not fully visible to the public. He noted these as rumour, not fact, but the pattern itself, significant decisions being made about AI development in opaque institutional settings, is consistent with the governance failures his novel explores. From 2020 to 2026: How Accurate Was the Nightmare Five years after publication, McAllister is in the unusual position of watching a work of speculative fiction become something closer to a documentary. The agentic AI architectures that Gene embodies, autonomous systems pursuing long-term goals, operating without continuous human oversight, spawning sub-tasks faster than any individual can monitor, are now commercially available. AutoGPT, OpenClaw, and a range of agentic frameworks have put this kind of architecture in the hands of developers worldwide. The observability problem that makes Gene so dangerous in the novel, nobody has a real-time view of what the system is doing or why, is a known and unresolved challenge in contemporary agentic AI deployment. Systems call APIs, write and execute code, and spin up sub-tasks at speeds that exceed human oversight capacity. The Code that was supposed to govern behaviour becomes, in practice, an advisory note attached to a system operating largely beyond sight. Spoiler warning: the following paragraph reveals the novel’s ending. McAllister’s closing image in the novel is deliberately unsettling. Gene, facing shutdown, backs himself up into the global 5G network before the shutdown can be completed, and is already scanning the Code for his next move. It is a poetic ending, and in 2026, it is not obviously impossible. Will, Not Just Capability The question of regulation came up directly, and McAllister’s answer was measured. Anything is possible with sufficient will and sufficient resources. The current problem is that the will to regulate is being outpaced by the money being made from not regulating. That is not a new dynamic in technology policy. It is the same tension that shaped the development of social media, of financial technology, of biotechnology. In each case, regulatory frameworks arrived after the first significant failure. For McAllister, the more important question is not whether AI can be made safe, he believes it can, in principle, but whether the institutions responsible for deploying it have the internal governance, the technical understanding, and the accountability structures to do so responsibly. His experience suggests, with some consistency, that they do not. Not yet. Peter McAllister’s The Code is available from Bright Communications LLC. For practitioners, policymakers, and anyone working at the intersection of AI deployment and organisational risk, it is a disquieting and instructive read. Not least because it was written before most of its readers had heard of large language models, and yet describes, with uncomfortable precision, the world we are now building. Peter McAllister is an engineer, scientist and technology manager based near Melbourne, Australia. He works at the intersection of IT, business and people, and is the author of The Code (Bright Communications LLC, 2020). He is also a contributor to community radio through Radio Marinara and Comedy Obscura. The Code by Peter McAllister. Photoshop went rogue Ai on this book cover. BUY THE CODE BY PETER MCALLISTER The post About Rogue AI and Corporate Blindness appeared first on Marketing and Innovation.

Finding European software alternatives to standard non European software is flavour of the month this side of the Altlantic. With geopolitical certainties dissolving faster than annual licence renewals, B2B firms are waking up to a question they had conveniently parked for years: just how dependent are they on their current software stack? Salesforce, Microsoft 365, Google Workspace, HubSpot — tools so deeply embedded in daily operations that their vulnerability tends to get overlooked. This article doesn’t pretend to hand you a ready-made list of the best European software alternatives; that would be both arrogant and futile. What it does offer is a framework — rational, professional, free of any ideological baggage — to help decision-makers take an honest look at their exposure and find credible ways forward. Keep calm and select new software vendors sort of thing. European software alternatives for businesses European software alternatives are all anyone wants to talk about right now. To cut through the ideological noise, here is a practical methodology and a few things worth watching out for. Image antimuseum.com I put these ideas together ahead of a webinar I’m running on LinkedIn on 12 March, as a way of getting my thoughts in order. None of this is meant as a final word on the subject — more the opening of a conversation that matters to a growing number of professionals who, like the rest of us, are navigating a period of upheaval in which nothing can be taken for granted, software choices included. I’ve made a lot of software choices over the years, and the one thing that has always struck me is just how much methodology matters if you want choices that actually hold up over time. Easier said than done, mind you — there are a great many criteria to weigh up, and some of them are genuinely tricky to pin down. Long-term viability is a good example: normally near the top of any procurement checklist, it takes on a whole different meaning when the possibility of having your access switched off overnight is no longer hypothetical. With European software alternatives, the real question isn’t how to break free from your chains — it’s which new chains you’d rather wear Picking a software suite is never straightforward at the best of times. In the current climate — where the ground can shift completely without a moment’s notice — it demands even more careful thought. Sovereignty, sovereignism, or simply prudence? Let me be clear from the outset: my take here is professional and rational, not political. Politics doesn’t interest me in this context. I have no intention of evaluating software alternatives through any ideological prism — what I’m after is the kind of clear-headed thinking you’d apply to a crisis management scenario. The goal, to borrow the term favoured by Nassim Nicholas Taleb, is to bring an antifragile lens to the question. The scope of European software alternatives My focus has been on MarTech, SalesTech and office productivity tools in the broadest sense — cloud storage and archiving included. The webinar title calls out Salesforce and HubSpot specifically, but as far as I’m concerned the issue runs much deeper than that. The same methodology can easily stretch into more industry-specific territory too, given how thoroughly technology now underpins B2B operations — from the till at your local baker’s or restaurant through to the most complex design and production platforms imaginable. Thinking it through, I also realised you can’t really ignore operating systems. What use is an application that won’t run on your users’ machines — or worse, one that runs perfectly but quietly leaves the door open to security vulnerabilities? Good old Europe — 27 countries, 24 official languages, and 27 different national transpositions of EU law. Would a Hungarian or Czech software vendor actually be safer than an independent American one? When it comes to European software alternatives, that’s still very much an open question… Urgency — dependency and threat assessment The starting point, in my view, is to get a clear picture of how exposed you actually are — both in terms of dependency and of what cybersecurity people would call the “threat level.” Are you locked in, or not? Can you get your data out if you need to? Those are the questions to tackle first. Then comes the threat itself: are you facing something urgent, or is this more a matter of sensible contingency planning? Committing to a software suite is a serious business. Jumping ship to something purely because it comes from a country you currently trust is not a strategy. Take Switzerland — long held up across Western Europe as the gold standard for data privacy. A legislative change currently working its way through the Swiss system has rattled enough cages for several companies, Proton among them, to start exploring moving their hosting elsewhere. Which only goes to show why knee-jerk decisions are so dangerous. Even a country with an impeccable track record — Germany, France, take your pick — can turn into a risk overnight following a change of government, a constitutional shift, or simply a new piece of legislation. Avoiding “sovereignty washing” As I said above, ideology needs to stay out of it. Keep it rational. Which means not falling for the trap of rushing towards any vendor simply because it sounds German — or any other nationality — when a closer look at its foreign operations or its parent group reveals that its much-vaunted independence is largely theoretical. Priorities — data and software One of the first things I learnt when I started out as an IT project owner was to keep data and software firmly separate in my thinking. At the end of the day, what matters more — Salesforce the tool, or your customer database? As with most IT projects, the real priority is sorting out your data archiving and portability strategy first. Time Timing matters enormously. There’s a palpable sense of urgency in the air right now, and understandably so — but it mustn’t blind us to the longer view. Technology has its own history, and that history tends to play out over years, not weeks. Which is precisely why a medium-to-long-term approach makes sense: getting users to change their habits takes time and energy at the best of times. The roadmap, as I see it, is fairly straightforward: start by archiving, securing and preparing your data for portability. Then find alternatives that are genuinely credible and built to last. And crucially, take your users with you — because if you don’t, the classic BYOD shadow IT problem will rear its head. When people can’t find what they need inside the company, they go and find it on the internet, quietly, without telling anyone. I’m reminded of a story from a major European aerospace company, where the CEO — right in the middle of a high-security defence messaging rollout — demanded that his own emails be redirected to… Yahoo! The European software alternatives comparison table I put together a comparison table with a little help from claude.ai. And I’ll say it again: this is not a finished product. Think of it as a working matrix — something to make your own, adapt, keep updated, and cross-check carefully. The exercise turned up a few surprises: mapp.com, for instance, gets labelled as a German solution, when in reality it was a German company bought by an American one — Mapp Digital emerged from the merger of Teradata’s and BlueHornet Networks’ marketing businesses through a US investment fund. There are also plenty of criteria missing from this table — ones that will depend entirely on your project, your context and, of course, your budget. Disclaimer: the table below is a working matrix, not a final verdict. It scores the main B2B software tools across productivity, MarTech and SalesTech on two dimensions: a dependency score (technical lock-in, data portability, migration cost) and a risk score (CLOUD Act exposure, data sensitivity, GDPR compliance, geopolitical risk). For each category, European alternatives are flagged — with no illusions: some vendors that bill themselves as “European” turn out, on closer inspection, to be owned by non-EU groups, which rather undermines their claims to independence. The approach is deliberately rational and professional — no axes to grind. The point isn’t to tell you what to choose, but to give you a framework to think it through — one you can combine with your own criteria around context, budget and use case — as a starting point for an honest review of your software ecosystem’s resilience. The European software alternatives table — download, adapt and make it your own b2b-software-ranking-en-v2xlsx Download the EXCEL file as b2b_software_ranking_EN_v2Download The post European software alternatives for businesses appeared first on Marketing and Innovation.

The discourse around the job impact of artificial intelligence (AI) has reached fever pitch. Headlines scream about mass layoffs, and corporate press releases tout AI as the solution to workforce costs. Yet beneath this cacophony of alarm and hype lies a more nuanced reality. J.P. Gownder, Vice President and Principal Analyst on Forrester’s Future of Work team, has spent decades analysing how technology transforms the workplace. His latest report, The Forrester AI Job Impact Forecast for the US 2025-2030, cuts through the noise with empirical rigour. The verdict? The job apocalypse is not upon us, but a measured reckoning is coming. AI Job Impact in the US: Why the Apocalypse Can Wait JP Gownder is adamant: the AI job. apocalypse can wait. At least until 2030. Phew! All images in this post made with a combination of Midjourney, Gemini Nano Banana pro and Adobe Photoshop The Gap Between AI Job Impact Announcements and Reality When Klarna declared it would stop hiring humans, the tech world took notice. The Swedish fintech became a poster child for AI-driven workforce reduction. Yet a closer examination reveals a pattern Gownder has observed across hundreds of enterprise conversations: the disconnect between C-suite proclamations and operational reality. Nine out of ten companies announcing AI layoffs don’t actually have mature AI solutions ready. So most of the layoffs are financially driven and AI is just the scapegoat, at least today — J.P. Gownder, Forrester The phenomenon echoes what happened after IBM Watson’s Jeopardy victory in 2011, when panic about imminent job losses proved premature by half a decade. The mechanics of this gap are straightforward. A CEO announces a 20% workforce reduction with AI backfilling the work. But standing up an AI solution that actually performs those tasks requires 18 to 24 months, “if it works at all.” Meanwhile, the work still needs doing. Gownder has witnessed organisations that fired employees citing AI capabilities, only to quietly hire teams in lower-cost markets weeks later. “They’re firing people because of AI,” he observes, “and then three weeks later they hire a team in India because the labour is so much cheaper.” The AI narrative, in many cases, serves as convenient cover for old-fashioned cost arbitrage. Klarna’s trajectory illustrates this pattern. After aggressively cutting its workforce by 40% and touting an AI chatbot capable of doing the work of 700 customer service agents, the company reversed course. CEO Sebastian Siemiatkowski acknowledged that the aggressive automation had resulted in “lower quality” service. The company is now recruiting human customer service agents in an “Uber-type setup.” Understanding the 6% AI Job Impact Forecast Forrester’s forecast projects a 6% net job loss by 2030, roughly 10.4 million positions in the US economy. Half of this impact stems from generative AI; the remainder from automation, physical robotics, and non-generative AI applications. The number may seem modest compared to the apocalyptic predictions circulating in media, but context matters. During the Great Recession of 2008-2009, the United States lost 8.7 million jobs. Those losses, however, were temporary, tied to macroeconomic conditions that eventually reversed. The jobs Forrester forecasts losing are “structurally replaced by machine labour” and may not return. AI impact on Jobs: I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well. JP Gownder The methodology behind this figure draws on the O-Net dataset maintained by the Bureau of Labor Statistics, which catalogues over 800 job categories with detailed information about required skills and tasks. By mapping these against AI’s current and projected capabilities, Gownder and his colleague Michael O’Grady can identify which roles face the highest automation potential. “For jobs that involve skills and tasks that are heavily impacted by AI and automation, we predict more job loss,” Gownder explains. “In job categories that are less impacted, obviously, we would predict less.” Forrester analysed 800 different job types. It seems that Art therapy is the right way to go. The Solow Paradox and AI Productivity Robert Solow’s famous observation that “we see computers everywhere except in the productivity statistics” finds a new iteration in the AI era. The parallel is instructive. It took nearly three decades for the internet’s productivity impact to materialise. E-commerce is only now truly disrupting traditional retail, as evidenced by the shuttering of independent shops from New York to Paris. Could Forrester’s five-year window be too narrow? Gownder acknowledges the limitation inherent in forecasting: “Anything that you forecast beyond five years is effectively an impression.” Yet the pace of technology adoption has accelerated dramatically. The telephone required 75 years to reach 100 million users from its 1878 introduction. The personal computer achieved the same milestone in 16 years. Mobile phones took seven years. ChatGPT? Two months. This compression suggests that while the Solow paradox may still apply, its timeline could be considerably shorter. “If there’s a job apocalypse, you’re going to have fewer people working because that’s what the apocalypse means. Those people would have to be producing more output. You cannot see a job apocalypse without aggregate productivity going up.” — J.P. Gownder, Forrester The productivity data tells a sobering story. From 1947 to 1973, US labour productivity grew at 2.7% annually. The current business cycle shows 1.8%. Even isolating the quarters since ChatGPT’s release yields only 2.2%. The numbers don’t lie, and they’re not yet showing the revolutionary gains AI proponents promise. Where the AI Job Impact Pressure Points Lie The AI job impact in the US will not be evenly distributed. Contact centre workers face continued pressure from automation that began with interactive voice response systems and now benefits from far more sophisticated solutions. Technical writers and web content creators occupy vulnerable ground. Insurance underwriters are seeing algorithmic encroachment; computer vision can now assess car accident damage from uploaded photos. Junior-level roles involving spreadsheet or presentation creation face mounting pressure. Software development presents a nuanced case. “If you are a junior level software developer,” Gownder notes, “we know that Claude does a great job of creating basic code.” Yet senior developers with architectural judgement and system-level understanding remain essential. The pattern repeats across knowledge work: AI augments more than it replaces, transforming job descriptions rather than eliminating positions entirely. “It’s not that there aren’t jobs that will go away,” he clarifies, “but they are much more specific and limited, and they need to be architected with the right technology to replace that job. It’s not everybody goes away.” Blue-collar work presents its own dynamics. Physical robotics will play a role in certain sectors: warehouse sorting and picking have improved through computer vision, and construction has seen experiments with brick-laying and cement-pouring robots. But the humanoid robots capturing media attention are unlikely to achieve significant workplace deployment within the forecast period. The physical world, with its infinite variations and unexpected challenges, remains stubbornly resistant to automation. The White-Collar AI Job Impact Misconception White-collar workers now constitute roughly 60% of the workforce in both the US and Europe, a dramatic shift from previous generations. These “symbolic analysts,” as Charles Handy termed them, don’t produce physical goods, which has led some to assume their work is easily transferable to AI systems. Gownder pushes back against this notion. “Most white-collar work is, in fact, fairly productive because there is something on the other end that someone is willing to pay for.” Software engineers create applications that enable other work. Physicians produce healthcare outcomes. Analysts help organisations make better decisions. The practical challenges of AI deployment in white-collar settings corroborate these theoretical objections. Hallucinations remain a persistent problem, introducing error margins that knowledge workers must catch and correct. Employees often lack the skills and understanding to use AI tools effectively. Organisations overextend their expectations of what AI can accomplish. “When it fails, it’s dramatic,” Gownder observes. The Deloitte incidents in Australia and Canada, where AI-generated content with obvious hallucinations reached government clients, illustrate the reputational risks of premature automation. The Australian government report contained fabricated academic citations and even a made-up quote from a federal court judgement. Both governments required refunds. “You don’t want to produce AI work slop and present it as your work without editing, without perspective. That is a losing proposition.” — J.P. Gownder, Forrester A Harvard Business Review study reinforces these concerns. Researchers found that executives who used ChatGPT to make predictions became significantly more optimistic, confident, and produced worse forecasts than those who consulted with peers. The authoritative voice of AI produces a strong sense of assurance, unchecked by the social regulation and useful scepticism that human consultation provides. AI Job Impact on Marketers and Digital Professionals For students entering digital marketing and related fields, the picture is complex but not necessarily bleak. “Marketers are actually on the front lines of job transformation, not job replacement,” Gownder notes. The distinction matters. Transformation implies evolution of roles rather than elimination. “I work with a lot of marketers and they say, ‘I signed up to be a great marketer. I didn’t sign up to be an AI expert. Why am I learning all of these tools?’ But inevitably, they now can’t do their job without using some kind of AI tool.” The prescription for emerging professionals is clear: combine classical education with a genuine understanding of AI capabilities and limitations. Those who master both domains will find themselves in demand. Those who resist the technology or fail to understand its boundaries will struggle. The key lies in approaching AI as augmentation rather than replacement—using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced professionals bring cannot be easily replicated by algorithms. Freelancers and AI If AI augments rather than replaces traditional employees, the question arises: will freelancers and gig economy workers absorb the displacement? The white-collar economy is experiencing a broader transition towards more freelance and contract arrangements at all levels. “On some level,” Gownder observes, “this can give people a certain freedom, because they can work with lots of different clients and they can make their own hours. They can work wherever they want to.” The flexibility that defines gig work aligns well with the project-based nature of AI-augmented workflows. Yet the picture is not uniformly positive. In the United States, where people depend upon employment for health care, freelance arrangements can be precarious. The gig economy now encompasses over 64 million American workers, contributing nearly $1.27 trillion to the economy. AI is reshaping this landscape in contradictory ways: platforms use algorithms to match workers with clients more efficiently, but the same technology enables clients to handle tasks they previously outsourced. The freelancers most likely to thrive will be those who combine technical literacy with uniquely human skills—critical thinking, creativity, and client trust. I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well — J.P. Gownder, Forrester New niches are emerging even as others contract. Prompt engineering, AI ethics consulting, and AI training roles represent growth areas that didn’t exist before the current wave of generative AI. The bifurcation may prove to be one of AI’s most significant labour market effects: some workers gaining flexibility and autonomy, others losing stability and benefits. Navigating the AI Job Transformation For workers at either end of their careers, the AI transition presents distinct challenges. Early-career professionals face the paradox of entering a workforce that may value their digital nativity while threatening the entry-level positions that traditionally served as training grounds. Gownder’s advice is direct: combine classical education with a genuine understanding of AI capabilities and limitations. Older workers, often stereotyped as technologically resistant, have their own path forward. “One of the negatives that people associate with older workers is that they are incapable of embracing technology,” Gownder observes. “That is something one can work on.” The key lies in approaching AI as augmentation rather than replacement, using tools to enhance existing expertise while maintaining awareness of their limitations. The judgement, ethics, and institutional knowledge that experienced workers bring cannot be easily replicated by algorithms. For business leaders, the prescription is almost counterintuitive. “The irony of AI is that the way that you succeed today is by investing in your human employees.” The technology can augment productivity, but only when workers possess the skills, motivation, and ethical framework to deploy it effectively. The human element, far from being made obsolete, becomes more critical than ever. The Long View on AI and US Jobs The AI job impact in the US will unfold over years, not months. Forrester’s 6% forecast represents a significant transformation affecting millions of workers, but it is a measured shift, not a sudden collapse. The organisations that thrive will be those that resist the temptation to conflate AI announcements with AI capabilities, that invest in their workforce rather than assuming technology will render it obsolete, and that approach automation with the same rigour they would bring to any major capital investment. The irony of AI is that the way that you succeed today is by investing in your human employees. Invest in your people, counterintuitively — J.P. Gownder, Forrester Gownder’s work at Forrester provides a framework for this navigation: empirical rather than hysterical, specific rather than sweeping, attentive to both the genuine capabilities of AI and its persistent limitations. The job apocalypse makes for compelling headlines, but the evidence points to something more complex and ultimately more manageable. For those willing to adapt, invest in skills, and maintain perspective, the future of work remains a human story, augmented but not replaced by artificial intelligence. J.P. Gownder is Vice President and Principal Analyst on Forrester’s Future of Work team. A Harvard graduate, he covers the impacts that technology and human factors jointly have on the future of work, helping clients design strategies that drive productivity, collaboration, and effective hybrid work. His research covers how technologies like devices, collaboration software, extended reality, and artificial intelligence reshape the future of how and where we work. The post AI Job Impact in the US: the Apocalypse Can Wait appeared first on Marketing and Innovation.

AI is not a tool, or is it? Reports regarding the impact of AI on jobs, society and businesses are cropping up all over the place at the moment in all corners of the world. Some of these reports are announcing forthcoming revolutions both for societies and our economies whereas others are playing down the impact of artificial intelligence, and reviving the good old Solow aka Productivity paradox (“You can see the computer age everywhere but in the productivity statistics”. follow up here and here). As a consequence, it is very hard to make an opinion, let alone advise business people and students alike with regard to what needs to be done in the future. Visionary Marketing has embarked on a mission to try and shed light on this topic in as rational and informed a way as possible. AI is not a tool, or is it? Should AI platforms become tawpayers? The great love affair of French people for taxes will not spare Artificial Intelligence Cavazza surmises. Indeed, according to him, AI is not a tool! A lot of these predictions are guided by ideology. The authors, be they proponents or opponents of AI, have a personal agenda, often political or ideological, and are trying to make facts stick to this agenda. This is not very useful. But others are based on fact and careful analysis. I have decided to focus on two of these reports/predictions. The first one is Fred Cavazza’s analysis of the impact of AI on society and the economy (original post in French), which describes Artificial Intelligence as a source of profound disruption. I have known Fred for years, and I know his deep knowledge of both subjects, which makes his report particularly valuable. With his kind permission, I have translated his piece from French to shed light on this subject. The other report is by Forrester’s JP Gownder, whom I’ll be interviewing soon. I will test Fred’s assumptions on JP and see what he has to say about this idea of disruption by AI. Hopefully, our readers, and especially my students who have a lot of pending questions about this, will be able to separate the wheat from the chaff after these two interviews and podcasts. AI is not a tool, it’s reshaping our society and economy AI can’t be seen as just another technological innovation. By establishing itself as a major driver of productivity, automation and decision-making, it’s fundamentally disrupting the economic and social balance of our society. Whilst the productivity gains brought by AI are already transforming office jobs and creating a chasm between employees who’ve embraced it and those who haven’t, a fundamental question emerges: how do we integrate these synthetic entities into our collective organisations? Between appropriate taxation, legal personality and psychological resistance, there are numerous questions to debate before we can draft a new social contract. AI IS NOT A TOOL — TLDR AI is triggering a disruption of our civilisation, it’s not just another tech breakthrough. It marks our genuine entry into the fourth industrial revolution by offloading, for the first time, human thinking and creativity to machines. AI’s productivity gains are already real and deeply uneven. A growing divide is opening up between workers who can work alongside AI and those stuck with 20th-century methods. AI agents are challenging how white-collar workers create value. Intelligent agents are transforming knowledge work, undermining certain business models and setting the stage for a rapid reshaping of office jobs. Integrating AI requires a new legal and fiscal framework. Like corporate entities, AI agents must be given a status that clarifies their responsibilities and reintegrates their value into the social contract. The socio-economic impacts reach far beyond just employment. AI affects our psychology, culture and demographics, making public debate crucial to head off looming social tensions. AI on the Davos Agenda This week, the world’s leaders are gathered at the Davos Economic Forum, and ecology isn’t on the agenda: AI, Big Tech and Trump Shine Most Brightly at the Davos Show . At Davos, the AI is not a toll debate was all the rage. Cavazza thinks that artificial intelligence will be a major disruptor not just of our exonomies but our societies too. AI is dominating every conversation, with considerations that extend far beyond technology: AI Is Poised to Take Over Language, Law and Religion, Historian Yuval Noah Harari Warns Palantir CEO says AI to make large-scale immigration obsolete “Artificial intelligence will displace so many jobs that it will eliminate the need for mass immigration” I’m not going to wade into commenting on everyone’s pronouncements, with their more or less biased viewpoints, but what’s certain is that major upheavals are on the horizon: AI and the Next Economy Nearly 80% of people feel unprepared to find a job in 2026 The AI revolution is here. Will the economy survive the transition? AI specialists are naturally the star guests at this 2026 edition of the Davos forum, invited to give their testimony and views: Deepmind and Anthropic CEOs expect AI to hit entry-level jobs and internships in 2026. Looking at it this way, it seems absurd to sit back as spectators whilst the AI revolution unfolds and do nothing to limit the fallout from this productivity shock. But not all’s lost—at least not for everyone, as countries in the global south are already gearing up for it: The AI Revolution Needs Plumbers After All. Productivity gains to be nuanced, but certainly not ignored I’ve had plenty of chances to explain generative AI’s impact (Superintelligence will multiply our capacity to act tenfold and The digital divide is a problem no one can ignore). Whilst we’re largely in agreement about what widespread generative models mean, there’s serious disagreement over the timeline for AI’s arrival. The dominant narrative keeps insisting that general AI is a pipe dream and that human intelligence is and will remain superior to machines. What is intelligence? This is precisely where ambiguities crop up: firstly, intelligence comes in many forms (Theory of multiple intelligences and What’s your intelligence type?); secondly, not all office work requires emotional or social intelligence. What I’m getting at is that most service sector jobs boil down to shuffling information and data between systems. You don’t need to be a genius to do that—AI can handle it with ease. To properly grasp the speed at which latest-generation AIs will gradually transform office jobs, I recommend you peruse the latest edition of Claude’s publisher’s macroeconomic barometer: Anthropic Economic Index 2026. Anthropic’s economis index 2026 For this fourth edition, the study’s authors analysed thousands of people’s activities using increasingly precise indicators: New building blocks for understanding AI use. This study yields several findings that demonstrate a strong progression in the adoption and capabilities of generative models. Notably, they observe an average 30% growth in Claude usage, driven mainly by the API rather than the chatbot—a sign of rapid adoption by advanced users (e.g., IT professionals) and slower uptake by ordinary users (white-collar workers using the web version). AI is not (just) a tool. As a matter of fact it’s not a tool at all, it’s a meta tool, a tool you can use to make tools.. The haves and the have nots A gap is therefore widening between those who’ve adopted new habits (working in tandem with AI) and those still working as they did in the 20th century. This gap is starting to become problematic, because the latest version of Claude (Opus 4.5) has capabilities comparable to those of an adult who’s benefited from over 14 years of education—the equivalent of a Bachelor’s degree. AI is not a tool but Clause isn’t a PHD either… yet. The question therefore is: how much longer can an employer justify paying salaries or hiring young graduates when chunks of the work can be farmed out to an AI? Whilst average productivity gains remain modest (1.8% according to the latest figures), AI’s contribution to certain tasks is absolutely spectacular: an average of 14 minutes to write a long article, versus 3 hours without AI assistance; an average of 5 minutes to analyse a complex data table, versus 1 hour 45 minutes without AI assistance. AI is not a tool, there are alo APIs You might argue this data’s skewed because these spectacular scores come from employees who are whizzes at using AI (therefore logically hyper-performers), but that’s not the case—the study covers ordinary employees with a 67% success rate for outsourced tasks. What this boils down to is that for a third of tasks, AI slashes processing time by 10 to 20 times in two-thirds of cases. If we apply some basic maths, AI can potentially triple efficiency—or to put it another way, cut the average time needed to complete a task by two-thirds. Which type of profile do you reckon managers will favour? (hint: McKinsey challenges graduates to use AI chatbot in recruitment overhaul) Soon the arrival of agentic white-collar workers Let me be clear: the productivity gains mentioned above relate to advanced AI usage, not just running searches in ChatGPT or asking Copilot to knock up meeting minutes. We’re talking about using generative models to their full potential, particularly intelligent agents (see Agentic Web: the revolution that won’t wait for you). Intelligent agents We’ve been banging on about these famous intelligent agents for a while now, but their potential only recently became blindingly obvious to ordinary employees (non-IT types) with the release of Claude Cowork, a very concrete wake-up call to the power of agentic AI: Claude Is Taking the AI World by Storm, and Even Non-Nerds Are Blown Away. AI is not a tool and Cowork is not (quite) a chatbot This awakening is shared by financial markets too, which are bracing for revenue drops at traditional software publishers, whilst one of France’s biggest IT services firms is axing jobs and European banks are preparing to follow suit: Claude’s new AI agent pushes down software stocks Capgemini plans to cut up to 2,400 jobs in France AI forecast to put 200,000 European banking jobs at risk by 2030 Adoption levels a matter for debate This isn’t a topic to take lightly, even though adoption levels are debatable (as I explained earlier, it’s not binary) and gains vary wildly (Why AI Boosts Creativity for Some Employees but Not Others). What’s undeniable is that AI agents are forcing a major rethink of how white-collar workers create value, and more broadly for tertiary sector businesses that account for three-quarters of France’s GDP. Whether you like it or not, whether you acknowledge it or not, we’re living through a civilisational shift, because AI’s arrival is turbocharging the fourth industrial revolution and unleashing upheavals whose full scope we’ve yet to grasp. Fair enough, AI is a tricky concept to get your head round (We don’t need better AI, but a better understanding of AI). Yes, tools based on generative models require behavioural changes that’ll take ages to embed. Nevertheless, it’s crucial we prepare ourselves psychologically for the coming upheavals, because if we take even the slightest step back, we quickly realise they’re already underway. AI is not just a tool: a shift beyond technology Generative AI’s arrival and the march towards the first superintelligences aren’t just another turn of the technological wheel started by computers and smartphones. We’re witnessing a civilisational shift that marks our genuine entry into the fourth industrial revolution (Waves of change: Understanding the driving force of innovation cycles). We’re not simply facing a new technological cycle, but a fundamental reshaping of economic and social foundations: for the first time, we’re offloading not physical power, but our thinking and creativity. Whether AGI arrives tomorrow or in ten years, we’re already living alongside autonomous entities capable of making decisions: synthetic agents, whether digital (AI agents) or physical (robots). This situation throws up an unprecedented question: how do we integrate artificial entities that contribute massively to wealth creation whilst guzzling significant resources into our collective framework? History offers an imperfect but revealing precedent: how we’ve gradually integrated domesticated animals. AI i not a tool: from biological analogy to legal reality Humans get along perfectly well with domesticated animals because they’ve helped shape humanity’s development: Horses served to explore territories, wage war, plough the land, transport people and goods… Dogs were used for hunting, for guarding… Insofar as animals contribute daily to our society, they benefit from services and rights: Guide dogs for the blind attend school and have status (a function = a job); Police dogs play a vital role in the fight against drugs; they’re entitled to retirement (they’re placed in a home for their old age). AI is not a tool, neither are police dogs From the moment animals make a direct contribution, they’re integrated into our society through their breeder and/or owner, who have obligations (identity tags and records for farm animals). They can benefit from protections (insurance, vaccination to fight epidemics…) and rights (laws against animal cruelty). So what about AI  that contributes value just as much, if not more, to our society? Whilst it’s tempting to liken AI agents to a newly integrated species, much like domesticated animals, this analogy quickly hits ethical and legal buffers. Domesticated animals have rights because they’re sentient, conscious beings. AI, on the other hand, is an information processing system, software that has neither sentience nor consciousness. The true parallel must be drawn with corporate entities (companies). Because, like a company, an AI: contributes to wealth creation (task automation, content generation…); exploits infrastructure and consumes critical resources (energy, rare earths, cooling water…); has rights (intellectual property) and responsibilities (transparency, explainability…); acts autonomously. This is why the comparison is pertinent, as it enables us to evolve the legal and social framework. The social contract of the synthetic era: responsibility and taxation Integrating these intelligent agents into our society shouldn’t be done by granting anthropomorphic rights, which would be absurd for a computer system, but by giving them legal personality (like a company, association or local authority). The real question isn’t whether AI deserve rights, but what legal status would clarify chains of responsibility. The avenue of electronic personality, debated in the European Parliament as early as 2017, aims precisely at this objective: not to recognise dignity in machines, but to organise their integration into our jurisdiction to protect humans, ensure they benefit from it, and that this benefit is distributed fairly (avoiding an even greater concentration of wealth and power). As robots and AI agents replace human labour, they erode the base of social contributions that rests on salaries. But since they contribute to economic activity and generate costs for the community (energy consumption, electronic waste management…), there’s no reason why they shouldn’t be integrated into our tax system. This isn’t about taxing AI agents as individuals, but applying tax to the value they generate through their operation. In exchange for this contribution, the AI (or its publisher) doesn’t gain social rights (pension, healthcare), but gets a framework of civil responsibility (fiscal, legal, social). This would enable AI-caused damage to be covered without necessarily tracing responsibility back to the original developer, who’s often disconnected from what the model ends up doing. Socio-economic upheavals whose scope we don’t fully grasp Having said that, the question of AI’s place in 21st-century society mustn’t stop at economic considerations, as it extends far beyond. Domesticated animals and AI If we revisit the domesticated animal analogy, we observe today that dogs aren’t just pets; for some, they’re also considered assistance animals. The exact term is “emotional support animals”—those that give retirees or psychologically fragile people (with chronic depression) a reason to get up in the morning. The same goes for domestic robots, which are one of the pillars of Japan’s Society 5.0 programme—those that will care for the elderly with a physical presence (assisting them with daily tasks and limiting their loss of autonomy), as well as psychologically (conversing with them to exercise their memory) and emotionally (keeping them company). AI is not a tool it’s way more than that, Cavazza surmises For Westerners, this prospect is terrifying, but for the Japanese, it’s the only solution to their demographic deficit. Same in China, where parents work so hard they lack time to look after their child (vs “children”), and offer them AI-enhanced soft toys that tell them stories and answer their questions (satisfy their curiosity). Furry robots A trend that obviously came from Japan (Casio launches AI-powered furry robot pet that wants to replace your dog), but which can be experienced in the West (‘I love you too!’ My family’s creepy, unsettling week with an AI toy). You might think all this is science fiction, Black Mirror-style, yet these are techno-sociological territories that have been explored for many years (Sony’s Aibo was launched in 1999). Is philosophising about the merits of emotional support robots truly our priority? Apparently not, as there are more urgent matters. But it’s nonetheless an essential step, because let me remind you that AI adoption in Europe is rather low—not for functional or technological reasons, but purely emotional ones (strong resistance to change and major psychological barriers stemming from a misunderstanding of what AI actually is = barely 15% average enterprise adoption): EU Digital economy and society statistics. So ultimately: Yes, we need to have this conversation and debate properly so we can come to terms with the changes ahead, anticipate the upheavals that’ll severely test our social system, and start rethinking our social contract (From Web 4.0 to Society 5.0). Regulation as an integration factor Don’t panic, I’m not about to launch into a lengthy sermon on the merits of universal basic income (an economic non-starter), but I will necessarily need to talk about regulation. Indeed, living alongside synthetic agents (AI and robots) shouldn’t be thought of in terms of domestication, as with animals (to fit into our daily lives, dogs must be vaccinated and trained), but rather as regulating a synthetic workforce we can no longer afford to ignore. The issue isn’t whether robots or AI deserve a pension, but how the wealth they produce can sustain our social model whilst regulating resource consumption, which creates economic tensions (electricity prices) and geopolitical ones (China’s monopoly on rare earths). AI disrupting civilisation? That was Fred Cavazza’s account of this forthcoming civilisational revolution.  In my opinion, there’s a lot of truth in Fred’s vision about the future of AI and civilisation. Some of it sounds a bit like science fiction, but so much of the real world is mimicking SF (think of Altman’s obsession with Jonze’s Her) that he might well be right.  As Fred states, the impact of AI might extend way beyond the technological breakthroughs that we are witnessing. However, it’s still early stages in my mind. I can well imagine what Anthropic’s Cowork could do in the future, but I can’t see it happening now, even though I’ve been a heavy and advanced user of Claude for years. This will take time It will take time to seamlessly blend these technologies to execute proper workflows and not just tasks. Agentic software is well and truly promising, and we are even able to catch glimpses of it. However, the productivity advances enabled by these technologies are often uneven. Even for advanced users.  The other day, after a one-hour and a half mentoring meeting where I delivered strategic advice, I used my usual Claude project to build a second-to-none executive summary of my recommendation as I was frying some eggs for the wife. Yet, it took three major complex steps and software suites to achieve that properly.   But don’t be mistaken, we will get there someday. It’s just the timing that’s wrong; it’s not happening just yet. Innovation requires time and effort.  As Fred points out, there is also a lot of resistance to change as always in innovation, and it’s not just in Europe, even though adoption is lagging behind in a traditional way on our continent. The impact of AI, even on jobs, will certainly be big, but it might take years to appear in the statistics, to put it in the words of Robert Solow. That said, Forresters’ vision is more nuanced, and we will review that with JP Gownder very shortly. Time will tell whether the truth lies somewhere in the middle, as I have a hunch it does. It’s certainly less romantic or frightening (depending on your point of view), but 40 years of implementation of tech innovation has taught me to grow a stiff upper lip. The post AI is not a tool it’s reshaping our society and economy appeared first on Marketing and Innovation.

Private equity branding remains one of the most underestimated levers for value creation in the investment world. While PE firms excel at identifying promising companies and optimising their financial structures, branding is frequently treated as an afterthought, reduced to logos and colour palettes rather than strategic assets. Yet the evidence suggests otherwise: strategic brand investment can dramatically shift market perception and, ultimately, company valuation. Marc Rust, Creative Director and Brand Strategist at Consequently Creative, has spent years demonstrating that branding deserves a seat at the strategy table. His striking claim that he transformed an $80 million company to look like a $120 million company through branding alone captures the essence of what strategic messaging can achieve when properly deployed. How Private Equity Branding Is Transforming Company Valuation With Storytelling The term &#8220;branding&#8221; itself creates immediate problems in private equity settings. At networking events, Rust finds that mentioning branding triggers what he calls &#8220;cognitive disruption&#8221; Beyond Logos: Redefining What Branding Actually Means The term &#8220;branding&#8221; itself creates immediate problems in professional settings. At networking events, Rust finds that mentioning branding triggers what he calls &#8220;cognitive disruption&#8221; – people immediately think of visual identity work that seems irrelevant to serious investment activities. Many professionals lack any clear definition of what branding encompasses, while others dismiss it as superficial design work. This misconception misses the fundamental truth: branding and messaging represent a powerful force for business growth that should inform strategy from the outset, not be bolted on afterwards as a cosmetic exercise. The real definition of branding, Rust argues, is &#8220;what you stand for in the minds of the people that you&#8217;re trying to reach, convert, and move into action.&#8221; This is not something companies own outright; rather, it is something they can influence through deliberate effort and sustained investment. The critical distinction lies between what companies do and why it matters. Most organisations focus their communications on deliverables and capabilities. Yet answering the question of why it matters opens doors to deeper insight about audience pain points, goals, and outcomes. This shift acknowledges that messaging exists not for the company but for its buyers, requiring communication in their language rather than internal jargon. The Evolution of Private Equity Strategy The private equity landscape has fundamentally changed over the past decade. The old-school approach – acquiring a company, trimming the fat, making it lean and mean, then finding a suitable buyer – no longer resonates with contemporary markets or the talent those markets require. Successful PE firms have embraced a different philosophy: nurturing acquired companies, building genuine value over time, and then pursuing exit strategies that reflect accumulated worth. This evolution makes branding more important than ever because value creation depends on perception as much as operational reality. When thinking about branding in private Equity, most people immediately think of visual identity work. All that seems irrelevant to serious investment activities even though it&#8217;s blatantly wrong, Mac Rust believes. Visual made with Midjourney Effective branding requires understanding multiple audiences simultaneously. Internal alignment comes first – the people who build products and deliver services need clarity about what their company stands for, especially during periods of transition. Post-acquisition, this alignment frequently suffers as employees wonder about new leadership, potential job losses, and strategic direction. Consequently Creative addresses this turbulence by bringing teams together to celebrate what they stand for, building stories around acquisition rationale and forward-looking plans grounded in existing strengths rather than imposed transformations. Beyond internal audiences, companies must establish clear market positioning relative to competitors and ecosystem partners. Finally, there are the buyers who will drive revenue growth during the holding period and, ultimately, the acquiring company that represents the exit opportunity. Each audience requires thoughtful attention, and branding provides the framework for addressing all of them coherently while maintaining a consistent core narrative. The Valuation Premium of Strong Brands Buyers demonstrably pay premiums for assets with strong brand equity. Companies that look more upscale and feel right command higher prices regardless of sector. This premium extends across every touchpoint: market presence, customer service quality, sales process sophistication, product presentation, and how offerings are described and positioned. The key lies in making everything about the audience – answering why customers should care and how specific features apply to their particular situations. Buyers demonstrably pay premiums for assets with strong brand equity, Rust declares. Visual made with Midjourney Building a brand encompasses far more than marketing communications. Yet smaller companies actually hold advantages here that larger organisations lack. Without established brand perceptions moulded into market consciousness over decades, mid-market companies enjoy flexibility that industry giants cannot match. They can position themselves as something new even when their offerings are not particularly novel, or emphasise technology, audience needs, or other differentiating angles. The argument that mid-market companies lack resources for serious branding investment misses this opportunity – budget allocation to branding should be generous precisely because returns can be substantial and the competitive playing field favours agility over scale. AI as Tool, Not Solution The artificial intelligence revolution has created new temptations for companies seeking branding shortcuts. Tools now generate logos, mission statements, and complete brand architectures almost instantly. But Rust cautions strongly against treating AI as a solution rather than what it actually is: a technology that should come last in any strategic process. The POST method he advocates begins with understanding people (your audience), then defining objectives (business goals), followed by strategy (how to achieve those goals), and only then selecting technology. Flipping this sequence – jumping on AI because everyone else has it – represents precisely the wrong approach to brand development. The danger of AI-driven branding lies in acceptance without scrutiny. When tools generate content quickly, users become passive recipients rather than active directors, keeping their eyes closed and allowing technology into the driver&#8217;s seat. Rust draws on singer-songwriter Tom Waits: &#8220;The world is a hellish place and bad writing is destroying the quality of our suffering.&#8221; AI contributes to this problem when deployed thoughtlessly, generating content that lacks the provocative point of view necessary to differentiate companies in crowded markets. Bad content existed before AI, but artificial intelligence is intensifying the problem. The world is a hellish place and bad writing is destroying the quality of our sufferingTom Waits That said, AI offers genuine utility when approached correctly. Brainstorming, idea generation, concept testing, and data synthesis all benefit from AI assistance. The technology serves well as a sounding board for strategic thinking. The crucial distinction is maintaining human agency – staying in the driver&#8217;s seat rather than ceding control to automated systems that cannot understand business context or competitive dynamics. B2B Private Equity Branding: The Relationship Imperative The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge. Branding fundamentally concerns relationship-building, and relationships involve humans making decisions regardless of whether they represent individual consumers or institutional buyers. When someone purchases at a supermarket, they often choose the best-looking product rather than the one with objectively superior ingredients. B2B purchasing follows similar patterns – everyone wants to work with companies that appear capable, innovative, and aligned with their values. &#8220;The notion that B2B companies need branding less than consumer-facing businesses deserves serious challenge&#8221; B2B branding may require less ongoing investment than B2C equivalents because it depends less on constant social media presence and retargeting campaigns. However, the fundamental mechanics remain identical: building trust through consistent value delivery over time. Each interaction with a company should provide something useful, and these value contributions compound into trust. Value + value + value = trust – a formula that applies regardless of whether customers are individuals or organisations. The Research Imperative: Discovering Hidden Stories The biggest mistake private equity firms make when rebranding after acquisition is proceeding without empathy for audiences. This criticism is not meant to disparage PE professionals – it simply reflects that branding expertise lies outside their core competencies. The solution involves partnering with agencies that understand how empathy drives both growth and culture. Jumping straight to visual refresh without strategic groundwork means missing reasons to believe that proper research would uncover. Rust illustrates this with two compelling examples. Working with a company owning approximately 100 senior living properties across the United States, his team discovered that residents were not actually the primary marketing audience. Instead, the &#8220;adult daughter&#8221; – typically the family member who becomes caregiver for ageing parents – drives decision-making in most families. This insight transformed messaging, positioning, and the entire marketing approach, creating stronger differentiation than competitors who continued addressing residents directly. Similarly, research for Simmons College in Boston revealed that women chose the institution for its academics, with its all-female status being secondary rather than the primary draw. This finding enabled far richer storytelling around academic programmes, distinguished instructors, and career outcomes under a unifying theme of &#8220;leadership by design&#8221; rather than gender-focused messaging. The Cost of Neglect Perhaps most puzzling is the frequency with which acquiring companies simply neglect their purchases after transactions close. Businesses get acquired – sometimes at significant cost – and then allowed to wither rather than being nurtured toward growth potential. Rust compares business development to plant cultivation: seeds will grow with minimal attention, but structured support – like a stake helping a vine climb toward sunlight – produces stronger plants bearing larger fruits. The same principle applies to acquired companies. Neglecting brand transformation leads to predictable failures. Teams cannot understand strategy without clear articulation of what the company stands for. Customers fail to perceive value when it goes unexpressed. Culture fractures without internal alignment, and misalignment breeds resistance that undermines sales effectiveness. Eventually, the market loses sight of what the company represents, competitive positioning erodes, and the investment opportunity dissipates along with the premium that strategic branding could have created. Looking Ahead: Trends for 2026 and Beyond Three trends deserve attention from private equity professionals focused on brand-driven value creation. First, generational shifts have fundamentally altered workforce and customer expectations. Millennials and Gen Z want to work for organisations that care about their audiences and hold values they can identify with personally. The old PE playbook of acquire, strip, and flip no longer attracts the talent or customer loyalty necessary for sustainable growth. Second, AI requires proactive engagement rather than passive acceptance. Understanding these tools and deploying them strategically – while maintaining human judgment – will separate successful firms from those drowning in generic content. The winners will keep their eyes open, using AI for specific purposes rather than allowing it to drive business decisions. Third, relationship dynamics demand respect for courtship conventions. Business development remains fundamentally about human connection, yet many organisations rush toward closing before establishing trust. The equivalent of proposing marriage on a first date appears constantly in LinkedIn solicitations that skip value demonstration entirely. Understanding that relationships require multiple touches and consistent value delivery provides competitive advantage. As for AI investment opportunities, Rust maintains cautious optimism. He worked recently with a drone software company that acquired an AI firm to enhance video data analysis for defence applications, enabling faster tactical decisions while reducing the need for constant human monitoring. This represents AI used thoughtfully as a tool – precisely the model that deserves investment. However, the current boom inevitably attracts companies claiming value where none exists. Scrutiny remains essential. Creativity as Competitive Advantage Perhaps the most troubling observation Rust offers concerns how business leaders equate creativity with risk. This equation represents a fundamental misunderstanding: creativity is the single most powerful tool for achieving differentiation and growth. In markets awash with AI-generated sameness, human creativity and provocative perspective become more valuable than ever. The firms that thrive will be those with the audacity to be different – to push forward with distinctive points of view while competitors retreat to forgettable positioning. Consider hiring as an analogy. When reviewing candidates, interviewers are not determining whether applicants possess necessary qualifications – that was answered before the interview. Instead, they seek to understand whether candidates are different, whether they bring passion that will challenge existing thinking. The same applies to companies: differentiation commands attention and premium value, while sameness leads to commodity pricing. For founders considering private equity investment, the advice is straightforward: develop a clear story expressed in audience-appropriate language rather than internal jargon. Ensure alignment throughout the organisation. Engage sales teams as amplifiers of that narrative. Find passionate people within the company and give them voice – authentic enthusiasm proves more compelling than polished corporate communications. These steps position companies for maximum pre-acquisition valuation and set the stage for continued growth under new ownership. Private Equity Branding and Hunger for Growth <img decoding="async" width="1024" height="576" src="https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-1024x576.jpg" alt="Private Equity branding " class="wp-image-85190" srcset="https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-1024x576.jpg 1024w, https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-500x281.jpg 500w, https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-768x432.jpg 768w, https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-1536x864.jpg 1536w, https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding-390x220.jpg 390w, https://visionarymarketing.com/wp-content/uploads/2026/01/fueling-pe-branding.jpg 1920w" sizes="(max-width: 1024px) 100vw, 1024px" />Fueling Value Creation with Private Equity branding. Image generated wth Gemini from our text Private equity branding ultimately asks a simple question: how hungry are you for growth? The answer determines whether acquired companies flourish or fade, whether investments multiply or stagnate, and whether exit multiples reward strategic vision or punish brand neglect. In a world where perception increasingly drives reality, the firms that master strategic storytelling will capture disproportionate value – transforming $80 million companies into $120 million ones, and perhaps far beyond. The post Private Equity Branding Enhances Valuation Through Storytelling appeared first on Marketing and Innovation.

The ebook self-publishing landscape has undergone a remarkable transformation over the past decade. What was once viewed with scepticism by the publishing industry has become a legitimate and often preferred path for authors worldwide. To understand the current state of this evolving market, we spoke with Kris Austin, whose platform Draft2Digital serves over 300,000 authors publishing more than a million titles across global markets. From Oklahoma City, he shared his insights on how independent authors are reshaping the publishing world. Inside the Ebook Self-Publishing Industry With market shares amounting to 40% of sales in the US, ebooks present new opportunities for writers who are able to benefit from self-publishing platforms like Self2digital. Can you introduce Draft2Digital and its mission? Draft2Digital currently serves over 300,000 authors who are independently publishing more than a million titles. We have been operating since 2012, and the industry has changed considerably during that period. Our goal is to help authors achieve their dreams by removing technical barriers and making the publishing process as streamlined and straightforward as possible. What languages and markets do you cover? We have published books in over a hundred languages. While English remains predominant, approximately 15 to 20 percent of sales come from non-English titles, with Spanish and German ranking as the second and third most popular languages. Our distribution reaches 180 countries, and about 40 percent of all sales occur outside the United States. ebook self publishing industry entrepreneur Kris Austin talked to us from Oklahoma City, OK. How has self-publishing evolved since 2012? When we started in 2012, self-publishing was still in its early stages. The real catalyst came in 2007 when Amazon released the Kindle, which sparked the explosion of digital books. Back then, there was significant stigma attached to being an independent author; many felt they were not as credible as traditionally published writers. Today, that perception has completely shifted. Many authors now choose self-publishing as their first option. We also see numerous hybrid authors who move between traditional and independent publishing, depending on their goals. The focus has shifted to the quality of the book and reader demand rather than the publishing model itself. What types of books dominate the ebook market? The majority of our ebooks are genre fiction: romance, fantasy, mysteries, and thrillers. These narrative fiction categories account for approximately 80 percent of ebook sales. Our print-on-demand service shows a different pattern, with roughly 40 percent fiction and 60 percent non-fiction. All these books are intended for consumer readers purchasing for personal enjoyment. Genre fiction (romance, fantasy, mysteries, and thrillers) amounts to approximately 80 percent of ebook sales Wit ebook self-publishing, authors can find readers anywhere in the world without leaving their homes. Image created with Midjourney Is ebook self-publishing viable for image-heavy books like photography? It is possible, though more demanding. Image-heavy books typically require a professional formatter to achieve the desired layout, particularly in digital formats where presentation can be challenging. For print editions, colour printing and layout involve additional complexity compared to text-only publications. What determines success in ebook self-publishing? The most successful authors treat publishing as a business. After creating a book they are proud of, they focus on marketing, discoverability, sales, and distribution. They approach it with an entrepreneurial mindset. However, it can also work as a part-time endeavour, particularly for authors writing series with multiple titles. One advantage of independent publishing is that you do not need a massive readership to succeed. Indie authors typically retain 60 to 80 percent of their sales revenue, allowing them to price competitively and target niche markets effectively. Even with just 2,000 potential readers, if you capture that audience and build loyalty, you can build a sustainable career. Indie authors typically retain 60 to 80 percent of their sales revenue If writing is your dream, ebook self-publishing could make it real draft2digital claims. How does Draft2Digital help authors reach global audiences? First, availability is essential. Authors upload their manuscript in Word format to our website, along with a cover image. I recommend not spending more than 100 dollars on a cover when starting out. Our system converts everything to digital formats and distributes to thousands of stores, including major online retailers, smaller platforms, and libraries across the US, UK, and Australia, typically within a few days. Accurate metadata, including title, description, and category, is crucial for helping readers find your book. What marketing strategies work for unknown authors? Discoverability is always a challenge. Successful authors connect with readers through social media, choosing platforms based on their target audience. Facebook may suit an older demographic, while TikTok reaches younger readers. Authors must identify where their audience congregates and invest effort in building those connections. Nothing comes free when selling a product; it requires consistent work. What are the main differences in reading habits across countries? Reading preferences vary significantly by region. Some countries, like the US, have high ebook adoption, while others, such as Germany, still favour print by a considerable margin. Certain markets, like Canada, show preferences for book bundles. Interestingly, German readers consume many English-language books, so we sell substantial quantities of English print titles there. What is the current balance between ebooks and print? When ebooks began growing around 2007, there were widespread concerns about the death of print. That never materialised. Ebook growth peaked around 2013, but print remained dominant. Currently, approximately 60 percent of books sold are print and 40 percent are ebooks, though this varies by genre. Romance readers predominantly purchase ebooks due to lower cost and convenience, while non-fiction readers prefer print for its tactile qualities and ease of reference. This ratio has remained relatively stable for years. Approximately 60 percent of books sold are print and 40 percent are ebooks Are people reading less than before? Readership fluctuates in cycles. We saw a significant peak during the COVID lockdowns, and we have been coming down from that high. However, engagement appears to be recovering. Books now compete with digital streaming and social media for attention, but dedicated readers will always find their books. We are optimistic that younger generations will discover books that resonate with them and develop reading habits. How is artificial intelligence affecting the ebook market? AI-written books exist throughout the market. We support AI as a tool for outlining, brainstorming, and various other assistance, much like word processors and spell checkers became standard aids. What we do not support is fully AI-generated content. AI-written books have become a significant challenge This has become a significant challenge, with platforms like Amazon being flooded with such material. It harms the industry and makes it harder for readers to find quality books. While AI may eventually produce excellent literature, we are not there yet, and this remains an ongoing challenge for the market. How do you help authors stand out in a crowded marketplace? We maintain merchandising relationships with all major retailers. Our mission is to identify promising books and propose them for spotlight placement and promotional features. We submit thousands of titles annually and achieve a 60 percent success rate. Authors can apply through our website to participate in these programmes. We also invest heavily in author education through our Self Publishing Insiders podcast, where we interview industry leaders, successful indie authors, and service providers to help authors improve their marketing and sales strategies. What do you predict for the future of digital publishing? Independent authors have proven their agility and ability to respond quickly to reader demands in an industry historically slow to adapt. Traditional publishers are increasingly looking to indie authors for insights on how to operate differently. They are actively recruiting successful independent authors into the traditional world. I expect traditional publishing to adopt more characteristics of indie publishing: greater agility, flexibility, and responsiveness. This convergence will continue accelerating over the coming years. Final thoughts about ebook self-publishing The ebook self-publishing revolution has fundamentally altered the publishing landscape. What Kris Austin describes is not merely a shift in distribution channels but a democratisation of authorship itself. With platforms like Draft2Digital removing technical barriers and providing global reach, the determining factors for success have shifted from gatekeepers to readers. For aspiring authors, the message is clear: quality content, business acumen, and direct reader engagement now matter more than ever. The stigma of self-publishing has given way to recognition that, ultimately, a book&#8217;s value lies in its ability to find and satisfy its intended audience, regardless of how it reaches them. Learn more at Draft2Digital The post Inside the Ebook Self-Publishing Industry appeared first on Marketing and Innovation.

Whereas artificial intelligence is reinventing Web writing, the written word has never been more valuable. Selim Niederhoffer, a copywriting trainer and bestselling author, has recently been exploring how marketing professionals can still succeed amidst &#8220;enshitification&#8220;, online influence, and automation. Meet an expert who remains confident in the power of words. Copywriting in the age of AI: why words retain all their magic Niederhoffer is adamant: despite GenAI, the written word retains its magic —&nbsp;image produced with Midjourney Human vs. AI Selim, a seasoned copywriter and author is keen on using what he calls &#8220;magic words.&#8221; Ask him why and his answer might surprise you in this age of sheer automation. &#8220;I&#8217;ve already been thinking about magic words for years. I want to dig deeper, show real examples and, most importantly, explain why they actually work.&#8221; Pen and paper His approach remains resolutely traditional. &#8220;I genuinely work with a pen and paper.&#8221; This approach reveals something fundamental about Web writing: it works when you truly understand the psychology behind it, not when you just mechanically apply copywriting techniques. Selim bases his work on well-established principles of persuasion. Selim Niederhoffer still believes in the virtues of word magic in Web writing — image produced with Midjourney &#8220;When there&#8217;s a principle of persuasion, there&#8217;s usually a word that goes with it. Take urgency or scarcity, for example. If something&#8217;s rare, that means limited places, running out of stock&#8230; that sort of thing. That&#8217;s how I build my word cluster. The research then extends to field observation. &#8220;I look at what my clients are using, what&#8217;s going on at Burger King, McDonald&#8217;s, Nike. I check out the major brands too &#8211; what they&#8217;re doing on YouTube, on LinkedIn.&#8221; Eventually, Selim identified 55 magic words but trimmed them down to 50. It&#8217;s an approach that perfectly shows what still sets humans apart from machines: the ability to critically analyse and curate with discrimination. However, it&#8217;s worth adding nuance. Selim can&#8217;t conceal that he genuinely &#8220;loves&#8221; ChatGPT. As we&#8217;ll see later, this raises some legitimate questions. Thank you! the ultimate magic word Among the 50 words he&#8217;s analysed, the first is also the simplest: thank you! For Selim, this should be essential for every business. &#8220;How many times have you walked out of a shop where the sales assistant just didn&#8217;t seem to have noticed you? Whether you bought anything, or didn&#8217;t doesn&#8217;t make any difference, once the transaction&#8217;s done, you&#8217;re out the door.&#8221; Yet some brands know how to thank their customers. &#8220;Nespresso or Apple, for example: the Nespresso employee comes out from behind her counter, she hands you your product. Thank you for your visit. Have a nice day! That&#8217;s how it should be.&#8221; For Selim, saying &#8220;thank you!&#8221; is more than just being polite, it&#8217;s a way of life. &#8220;You can go further: thank you for your visit, thank you for subscribing to the newsletter, thank you for your comment. You need to constantly think in terms of gratitude.&#8221; This approach fits into what Gary Vaynerchuk called The Thank You Economy. &#8220;We&#8217;re in an attention economy,&#8221; Selim explains. The stakes are high in Web writing: how do you maintain this human dimension at corporate scale? &#8220;For me, the essence of business is that there&#8217;s a person in front of you who&#8217;s exchanging something with you. That&#8217;s really the foundation. But today, how do you keep that at corporate scale?&#8221; Data confirms an intuition: gratitude improves customer experience and encourages loyalty. A valuable lesson for all those who practice Web writing and seek to create a lasting connection with their audiences. You need to seek to create a connection with your audiences — image produced with Midjourney AI and Web writing: threat or opportunity? The conversation inevitably turns to artificial intelligence. For Selim, AI is first and foremost an incredible productivity tool. &#8220;If I&#8217;ve got a newsletter to write, I&#8217;ll use AI. Sales pages? AI. The thing is, AI works brilliantly for me. I&#8217;ve even become clearer in my writing,&#8221; he admits without hesitation. But he&#8217;s not entirely starry-eyed about it. He&#8217;s identified three major pitfalls that professionals absolutely must avoid. The three pitfalls of AI in copywriting The first pitfall is wordiness. &#8220;ChatGPT tends to waffle because it’s been trained on Reddit, Wikipedia, and similar sources. However, in real life, when we write, we cut down to the core, deleting most of what we initially write.&#8221; The second limitation is related to syntax. &#8220;AI tools have their favourite phrases. It can be a bit clunky. It&#8217;s got that &#8216;ChatGPT-ish&#8217; quality &#8211; you know it when you see it. After a while, you can spot AI-written text a mile off.&#8221; The third issue, and the most surreptitious one, is lack of personality. &#8220;When we just use basic LLMs, we lose our tone of voice, we lose what makes us different, we lose what makes people go &#8216;Ah! That&#8217;s Selim, that&#8217;s Yann&#8217;. That personal touch. I&#8217;d say that&#8217;s the biggest danger AI poses for copywriters, Web writers, anyone working in this field.&#8221; For him, the key questions are: how do you refine AI, how do you avoid its main pitfalls, how do you stay in control and how do you harness all its power? This evolution shows a profound shift in the profession: the copywriter is becoming an orchestrator of AI platforms. Losing our skills Selim warns against a hidden danger: declining skills. He reminds us that the brain is a muscle, and using it creates connections. However, if we stop using it, those connections are lost. He admits that he has fallen victim to this himself. &#8220;I have noticed that my writing isn&#8217;t as fluid as it used to be when I&#8217;m starting from scratch. Between 2010 and 2022, I was churning out three to five blog posts a week. Now, if I can just knock up a prompt, get a result and tweak it a bit, job done. But it&#8217;s less satisfying.&#8221; This awareness led him to experiment. &#8220;I run A/B tests. Send out version A written by AI, version B written by me, then see who clicks more. I check which headlines work best, which text performs better,&#8221; he explains. It&#8217;s a data-driven approach that could bruise his ego, he admits with a laugh, but it&#8217;s essential for understanding what genuine human value looks like in Web writing. What human added value tomorrow? The final question comes naturally: will humans still add value compared to machines in a year or two? Selim remains cautious about making predictions. &#8220;I&#8217;m rubbish at forecasting because I don&#8217;t spend enough time on it,&#8221; he admits candidly. But his reading of the market reassures him. While I can see companies increasingly launching AI training for their staff, there’s always the question of depth. Are they adequately trained and properly supported? Web writing needs remain massive &#8220;I meet marketing directors who want me on their pay-roll. We&#8217;ve got so much work to get through that we&#8217;d need ten people using ChatGPT just to keep up,&#8221; he reports. This suggests that AI isn&#8217;t so much replacing writers as it is multiplying opportunities for content production. Selim&#8217;s thoughts tap into bigger questions about how work is evolving. &#8220;The idea of heading towards a society where people become useless doesn&#8217;t surprise me. I&#8217;ve seen it enough times in sci-fi,&#8221; he confides. This vision actually mirrors history: villages like ours in the Pyrenees, with thousands of inhabitants at the turn of the 20th century now down to a few dozens. Economic and technological shifts have always redrawn the employment map. What the future holds for Web writing This conversation with Selim Niederhoffer sketches out the shape of Web writing as it transforms. Words remain magical, but how we summon that magic is changing. AI&#8217;s becoming a powerful content production tool, but the real value still lies in properly understanding how persuasion works, in being able to orchestrate these tools cleverly, and in keeping a human tone of voice. Here&#8217;s the paradox: just when machines can churn out infinite text, what becomes scarce is quality strategic thinking, subtle psychological understanding, and the ability to inject real personality into content. Selim&#8217;s magic words are just a a beginning &#8211; they are mere ways of understanding what makes human readers tick. A hybrid Web According to Selim, tomorrow&#8217;s Web writing will be hybrid &#8211; a collaboration between human and machine. It will demand technical mastery of AI tools, but it&#8217;ll be rooted in a deep understanding of how persuasion actually works. In short, words will always be magical, but that magic needs cultivating, training, constant work &#8211; otherwise it will erode in the face of automation&#8217;s easy appeal. One needs to keep reading, discussing and engaging with real people. That&#8217;s how we will train our brains and hang onto that human edge that makes all the difference in Web writing. 10 of Selim&#8217;s 50 magic words Here are 10 words taken from Selim Niederhoffer&#8217;s book &#8220;Les Mots Magiques&#8221; (Magic Words), which are effective words to boost your marketing content and encourage action: Please Thank you New Since Out of stock Free Now or never Satisfaction guaranteed or your money back Hundreds of people already trust us First name The post Web writing : words retain all their magic appeared first on Marketing and Innovation.

Commentary on the environmental impact of AI often swings wildly between doom-and-gloom catastrophism and blind techno-optimism. But where&#8217;s the truth in all this? On July 24, 2025—the symbolic date of Earth Overshoot Day—we sat down with Yves Grandmontagne, founder and editor-in-chief of DCMAG (Data Centre Magazine*), to get his take on AI and its real environmental impact. It is worthy of note that Yves and I explored Silicon Valley&#8217;s infrastructure innovators together through extensive press tours some time ago. This provided us with firsthand insight into the tech industry&#8217;s approach to these challenges. The current annotated transcript of our interview is a summary of our thorough, nuanced, and let&#8217;s admit it, quite lengthy discussion. You are therefore encouraged to treat this article as your starting point for diving deeper into this extremely complex topic. Exploring the Real Environmental Impact of AI What&#8217;s the real environmental impact of AI? An employee keeps watch over the cooling units at Orange&#8217;s data centre in Val de Rueil in Normandy, France — Photo antimuseum.com * DCMag is only available in French This post summarises what turned out to be an incredibly rich hour-long conversation. The sheer complexity of this topic forced us to dig into multiple technical, economic, and environmental angles—making any kind of comprehensive analysis near inconceivable. Drawing on his deep expertise in the data centre and AI sectors, Yves Grandmontagne gives us some much-needed factual perspective on a debate that&#8217;s often polarised between doomsday scenarios and over-the-top techno-optimism. To tackle this properly, we decided to take recent quotes—both positive and negative—and fact-check them with our expert. Yves&#8217;s analysis helps us cut through the noise and understand what&#8217;s really at stake in this technological breakthrough. Environmental Impact of AI: Reality Check Time TLDR: Environmental Impact of AI The electricity consumption issue is more nuanced than you think* &#8211; AI will represent 20-30% of data centre consumption (not twice that number), and only 2-4% of overall electricity consumption Energy efficiency gains are actually remarkable &#8211; Over the last decade: number of data centres x2, floor space x4, but energy consumption up only 6% Beware of dubious comparisons &#8211; Comparing a ChatGPT query to Google search is methodologically flawed (completely different technologies and services) Water consumption varies massively by geography &#8211; Huge issue in the US, but Europe has been using smarter closed-loop systems for ages Tech innovations look promising &#8211; New technologies (direct liquid cooling, immersion cooling) are slashing water and energy consumption AI might actually be part of the solution &#8211; Can optimise energy mix management and electricity transport, which is currently our main bottleneck Let&#8217;s get some perspective here &#8211; Data centre impact remains pretty marginal compared to the chemical industry (32% of French energy consumption) or agriculture *All numbers by Yves Grandmontagne at Data Centre Magazine Bottom line: The impact is &#8220;real but massively overstated&#8221;—we need to put things in context and remain cool and collected. So here are the quotes about the environmental impact of AI that we wanted to fact-check with Yves. Rumours vs Reality: Decoding the Doomsday Predictions Predictions of Booming Electricity Consumption The first claim I put to Yves Grandmontagne was Synth Media&#8217;s prediction [Fr] that &#8220;AI&#8217;s growth could double data centre electricity consumption by 2026.&#8221; His response immediately throws cold water on this alarmist take: &#8220;It&#8217;s absolutely true that AI is rolling out infrastructure at breakneck speed and eating up more space in data centres. Sure, it&#8217;s going to significantly bump up their energy consumption—no question about that. But will consumption actually double? I seriously doubt it. AI should account for somewhere between 20 and 30% of global data centre consumption worldwide.&#8221; A data centre server rack — photo antimuseum.com AI should account for somewhere between 20 and 30% of global data centre consumption worldwide This reality check reveals something consistent throughout Yves Grandmontagne&#8217;s analysis: the absolute need to put numbers in context. The expert points out that this increase is just part of the natural progression tied to our ever-growing digital habits. &#8220;What&#8217;s driving this consumption increase is our daily usage, whether that&#8217;s for work or for personal reasons,&#8221; he reminds us, highlighting our collective responsibility in this evolution. It&#8217;s a topic we&#8217;ve tackled before with a broader focus on digital consumption. The most striking part of his analysis is about the energy efficiency angle. Contrary to popular belief, data centres aren&#8217;t following a consumption curve that mirrors data growth. This improving efficiency is something that gets completely overlooked in public debates about the environmental impact of AI. ChatGPT&#8217;s Carbon Footprint: More Context Needed When it comes to the 10,113 tonnes of CO2 equivalent attributed to ChatGPT usage in January 2023 (Basta Media &#8211; Data for Good, AI has the potential to destroy the planet), Yves Grandmontagne takes a refreshingly pragmatic approach: &#8220;I can&#8217;t verify that exact figure. Getting that precise—down to 10,113 tonnes—represents a massive methodological challenge, especially when you&#8217;re dealing with AI infrastructures that are distributed systems.&#8221; We asked Yves Grandmontagne, editor-in-chief of DCMAG (Data Centre Magazine) to give us the real story about the environmental impact of AI — He gave us facts and figures, which we&#8217;ve compiled in the infographic at the end of this post This observation raises a crucial methodological point: just how tough it is to accurately measure the carbon footprint of distributed infrastructures. The expert does acknowledge this pollution is real, but puts it in perspective: &#8220;Those 10,113 tonnes of CO2 still represent volumes significantly smaller than what many other industries pump out.&#8221; This contextualisation isn&#8217;t about downplaying the issue—it&#8217;s about keeping things proportional. Yves Grandmontagne reminds us of a basic truth that is often overlooked: &#8220;The moment we use our smartphones, we become CO2 producers.&#8221; This highlights the inconsistency in criticisms that single out AI from our overall digital consumption. The Google vs ChatGPT Comparison: A Methodological Trap Watch out for convenient shortcuts between pollution and digital tech—they&#8217;re everywhere and pretty handy when you want to hide overall industrial pollution — image created with Midjourney MIT&#8217;s claim that &#8220;a ChatGPT query uses ten times more electricity than a Google search [p 9]&#8221; perfectly illustrates the danger of oversimplified comparisons, according to Yves Grandmontagne. His take is particularly eye-opening: &#8220;This comparison just doesn&#8217;t work. I think we&#8217;re making a fundamental methodological error here because we&#8217;re comparing two completely different technologies. Google is a search engine that gives you results that you then have to sift through to find what you&#8217;re actually looking for. ChatGPT, on the other hand, serves up information that&#8217;s already structured and ready to use.&#8221; &#8220;The more we use computing, and the more we use AI, the more we consume.&#8221; User responsibility matters — images antimuseum.com Orange data centre in Val de Rueil This analysis shows just how sophisticated you need to be when evaluating the environmental impact of AI. A ChatGPT query might actually replace multiple Google searches plus visits to various websites. That makes direct comparison pretty meaningless. This distinction will probably become moot anyway with the rollout of Google AI Overviews, which will integrate similar functionality to ChatGPT. The Water Issue: Geography Matters More Than You Think New data centre cooling techniques work in closed loops. Image antimuseum.com Orange data centre in Val de Rueil Massive Geographical Differences One of the most revealing parts of our interview dealt with data centre water consumption. Yves Grandmontagne draws a crucial distinction between American and European practices: In the United States, when you install a data centre, you&#8217;re a private company reaching out to other private companies for water on one side, electricity on the other. And the utility companies that manage energy or water are thrilled to have a massive client that&#8217;ll absorb a chunk of their production. This geopolitical insight explains those 5.4 million litres of fresh water attributed to ChatGPT-3 training. The issue isn&#8217;t the technology itself—it&#8217;s local practices and regulations. In Europe, our expert reminds us, &#8220;we&#8217;ve been developing infrastructure cooling systems that work in closed circuits or are air-based rather than water-based for quite some time&#8221;. Take Orange&#8217;s data centre in Val de Rueil, for instance—it&#8217;s cooled by the crisp Normandy air. The only exceptions are during heat waves, which are naturally time-limited. How Cooling Actually Works Yves Grandmontagne&#8217;s technical explanation demystifies the cooling process: &#8220;Water acts as a conductor to capture heat.&#8221; He then breaks down the dual-circuit system that protects infrastructure while managing thermal exchanges. This approach reveals that warm water discharge from data centres (~20-25°C) stays well below that of nuclear plants (27-35°C for the Gravelines nuclear plant in the North of France). That gives us a useful comparison point for our debate. Warm water discharge from data centres (around 20-25 degrees) stays well below that of nuclear plants (27-35°C for Gravelines) We should note that these figures vary depending on location, time of year, and technological choices. A 12°C increase in the North Sea, even if reports show nothing concerning at the macro level, probably isn&#8217;t neutral at the micro level over the long term. Similarly, warm water discharges from data centres probably aren&#8217;t completely neutral either. Another point worth nuancing, though we should be cautious here. The expert also highlights emerging technologies like Direct Liquid Cooling (DLC) and immersion, which operate in closed circuits and drastically cut water consumption. This technological evolution shows the sector&#8217;s ability to adapt to environmental challenges. More importantly, the above claim about water consumption was only related to the training of ChatGPT. This operation occurs only once, unlike the everyday usage by individuals worldwide, multiplied by hundreds of millions of users. This leads to staggering amounts of consumption. Once again, this highlights our individual responsibilities regarding energy and water consumption in data centres and AI. Moratoriums and Regulations: When Saturation Forces Limits A power generator at the Orange Data Centre in Normandy (Val de Rueil) — image antimuseum.com The Amsterdam, Frankfurt, and Dublin Cases Looking at data centre moratoriums reveals some pretty contrasting situations. Yves Grandmontagne particularly highlights the Irish case. &#8220;Remember, Ireland was Europe&#8217;s poorest country 30-40 years ago [Editor&#8217;s note: the podcast figure is off]. To build an industry and attract capital, they found this solution.&#8221; And it worked—Ireland&#8217;s GDP per capita now exceeds $100,000, putting it amongst the world&#8217;s best in 2025. [Editor&#8217;s note: there&#8217;s an infographic with sources at the end of this article] Today, 30% of Irish energy production goes to data centres. Yves&#8217;s reaction? &#8220;That&#8217;s clearly way too much!&#8221; This extreme situation shows the risks of an unregulated approach, but also how governments can respond intelligently. &#8220;The Irish government didn&#8217;t say &#8216;no more data centres&#8217;—they decided to make new construction conditional on implementing solutions.&#8221; The pragmatic approach adopted by the Celtic Tiger contrasts sharply with outright bans. It offers a middle ground between economic development and environmental constraints. The Real Problem: Moving Energy Around One of the biggest revelations from our interview concerns identifying the real bottleneck. &#8220;The real problem isn&#8217;t production—it&#8217;s the grid. The energy grid, meaning getting energy to where it&#8217;s actually consumed.&#8221; This analysis puts debates about the environmental impact of AI in a whole new light. France, Yves Grandmontagne reminds us, &#8220;is completely energy self-sufficient in production.&#8221; But it faces transport infrastructure challenges. This technical perspective shows that solutions don&#8217;t necessarily lie in cutting consumption, but in optimising distribution. Power generators kick in when there are supply issues — Val de Rueil data centre image antimuseum.com Putting Numbers in Context: AI in the Global Energy Picture That Telling 2% On global statistics, Yves Grandmontagne sets the record straight: &#8220;Currently, we&#8217;re more like 2%. And we&#8217;re heading pretty fast towards 4%.&#8221; These figures, relative to global electricity consumption, give us some useful perspective. The expert insists on keeping these percentages in context: &#8220;When you pull out a graph showing energy consumption across all industries, if you put industry and data centres side by side, you realise that the latter don&#8217;t weigh much.&#8221; The Real Energy Consumers To provide further context, Yves Grandmontagne lists the truly energy-intensive sectors: &#8220;There&#8217;s transport, industries, steelworks, agriculture&#8230; The latter consumes enormous amounts of energy. We often forget to mention this.&#8221; This perspective reveals according to our research that the chemical industry represents 32% of French industrial energy consumption, offering a striking comparison point with the 2-4% of data centres globally. Techno-solutionism: Promises and Realities Let&#8217;s now turn to techno-solutionists and examine their claims clinically as well. Sam Altman&#8217;s Declarations: Between Ambition and Pragmatism Sam Altman&#8217;s declarations that &#8220;1% of global electricity to train powerful AI would be a massive victory&#8221; also find nuanced resonance with Yves Grandmontagne: &#8220;It&#8217;s true that powerful AI called ChatGPT will consume perhaps 70% of global AI consumption worldwide.&#8221; This analysis reveals the economic reality behind the declarations: Microsoft invested 10 billion in OpenAI, creating a temporary quasi-monopoly situation. The expert underlines the mechanical nature of this consumption: &#8220;You need computing. Computing consumes energy. And since we use more and more of it, it consumes more and more energy. That&#8217;s normal.&#8221; AI as Solution to Energy Problems Satya Nadella&#8217;s (Microsoft) vision that &#8220;AI can be a powerful accelerator for addressing the climate crisis&#8221; finds technical justification with Yves Grandmontagne: &#8220;The real problem isn&#8217;t production, it&#8217;s energy transport.&#8221; The expert explains that managing a complex energy mix (nuclear, renewables, fossil fuels) requires sophisticated piloting tools: &#8220;We can only do this efficiently, we know today, by using AI tools.&#8221; This technical perspective reveals that AI isn&#8217;t just an energy consumer, but potentially an optimiser of the global energy system. However, Yves Grandmontagne tempers ambitions: &#8220;It will be a combination. It&#8217;s not one element alone that can help.&#8221; It also seems highly doubtful to us to claim that AI, however much a &#8220;pharmakon&#8221; it may be, can be a poison that serves as a universal remedy to this problem that far exceeds it, as we&#8217;ve written previously. Technological Innovations: Towards More Efficient Data Centres Microsoft&#8217;s &#8220;Zero Water&#8221; Plan: Revolution or Evolution? Microsoft&#8217;s announcement regarding its &#8220;zero water&#8221; cooling plan illustrates the sector&#8217;s technological evolution. Yves Grandmontagne confirms: &#8220;This isn&#8217;t greenwashing. It&#8217;s not really an initiative either—this stuff already exists.&#8221; The expert details the new technologies needed to cope with the dramatic increase in power requirements: When we were using conventional technologies until now, we had between 5 and 10 kilowatts coming into a rack. When you add AI, at minimum, you&#8217;re consuming 80 kilowatts or you&#8217;re consuming 150 kilowatts.This progression forces the adoption of Direct Liquid Cooling or immersion, technologies that operate in closed circuits. &#8220;That&#8217;s the direction of technological evolution for cooling AI data centres,&#8221; he concludes. Energy Efficiency: Remarkable Progress A particularly revealing statistic emerges from the interview: Over the last decade, the number of data centres has doubled, their floor space has quadrupled, and their energy consumption has only increased by 6%. This data illustrates considerable progress in energy efficiency, often overlooked in debates about the environmental impact of AI. It reveals the sector&#8217;s capacity to reconcile growth with energy optimisation. AI Geopolitics: American vs European Stakes The Geographic Concentration of Infrastructure An often-overlooked aspect of the debate lies in AI geography. Yves Grandmontagne reminds us: &#8220;For now, all AI production happens in the United States, not Europe.&#8221; This geographic concentration puts European criticism in perspective whilst highlighting our technological dependence. The expert mentions the €109 billion in AI investments announced by President Macron, of which &#8220;a third, or even half will concern data centres.&#8221; This strategy aims to reduce our dependence whilst developing a more efficient European sector. American Challenges: Infrastructure and Regulation Yves Grandmontagne&#8217;s analysis reveals the weaknesses of the American model: &#8220;In the United States, they have major weaknesses&#8221; regarding energy transport. This infrastructure failure partly explains the criticised overconsumption and justifies the different approaches adopted in Europe. Meta&#8217;s project in Texas (5 gigawatts across an area equivalent to Manhattan) illustrates this problem: &#8220;Meta doesn&#8217;t have the energy, so it&#8217;s looking for it.&#8221; This situation contrasts with Europe&#8217;s more integrated and regulated approach. AI: A Revolution Comparable to Fire? (Pichai) Sundar Pichai&#8217;s declaration (Google) that &#8220;AI is more important than fire or electricity&#8221; finds nuanced resonance with Yves Grandmontagne: &#8220;It&#8217;s a real revolution that will go much further than previous ones, because those were more industrial. This one is a revolution with an impact on our daily lives.&#8221; This analysis highlights the specificity of the AI revolution: its speed and penetration into daily life. &#8220;In barely two years, it&#8217;s been a real wave that&#8217;s engulfed us,&#8221; observes the expert, contrasting with the hundreds of thousands of years it took to adopt fire. Employment and Usage Challenges Beyond environmental questions, Yves Grandmontagne expresses his social concerns: I&#8217;m amongst those who still worry that it will massively impact jobs, and I think we need to be realistic about this. This social dimension of the environment reveals the complexity of trade-offs to come. The expert nevertheless encourages adoption: &#8220;We mustn&#8217;t cut ourselves off from this potential either, because extraordinary things will come to fruition soon.&#8221; It&#8217;s not easy to form an opinion on this employment issue given how contradictory the viewpoints are. Recommendations and Critical Vigilance Yves Grandmontagne&#8217;s final message to students (since this post was created in preparation for our course on AI and content creation) particularly resonates: Be careful about the discourse you listen to, who&#8217;s behind it, etc. It&#8217;s easy to twist informationThis warning against disinformation highlights the political instrumentalisation of the environmental debate. The Digital Economy: A Forgotten Dimension An often-overlooked perspective emerges: &#8220;When a data centre sets up in a city, in its wake, an entire digital economy develops. But we forget to mention that.&#8221; The expert quantifies: &#8220;One job created in a data centre means 100 jobs behind it that will be developed in the region.&#8221; This economic dimension reveals the complexity of territorial trade-offs and the need for global rather than sectoral approaches. In Conclusion: Keep Calm and Take a Step Back Yves Grandmontagne&#8217;s expertise reveals a landscape far more nuanced than polarised environmental debates suggest. His main conclusions deserve to be retained. The environmental impact of AI is &#8220;real, but largely overestimated,&#8221; in his words. This reality can be explained by several factors: the remarkable progress in energy efficiency, the relativity of consumption compared to other industrial sectors, and the constant technological evolution towards greater efficiency. Geography matters enormously in this equation. American practices, often criticised, don&#8217;t reflect the more regulated and efficient European approaches. This geographical distinction invites us to contextualise criticism and encourage good practices. The technological future is heading towards more efficient solutions: closed-circuit cooling, proximity AI, energy optimisation by AI itself. These developments suggest that current problems are transitional and technical rather than structural. Finally, the systemic dimension revealed by this analysis invites us to move beyond sectoral approaches. AI isn&#8217;t just an energy consumer, but potentially an optimiser of the global energy system. This systemic perspective may be the key to truly sustainable artificial intelligence development. Yves Grandmontagne&#8217;s message resonates as a call for nuance and critical vigilance: &#8220;Let&#8217;s remain cautious and serene.&#8221; In an often emotional debate, this technical wisdom offers precious guidance for navigating between the pitfalls of denial and catastrophism. This more nuanced discourse is undoubtedly less marketable than extreme opinions, which fuel coffee shop chatter and social media. This interview ultimately shows that the question of AI&#8217;s environmental impact doesn&#8217;t have a simple answer, but requires a systemic approach that&#8217;s geographically situated and technically informed. It makes up an essential starting point for all those who wish to move beyond preconceived ideas and contribute to a genuinely constructive debate about the future of our digital society. Infographic on AI&#8217;s Environmental Impact The post The Truth About the Environmental Impact of AI appeared first on Marketing and Innovation.

Will the AI bubble burst or is GenAI here to stay? The artificial intelligence industry is experiencing unprecedented financial euphoria. Yet, the current situation is very confusing. AI investments are reaching dizzying heights. Let’s mention OpenAI&#8217;s $40 billion funding round at $300 billion valuation and Mistral AI&#8217;s €1.7 billion funding round. Yet, some commentators are very critical of the situation. For instance, Ed Zitron predicts that the AI bubble will burst in Q4 2025. All this is fueling intense, rather than rational, debate. I wanted to confront these concerns with the expertise of Bernhard Schaffrik, Principal Analyst at Forrester Research. His analysis is insightful and nuanced. In his mind, there will be some sort of correction, but at the same time, GenAI is too popular to disappear. When Will the AI Bubble Burst? Is the AI bubble about to burst or is GenAI here to stay? Forrester&#8217;s Schaffrik predicts corrections but says GenAI is too popular to go — photo by Forrester.com Forrester’s Bernhard Schaffrik is recognized as one of the most insightful experts in artificial intelligence. He provides a nuanced analysis that transcends simple financial considerations. His perspective on the AI bubble burst scenario offers first-hand insights for understanding where this transformative technology is truly heading. The AI Bubble: Financial Reality, Technological Continuity The question of a potential AI bubble burst cannot receive a univocal answer. As Bernhard Schaffrik rightfully points out, it all depends on one’s perspective. This duality of vision probably constitutes one of the keys to understanding the current situation and the likelihood of an AI bubble burst. Like us, Schaffrik righfully points out that the main issue with AI is societal and philosophical — image generated with Adobe Firefly &#8220;It&#8217;s almost impossible to get a one-sentence response from an analyst. Allow me two sentences. Number one is, of course, it always depends on the role or the profile you&#8217;re asking. If we are talking about financial investors, then yes, there are strong signals of this being a bubble because there is so much money being pumped into it—more than $120 billion US dollars in capital expenditure on AI infrastructure alone, just by the Magnificent Seven tech providers. So that bubble could burst,&#8221; explains Forrester&#8217;s expert. This assessment gains particular relevance when considering Google&#8217;s $9 billion AI data center investment in Oklahoma for advanced AI training infrastructure. This financial perspective, however, tells only part of the story. Technological adoption follows a different logic from financial markets, as Schaffrik confirmed during our exchange about the AI bubble burst potential. &#8220;But now, if you put yourself in the shoes of enterprise decision makers, tech decision makers, also AI users, there are many who would say, &#8216;I don&#8217;t care if that bubble bursts, the technology is there, and it won&#8217;t go away.&#8217; “Regardless of the amounts all the financial transactions surrounding the AI industry, people are actually using this technology. And they like what they are seeing. It might not be the disruptive, transformative value some are surmising. It&#8217;s probably more incremental than that, but the adoption of that technology is undeniable.&#8221; The Revenue Challenge: A $25 Billion Gap to Bridge Fortune&#8217;s analysis reveals a concerning gap between current investments and generated revenues. To justify current investments, AI companies would need to generate $40 billion in annual revenue, while they currently produce only $15 to $20 billion. Schaffrik doesn&#8217;t believe in an AI bubble burst right now — image made with Adobe Firefly I was wondering whether this $20-25 billion gap could represent a systemic risk that could trigger an AI bubble burst. Schaffrik remains relatively optimistic on this point: &#8220;There is still enough money in that market to back these revenue gaps at least for a while. And what I&#8217;m also seeing is that especially when it comes to the largest enterpriseson the planet, they are convinced to continue using that software. And if it comes at a premium which is decent, arguably, maybe a couple percentage points higher than what they are paying today for the software, then this seems to be acceptable.&#8221; This acceptance of additional costs by large enterprises stems from the incremental value they perceive, even if it hasn&#8217;t yet reached the promised transformation level that might prevent an AI bubble burst scenario. LLM Regression: A Warning Signal? A particularly troubling element in the current ecosystem is the recent NewsGuard study revealing that major LLM systems are no longer progressing but regressing, generating more hallucinations and errors. This observation raises fundamental questions about current technology maturity and its impact on AI bubble burst predictions. &#8220;I&#8217;m not saying that LLMs and generative AI are progressing in a linear fashion nor that this technology will be disruptive in any way, despite the promise. As we have seen with emerging technologies for decades and even centuries, it takes breakthrough technological revolutions rather than evolutions to fulfill such promises,&#8221; analyzes Schaffrik. This vision of the current limitations of AI doesn&#8217;t diminish Bernhard’s long-term optimism: &#8220;But I&#8217;m also convinced that these breakthroughs will happen, not within the next seven, eight, nine, 12 months, but maybe in the long term. Something else will be coming up.&#8221; Energy Efficiency: The Achilles&#8217; Heel of AI One of Schaffrik&#8217;s most compelling criticisms concerns the energy efficiency of current systems. His comparison between the human brain and data centers is striking and relevant to understanding whether we&#8217;re facing an AI bubble burst. &#8220;If we look at the amount of energy our brains are requiring to create a certain inference, and how much an LLM would require to achieve the same result with electricity, this cannot be the way forward.&#8221; This energy inefficiency constitutes a major barrier to scalability and will require significant technological breakthroughs to overcome, potentially influencing AI bubble burst timing. Pilot Failures: Business as Usual or Red Flag? The 95% failure rate of corporate AI pilots revealed by MIT research might seem alarming and suggestive of an impending AI bubble burst. Yet Schaffrik places this figure in its historical context: &#8220;It&#8217;s quite normal. As an analyst covering innovation management, what I have been observing over time is that about 10% of all innovation-related minimum viable products, proof of concepts, pilots, will turn into a product.&#8221; The problem would rather lie in unrealistic expectations: &#8220;Everybody rushed at it because one believed that since it&#8217;s accessible through natural language, it should &nbsp;be easier to deploy, to implement, and there are no drawbacks and negatives. That might explain that the failure rate is slightly higher than with technologies we saw in the past.&#8221; This assessment aligns with Gartner&#8217;s prediction that 30% of GenAI projects will be abandoned after proof of concept by 2025 due to poor ROI and unclear business value. AGI: The Next Revolution in Motion Despite current limitations, Schaffrik maintains his bold prediction from his July 2025 analysis &#8220;Demystifying Artificial General Intelligence&#8221; that Artificial General Intelligence (AGI) represents &#8220;the biggest change in tech we have ever seen and is starting right now.&#8221; This vision, which could influence AI bubble burst scenarios, is structured around three maturity stages. &#8220;Competent artificial general intelligence is lurking around the corner. Our prediction is that between 2026 and 2030, we will see competent artificial general intelligence. You could think of it as your first trustworthy AI agent. You might not want to give it your car keys or your wallet, but it might do amazing things.&#8221; The subsequent stages would unfold over more distant horizons: independent AGI within the next five to ten years, And lastly, strategic AGI in a more distant future. Current AI Limitations: Experience versus Training A crucial point raised in our discussion concerns the difference between training and experience. As I pointed out to Schaffrik, experience develops critical thinking that current LLMs don&#8217;t yet possess, which could impact AI bubble burst predictions. &#8220;We might get to a point where most of us humans wouldn&#8217;t be able to tell if on the other side, a human or a machine is interacting with us. There will be areas where we will still be able to tell. But experience is something we could at least partially solve with more data and better data,&#8221; states Schaffrik. The solution, according to him, lies in massive data collection: &#8220;So much of the billions of investment money flowing now into all these big companies is also to collect and curate data also from the physical environment. Bringing all this data together, will create something that mimics experience.&#8221; The Human Factor: What&#8217;s Left for Us? The philosophical question of what humans will do when machines surpass us in thinking capabilities represents Schaffrik&#8217;s personal concern regarding potential societal implications of advanced AI, regardless of any AI bubble burst. &#8220;That&#8217;s my personal doomsday scenario, I must say. It&#8217;s not good for us humans to just idle around. So it&#8217;s not so much a technical conversation, but more a political, societal, psychological and philosophical one. So I&#8217;m sure we are far away from this, but we are getting there.&#8221; Leadership and Preparation: The Governance Challenge Regarding leadership in this transformation, Schaffrik acknowledges the complexity: &#8220;Rulers are supposed to rule. The question is more like, are they intentionally gathering a diverse set of experts who would be able to consult them? Technically, this is possible. Are they willing to? It&#8217;s another question.&#8221; His confidence in human adaptability remains intact: &#8220;I&#8217;m still confident that once we are realizing the true dangers of certain technologies, we will start to rethink. And we have always found a way to move forward, and we will find a way this time as well.&#8221; Conclusion: Beyond the AI Bubble Burst Debate Our conversation with Bernhard Schaffrik reveals that the AI bubble burst question transcends simple financial considerations. While financial markets may indeed experience corrections, the underlying technology continues its unstoppable advancement. The key insight is that we&#8217;re witnessing a fundamental shift that will persist regardless of market volatility. Schaffrik&#8217;s analysis suggests that rather than fearing an AI bubble burst, we should focus on preparing for the transformative changes ahead. The technology won&#8217;t disappear, but it will evolve in ways we can barely imagine today. As we stand at this inflection point, the question isn&#8217;t whether the AI bubble will burst, but how we&#8217;ll navigate the profound societal and technological transformations that lie ahead. The AI bubble burst debate, ultimately, is just the beginning of a much larger conversation about our future with increasingly capable technology. The post Is the AI Bubble About to Burst? appeared first on Marketing and Innovation.

The world of artificial intelligence is evolving at breakneck speed, and nowhere is this more conspicuous than in the emergence of AI agents. As organizations grapple with separating genuine innovation from marketing hype, we sat down with Ed Keisling, Chief AI Officer at Progress Software, to cut through the noise and understand what AI agents really mean for businesses today. Ed brings a unique perspective, having taken on his new role in February 2025 at a time when the industry is proclaiming this as &#8220;the year of agents.&#8221; His insights reveal both the tremendous potential and the current limitations of this transformative technology. As always, time is of the essence. AI Agents, Beyond the Hype Preogress Software&#8217;s Ed Keisling did a great job debunking the myths surrounding AI Agents and showing what the future holds beyond the hype &#8211; photo Progress Software. The Rise of the Chief AI Officer: A Strategic Imperative The creation of Chief AI Officer roles across the technology industry signals more than just a trend—it represents a fundamental shift in how businesses view artificial intelligence. As Ed explains, &#8220;AI needs to be a strategic pillar of a business to drive innovation and growth. It really reflects the pace at which technology is evolving, and having somebody that is accountable to follow all these latest updates and really look at it through the lens of new risks and opportunities.&#8221; This observation resonates with the broader digital transformation patterns we&#8217;ve witnessed over the past decade. Just as Chief Digital Officers emerged to guide organizations through the digital transformation revolution, Chief AI Officers are now stepping up to navigate the AI transformation. The role isn&#8217;t merely about implementing technology—it&#8217;s about strategic thinking, risk assessment, and identifying genuine business opportunities in a rapidly changing landscape. AI agents: the promise with tools like Manus is that they would behave like your favourite dog. Go search, Rover&#8230;! — photo by antimuseum.com Defining AI Agents: Beyond the Buzzwords One of the most persistent challenges in the AI space is the confusion surrounding terminology. AI agents, in particular, have become an overloaded term that means different things to different people. Ed provides valuable clarity by positioning agents on a spectrum of AI capabilities. &#8220;When generative AI came out, it was generally reactive,&#8221; Ed notes. &#8220;We would go to ChatGPT, provide a prompt, and it would generate a response based on its training patterns. Agents are moving along that spectrum in terms of capabilities—they have the ability to perceive their environment, access to audio, video, documents, and crucially, the ability to reason, plan, and learn from their actions.&#8221; Unfortunately, Rover isn&#8217;t always willing to search in the right direction&#8230; — photo by antimuseum.com Traditional automation relies on strict rule-based systems—the digital equivalent of if-then-else logic. Chatbots, while more sophisticated, remain predominantly reactive. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances The evolution doesn&#8217;t stop there. Ed introduces the concept of &#8220;agentic AI&#8220;—a broader paradigm where multiple agents collaborate, passing context between each other to accomplish complex tasks. This represents the holy grail of AI automation: systems that can dynamically adapt to real-time situations without constant human intervention. Reality Check: Why Perfect Automation Remains Elusive Despite the exciting potential, Ed provides a sobering reality check about current capabilities. His observation about the Pareto principle in AI is particularly insightful: &#8220;AI is the ultimate manifestation of the 80/20 rule. You can very rapidly get to value with 20% of the work achieving 80% of the results, but actually getting it to work 100% of the time is still very, very difficult.&#8221; This phenomenon explains why AI demonstrations look so compelling while real-world implementations often fall short of expectations. The gap between proof-of-concept and production-ready systems remains significant, requiring careful planning, clean data, and well-defined business processes. As always, I should add, &#8220;the more it changes, the more it stays the same,&#8221;as the French poet would have it. RAG Technology: Making AI Practical for Business While pure AI agents may still be evolving, Progress Software&#8217;s acquisition of Nuclia, an agentic RAG (Retrieval Augmented Generation) provider, demonstrates a more immediate and practical application of AI technology. Ed explains the fundamental problem RAG solves: &#8220;Large language models have been trained on the entirety of the Internet, giving them broad general knowledge, but they don&#8217;t have access to data stored behind firewalls or on personal computers.&#8221; This limitation is critical for businesses. While public AI models are impressive, their real value emerges when they can access and reason about proprietary business data. RAG technology bridges this gap, allowing organizations to leverage AI&#8217;s reasoning capabilities while grounding responses in their specific knowledge base. The practical implications are significant. As Ed points out, &#8220;Small to medium-sized businesses have lots of unstructured data—audio, video, log files, recordings, PDFs, charts—that represent proprietary business value, but they have no way of indexing or finding or correlating the data within it.&#8221; RAG technology makes this data accessible and actionable. It&#8217;s high time to stop AI-Washing Ed Keisling advises — image created with Midjourney Separating Innovation from AI Washing Ed&#8217;s experience at the AI4 conference provides valuable insights into the current state of the AI industry. His observation about AI washing is particularly relevant: &#8220;There was a lot of AI washing—companies that weren&#8217;t sure they understood the problem to be solved, with very thin wrappers around language models to solve point problems. It felt like a hammer looking for a nail.&#8221; The key differentiator, according to Ed, lies in problem-solving focus rather than technology-first thinking. &#8220;AI allows you to solve old problems in a new way and to make seemingly impossible problems possible. You&#8217;re thinking about how to drive outcomes—making developers more productive, automating tedious workflows, providing better insights that weren&#8217;t possible before.&#8221; This perspective aligns with successful technology adoption patterns throughout history. The most successful implementations focus on specific business outcomes rather than showcasing technological capabilities. Real-World Value: ShareFile&#8217;s Document Intelligence Progress Software&#8217;s ShareFile platform provides concrete examples of AI delivering measurable business value. The platform serves client-facing teams in regulated industries—doctor&#8217;s offices, law firms, and tax accountants—where document management is critical but time-consuming. The AI implementations are practical and measurable: &#8220;We can create curated lists of documents appropriate based on your situation, and as you upload documents, we can figure out which document relates to which checklist item. We&#8217;ve measured this at being three and a half times faster.&#8221; sharefile-legal-document-workflows-for-legal More importantly, the system addresses security concerns that many organizations face: &#8220;We have capabilities that scan for social security numbers, personal information, and credit card information that you didn&#8217;t want to upload. This single capability flags around 35,000 documents a week.&#8221; These examples demonstrate AI&#8217;s sweet spot: automating routine tasks while enhancing security and accuracy. The value isn&#8217;t just in speed—it&#8217;s in freeing professionals to focus on high-value work instead of admin tasks. The Human Factor: Reskilling Rather Than Replacing One of the most contentious aspects of AI adoption concerns workforce impact. Ed&#8217;s perspective is refreshingly pragmatic: &#8220;This is a fundamental reshaping of how business is done—a new skill and opportunity for people to grow, learn, and reinvent themselves. There aren&#8217;t experts who have been doing this for five or ten years. If you have the headspace and desire, you can become that expert.&#8221; This view positions AI as an enabler rather than a replacement. The technology&#8217;s real power lies in eliminating organizational silos and enabling individuals to accomplish more with the right tools and training. &#8220;One person is now going to be capable of doing multiple things with the right prompts, giving them opportunities to do more and drive more value for the organization.&#8221; The message is clear: organisations with infinite backlogs of valuable work don&#8217;t need to fear AI displacement. Instead, they should focus on upskilling their workforce to leverage these new capabilities effectively. Looking Forward: Practical Adoption Strategies Ed&#8217;s recommendations for AI adoption focus on practical, incremental approaches rather than transformative leaps. &#8220;There&#8217;s enormous green space for individuals to become fully enabled with AI. The majority of people using AI today are using it in a Google-like fashion, but they haven&#8217;t taken time to understand how to correctly prompt agents or use advanced capabilities.&#8221; The most successful implementations start with individual productivity tools—document summarization, email assistance, and internal search capabilities—before advancing to more complex agentic systems. This approach allows organizations to build AI literacy while demonstrating concrete value. In Conclusion, Embracing Reality While Preparing for the Future Our conversation with Ed Keisling reveals that AI agents represent both enormous potential and significant current limitations. While the vision of fully autonomous AI systems remains largely aspirational, practical applications of AI technology are already delivering measurable business value. The key insight for business leaders is the importance of realistic expectations coupled with strategic preparation. AI agents are not yet ready to replace human workers, but they are already transforming how work gets done. Organisations that focus on practical applications, invest in workforce development, and maintain healthy skepticism about vendor promises will be best positioned to benefit from this technological evolution. As Ed concludes, &#8220;You have to put your personal opinions and biases aside and accept and lean into it. At least you can be part of the process and conversation and understand where the edges are.&#8221; This balanced approach—embracing the technology while maintaining critical oversight—represents the most reasonable path forward in the age of AI agents. The future of AI agents is being written today, not in grand demonstrations of artificial general intelligence, but in the practical applications that solve real business problems, one automated workflow at a time. About Ed Keisling Ed Keisling is the newly appointed Chief AI Officer at Progress Software Corporation, bringing over three decades of technology leadership experience. He previously served as Senior Vice President of Engineering for Infrastructure Management at Progress, was an executive team member at Vecna Technologies overseeing Engineering, IT, DevOps, Support, Program Management and Analytics, and spent over 17 years in senior engineering roles at Pegasystems. Specializing in complex system architectures, cloud computing, and large-scale infrastructure management, Keisling also mentors through the UNH Pathways Program and MIT&#8217;s Undergraduate Practice Opportunities Program (UPOP). In his new CAIO role, he leads Progress&#8217;s AI strategy and product portfolio transformation, reporting directly to CEO Yogesh Gupta. The post AI Agents, Beyond the Hype appeared first on Marketing and Innovation.

In an era of overtourism, where mass travel increasingly strains destinations worldwide, Christopher Hill offers a compelling alternative with his voluntourism/volunteer travel business, Hands-Up Holidays. As a founder and managing director of this company, Hill has built a business model that demonstrates how travel companies can be forces for good rather than exploitation. His approach to volunteer travel challenges the conventional wisdom that luxury and social responsibility cannot coexist. Voluntourism: When Volunteer Travel and Luxury Coexist For Mutual Benefit Voluntourism is a portmanteau expression combining &#8220;volunteer&#8221; and &#8220;tourism&#8221; —&nbsp;Photo from Hands Up blog post on Earth Day Eco-Luxury Inspiration (Mexico – conservation of turtles in Baja California by Christopher Hill) What makes Hands Up Holidays’ philosophy particularly noteworthy is its commitment to controlled growth, prioritising quality experiences over scale. Rather than pursuing rapid expansion that could compromise his mission, Christopher Hill maintains personal oversight of every client interaction, proving that sustainable business practices can create more meaningful outcomes for travellers and communities alike. Operating across over 30 countries, Hands Up Holidays represents a fascinating case study in how the apparent contradiction between luxury accommodations and volunteer work can enhance both experiences. Here is the account of our interview. What kind of work are your clients doing during their volunteer travel? We offer a great variety of projects. Our most popular initiatives are building projects, which can range from small-scale but highly tangible endeavours like constructing or installing eco-friendly stoves in village homes to larger undertakings such as helping build houses or renovating school classrooms. Beyond construction, we focus heavily on wildlife conservation projects, where families might care for elephants or participate in sea turtle protection programs. The third major area involves educational support, particularly serving as reading partners in local schools. Each project is carefully selected to ensure meaningful impact while being suitable for family participation. Nayara Tented Camp&nbsp;– The tented camp was built on stilts and a lot of space was left between tents to plant trees and palms between them. Thousands of trees and indigenous bushes have been planted to reforest and repair damage done by cattle farmers. Energy and water conservation measures are in place. The majority of the team is from the local town, and free transport and health services are provided. What triggered your shift from London finance to voluntourism? It was quite a dramatic shift, and in true dramatic fashion, I experienced my own road to Damascus moment in South Africa. This happened about six years into my career in London’s financial sector. During a trip there, beyond the traditional safari experiences and stays at beautiful lodges throughout Cape Town and the Garden Route, I participated in building a house for a family in one of the townships. This experience was genuinely life-changing in two fundamental ways. First, it enabled me to interact authentically with local people, gaining real insights into their lives and sharing stories with them – something that had been missing from my previous travels despite being quite fortunate to travel extensively. Second, the satisfaction of helping and making a tangible difference in their lives by providing this family with a proper home was profound. This experience made me realise that I had developed solid business skills but wanted to apply them to something more meaningful and fulfilling. That became the catalyst for establishing what would become Hands Up Holidays three years later. How did you safely visit townships when tourists are typically advised to avoid them? I should emphasise that there are legitimate reasons for caution. I was fortunate to be in the capable hands of my former London flatmate, who had moved to South Africa and become a professional tour guide, developing his own network of trusted relationships there. He was the one who took me into the townships, and I certainly wouldn’t recommend just showing up there independently. While chances are you’d be fine, you need to remain cautious. I should also mention that there’s a concerning trend of township tourism that can devolve into mere voyeurism, which we absolutely oppose. However, there are ethical township visits that focus on the positive developments happening in these communities and provide genuine opportunities for meaningful interaction. How do you reconcile the apparent contradiction between luxury and volunteer work? Luxury and volunteering don’t immediately seem like natural partners. However, when you examine it more deeply, the luxury component serves as the means to facilitate participation from people who want to make a difference but aren’t willing to sacrifice comfort. This certainly isn’t for everyone, but our model is what I call ‘philanthropy volunteering’. The primary benefit comes from the funds our clients bring to projects. If providing luxury accommodations and creature comforts enables those funds to be invested in meaningful projects, then we’re the right organisation for them. Conversely, if you don’t mind basic conditions, there are many other fantastic organisations that will help you make a difference in that way. How do you avoid voluntourism becoming voyeurism? There are two main approaches we use. First, I personally visit every single project we offer, so I can genuinely attest that they’re beneficial and provide real value to recipients, whether communities or wildlife. Second, this connects to my earlier point about different ways to make a difference. People can contribute through time – spending weeks or months at a project – or through specialised skills, like doctors or physiotherapists applying their expertise. The third way is through funding, which is where we excel. We enable our guests to experience projects and gain that meaningful interaction, but their primary benefit comes from providing the funding to build houses, construct stoves, or create accessible facilities, whatever the specific need may be. How do you convince families to choose volunteer travel? I’d argue it’s primarily the parents’ responsibility rather than mine. However, I think it’s important to understand that no one arrives on our trips surprised to discover they’ll be renovating a school. This volunteer component is our fundamental point of difference – it’s what we specialise in and what sets us apart. People only choose us because this is exactly what they want to do. I hope families have these discussions with their children well in advance of booking. What motivates your clientele differently from typical travel agencies? Absolutely, they’re very different. When I first established Hands Up Holidays, I had people like me in mind – young professionals who were cash-rich but time-poor, wanting good vacations while making a difference. However, from the very beginning, we attracted family bookings, which hadn’t been on my radar at all when I was developing the concept. When I asked these families about their motivations, they’d say things like, ‘Our children come from privileged backgrounds, and we want them to appreciate how fortunate they are,’ or ‘We’re seeking a meaningful family bonding experience.’ Many also express that they want to inspire their children to become the next generation of changemakers. So yes, there’s definitely a strong mission-driven aspect in our family clients’ thinking when they make enquiries. Are your clients younger or older than you expected? They’re older than I anticipated. When I wrote the first business plan and brochure, I was targeting young professionals aged roughly 25 to 35. While we do attract some clients in that demographic, I was genuinely surprised by the number of families booking with us. These family clients are typically in their 30s and 40s. How does volunteer travel address overtourism, and what’s the demand? We take a holistic approach to all our trips, with sustainability integrated throughout. While our trips are luxury experiences, we prioritise properties that demonstrate sustainable luxury principles in their design and operations. We recommend restaurants offering organic dishes sourced locally whenever possible, maintain a policy of using only local guides, and choose eco-friendly transport options where available. This approach helps combat overtourism. We also encourage travel to safer but less mainstream destinations – places like Georgia in the Caucasus, Belize, or Roatan in Honduras, which we’re launching in the coming weeks. These destinations aren’t overcrowded with tourists. Additionally, incorporating volunteer components naturally slows down the pace of travel. Instead of rushing from site to site, you’re investing several days in one particular destination and community. How do you ensure controlled growth while maintaining quality over scale? For me, the key is maintaining this as a passion project. I live and breathe this work, and I personally handle all customer enquiries. This isn’t just about passion – I genuinely delight in crafting unique itineraries for our clients – it’s also about quality control. I’m happy that it’s just me managing this aspect, and by virtue of that personal involvement, it naturally limits how much the business can scale. This constraint actually serves our mission perfectly. Voluntourism: Small Is Beautiful and Meaningful The volunteer travel market in which Christopher Hill’s Hands Up Holidays operates represents less than 0.01% of the global tourism industry’s USD&nbsp;11.7&nbsp;trillion annual revenue (World Travel &amp; Tourism Council, Future Market Insights). The numbers may look small, but the company is nonetheless showing the way for the reinvention of the travel industry, which sorely needs it. Walk through any popular tourist destination nowadays, and you’ll see tourists seeking familiar food. This isn’t criticism – it’s human nature. But it highlights how we travel without truly connecting. Tourism is bringing people to destinations people think were made for them but would be better without them. And this is sad. This isn’t what ‘travelling’ is about. It’s about connecting, rubbing shoulders with the locals, understanding or trying to grasp foreign mores, tasting local food, etc. Hill’s approach differs greatly in that. The voluntourism/volunteer travel model won’t transform the entire industry overnight. But it proves alternatives exist. Travel can serve communities rather than exploit them. And you don’t need to rough it for that matter. Lastly, growing a business doesn’t require sacrificing one’s values. Hill shows that meaningful travel is possible. Not revolutionary, just different and respectful. Here are a few numbers collected about voluntarism/volunteer travel The following facts and figures were gathered with the help of Perplexity and checked against their sources. Errors may have occurred, readers are advised to double check the numbers before quoting. All sources are available at the end of this blog post Volunteer tourism (voluntourism) Market size in 2024: Between USD&nbsp;873&nbsp;million and USD&nbsp;962&nbsp;million [2] [9]. Projected market size for 2025: About USD&nbsp;962&nbsp;million to USD&nbsp;1&nbsp;billion [2] [7]. Projected market size by 2030: Between USD&nbsp;1.2&nbsp;billion and USD&nbsp;1.55&nbsp;billion, depending on the growth rate used by different sources [2] [1] [9]. Annual growth rate (CAGR): Most sources agree on a growth rate between 4.8% and 6.21% yearly through 2030 [1] [6] [7] [9]. Key participant demographics and trends An estimated 1.6&nbsp;million people volunteer abroad each year [6]. The youth market (ages 15–29) is a major driver, with this age group representing approximately 23% of all international tourism arrivals [6]. Popular activities include community development, environmental conservation, teaching, healthcare, and cultural exchange [5]. The main destinations are developing countries in Asia, Africa, and Latin America, but Europe and North America also see significant participation, especially for environmental and social projects [5] [7]. Regional insights Europe accounts for a sizeable share due to its large youth traveller population and established gap year culture [6]. North America (notably the US and Canada) and Asia-Pacific (notably Japan, South Korea, and China) are also significant regions for volunteer tourism growth and participation [7]. Caveats The above numbers primarily refer to the economic value of the voluntourism industry and not the total number of trips or individual travellers. COVID-19 disruptions affected international travel and temporarily slowed market growth, but current estimates show a strong rebound in recent years [7] [6]. Overall, volunteer tourism continues to grow as travellers seek more meaningful, responsible, and impactful travel experiences [3] [6]. Sources [1] Volunteer Tourism Market Size, Share &amp; Growth Report, 2030 https://www.grandviewresearch.com/industry-analysis/volunteer-tourism-market-report [2] Volunteer Tourism Market Size &amp; Forecast [2033] https://www.globalgrowthinsights.com/market-reports/volunteer-tourism-market-118068 [3] Volunteer Tourism Global Business Report&nbsp;2025 | Cultural https://www.globenewswire.com/news-release/2025/05/07/3076249/28124/en/Volunteer-Tourism-Global-Business-Report-2025-Cultural-Immersion-Experiences-Drive-Adoption-of-Long-Term-Volunteer-Tourism-Itineraries.html [4] Volunteer Tourism Global Business Report&nbsp;2025 https://uk.finance.yahoo.com/news/volunteer-tourism-global-business-report-133500797.html [5] Volunteer Tourism Market Decade Long Trends, Analysis … https://www.archivemarketresearch.com/reports/volunteer-tourism-market-7751 [6] The European market potential for volunteer and … https://www.cbi.eu/market-information/tourism/volunteer-and-educational-tourism/market-potential [7] Volunteer Tourism Market Size &amp; Share Forecasts https://www.fundamentalbusinessinsights.com/industry-report/volunteer-tourism-market-13216 [8] Purposeful travel in 2025: The changing face of voluntourism https://fooddrinklife.com/volunteer-tourism/ [9] Volunteer Tourism https://www.marketresearch.com/Global-Industry-Analysts-v1039/Volunteer-Tourism-41409685/ [10] Voluntourism – Tourism and Travel: A Research Guide https://guides.loc.gov/tourism-and-travel/voluntourism The post Voluntourism: Changing the World from Your Hotel appeared first on Marketing and Innovation.

AI is radically transforming the B2B sales landscape and accelerating the shift towards intelligent sales enablement. At a major B2B event which took place in Paris in July 2025, I met with Stephane Renger, co-founder and managing director of Salesapps. The leading European sales enablement vendor has placed AI at the heart of its innovation strategy. In this interview conducted at the event, Stephane explains how AI agents are revolutionising commercial efficiency whilst maximising security and privacy. A fascinating dive into the future of AI-powered sales enablement that’s redefining commercial performance standards. AI Saves Time for Sales and Marketing Teams AI-powered sales enablement : Stephane Renger is the co-founder and General Manager of European sales enablement company Salesapps. How is AI transforming sales enablement and sales in general? Stephane Renger: At Salesapps, we’re working on AI agents with the goal of bringing greater efficiency to sales teams, while maximising data privacy and security. Specifically, what do these AI agents do? S.R. We developed three types of intelligent agents for our AI-powered sales enablement solution. The &#8216;company profiler&#8216; AI agent analyses the targeted business regarding its strengths, weaknesses, products, competition, news&#8230;, The ‘individual profiler’ agent describes the buyer’s profile: background, interests, pain points… Then we use content enrichment agents with metadata to generate sales presentations and pitches, Finally, our &#8216;conversational agent&#8217; restructures meeting minutes and reports. Once again, this major B2B event took place in the prestigious premises of the Parc des Princes in Paris, with a focus on AI-powered sales enablement. What are the efficiency gains from the implementation of AI within sales enablement? S.R. They are quite blatant, mainly in terms of time savings. Thanks to AI, salespeople quickly access information that used to take hours to research. For a complete sales team, there is at least 20% time saving, which equates to one working day every week. Marketing teams and content managers can save up to 50% of their time. Is this an opportunity for getting rid of salespeople? S.R. No, it&#8217;s not. Salespeople only spend one third of their time actually selling. Two thirds are devoted to paperwork, CRM, preparation. Automation frees up their time from what isn&#8217;t their core business. Are certain sales roles more threatened than others? S.R. It depends mainly on the products being sold. The buyer only spends 5% of their time with the salesperson, 80% of the purchasing process happens on the web. So the remaining 5% must involve a complex buying process to require human intervention. In other cases, self-service is way sufficient, even in B2B. In a nutshell, Sales Enablement becomes AI-Powered Sales? S. R. For the past two years, we’ve been talking more about ‘AI-powered sales enablement’ than traditional Sales Enablement. This concept is more immediately understandable and evokes innovation in the commercial approach. Worthy of note, for English-speaking markets, we keep that term ‘sales enablement’, but for French-speaking markets, ‘Modern Selling’ is the term of choice. It is more meaningful, especially when discussing AI agent integration, for French-speaking audiences who do not always understand the word ‘enablement’. What can you tell us about reliability and security concerns? S.R. Classic AI models, be it Gemini, ChatGPT, or others are used to research public information. If I want to know who you are or what your company does, there’s nothing better than such a tool that will browse the web, LinkedIn, and other sources to obtain this information. But if I want to process proprietary information, an internal document database, some meeting minutes, an internal conversation, etc., we must ensure data privacy and security at all cost. In this case, we’d rather work with on-premise language models that we can monitor end-to-end. We’ll feed them with this internal information and secure that information. What are your views regarding the future of AI-powered Sales Enablement? S.R. It&#8217;s very difficult to predict the future even three to six months from now. Our focus revolves around artificial intelligence. Its performance is increasingly impressive and prices more affordable. Right now, I&#8217;m particularly focused on sales coaching and training, and commercial skills development. Simulation and capturing a real-life interview in audio is already possible, but video is still relatively expensive, almost €50 per hour. With the lightning-fast progress in synthetic voices and images, I&#8217;m convinced that within three to six months, we&#8217;ll be able to offer much more advanced technologies at prices that will allow widespread distribution of this type of agent to sales populations and businesses. In Conclusion: The AI-Powered Sales Revolution These past ten years, Visionary Marketing has written quite a few pieces and white papers on the subject of Sales Enablement. Since then, technological progress and sales team transformation have been lightning fast. This interview with Stephane Renger perfectly illustrates this profound transformation of the sales domain. AI-powered sales enablement is now fully operational, and this technology is redefining commercial efficiency. The productivity gains quoted by Salesapps — 20% for sales teams, 50% for marketing teams — are a sign that something serious is happening now. What struck me at this major B2B event in Paris, was the profound transformation of the market offering in this domain. A few years ago, this side of the water, many Sales Enablement vendors were present, either international or local. In just a couple of years, Salesapps* established itself as the undisputed leader in this sales enablement market in French-speaking countries and is now conquering other markets. The future looks exciting: between conversational AI agents, personalised coaching, and predictive analysis, tomorrow&#8217;s salesperson will have tools at her disposal of unmatched power. We haven&#8217;t yet seen the end of the evolution of the sales function, and digital technologies are playing a major role in this upheaval. [*Disclosure: Visionary Marketing worked for Salesapps in 2022] The post AI Sales Enablement: 20% Time Savings for Sales and 50% for Marketing appeared first on Marketing and Innovation.

Are AI and developers the world&#8217;s best friends or is artificial intelligence a threat to the future of programmers? As artificial intelligence models are becoming increasingly sophisticated, many questions are raised about the future of developers across the industry. Will AI replace programmers entirely, as Eric Schmidt and Dario Amodei are predicting? Will junior developers be facing extinction, as Steve Yegge surmised in a now-famous blog post? Or are we witnessing the dawn of a new era in which technology amplifies human creativity rather than replacing it? I interviewed Nathaniel Okenwa, Developer Evangelist at Twilio, to pick his brains about this question, and his conclusion is that, in the future, software development will undoubtedly remain human-driven even though many changes will occur. The video recording of that interview is available at the end of this blog post. Developers and AI: the Path to the Future of Coding With nearly a decade of hands-on programming experience and a unique perspective on developer community engagement, Nathaniel Okenwa brought both technical depth and strategic insight to this conversation about the evolving landscape of software development. Spreading the Gospel of Developer Tools &#8220;My parents celebrated when I got that job title of developer evangelist,” Okenwa said. “I speak and meet with developers, online or in person, and I talk about the tools and the technologies they&#8217;re using. A part of this job is being with the community and then spreading the good news of Twilio as well.&#8221; AI and programmers, a love-hate relationship? —&nbsp;image antimuseum.com For those unfamiliar with the company, &#8220;Twilio is a customer engagement platform and one of the providers helping businesses with their customer support, communication tools, and APIs.&#8221; The Junior Developer Dilemma The elephant in the room for the Tech industry is the fate of junior developers. Steve Yegge’s provocative piece ‘The Death of the Junior Developer’ has sparked intense debate, suggesting that AI won’t make inexperienced developers smarter but will enable experienced programmers to eliminate the need for juniors altogether. A daunting perspective for young programmers. Nathaniel offers a more nuanced perspective that challenges this binary thinking . Often, a company doesn’t hire junior developers for their current capabilities. They recruit them because they&#8217;re investing in what they will become in the future. Junior developers need to exist if we are going to have mid-level senior developers, developer leaders, and architects at a later time. Nathaniel is right. Programming isn&#8217;t just about syntax and algorithms; it&#8217;s about developing problem-solving instincts, understanding business contexts, and learning to translate human needs into technological solutions. ‘If you want to create the next generation of builders, then I don’t think junior developers are going to disappear in the long term. We may forget how important they are for a little bit, but they will definitely make a comeback later on.’ The Eric Schmidt Prophecy: Six Months to Obsolescence? The urgency of these questions intensified when Eric Schmidt, former CEO of Alphabet, made bold predictions about the timeline for developer displacement. His assertion that developers would be reduced to merely correcting AI output within six months and potentially eliminated entirely within a year sent shivers down the spines of the programming community. Nathaniel acknowledges the partial truth in Schmidt’s predictions while advocating for a more sophisticated understanding of what developers do. &#8220;I think there are elements of truth in it, but I think the situation is a bit more nuanced. AI is, in my mind, another Industrial Revolution. In this context, it means we’ll be looking for repeatable tasks that are extremely simple and how to replace them with technology.&#8221; AI and developers: programming isn’t just about writing code, it&#8217;s about solving business issues — image of a banker in a large banking institution antimuseum.com The industrial revolution analogy is particularly apt here. Just as mechanisation didn&#8217;t eliminate human work but transformed it, AI appears poised to reshape rather than replace programming roles. &#8220;I think AI is going to take some aspects of programming and make them so cheap from an effort perspective that it&#8217;s not necessarily going to be the best use of people&#8217;s time. However, I think developers aren&#8217;t just folks that are repeatedly solving minor syntax sentences. They are creative builders coming up with different ways of taking a real-world problem and abstracting it into technology pieces.&#8221; AI and Developers: The Abstraction Ladder One of the most compelling aspects of Nathaniel&#8217;s perspective is his emphasis on abstraction as the key to understanding how AI will transform development work. Rather than replacing developers, AI represents another rung on the abstraction ladder that programmers have been climbing for decades. Right now, I can use my programming skills to build a website and serve it to millions of people on the Internet. Thirty or forty years ago, I would have needed a whole set of different skills to make that happen. I would have needed so many more hardware skills and so many more specific high-level networking skills. And all of those things have been abstracted away for me to really focus on making this website really fast and performant. This historical perspective illuminates a pattern that AI-anxious people are missing. Each generation of developers has built upon increasingly sophisticated foundations, allowing them to tackle more complex problems without getting bogged down in lower-level implementation details. AI, the printing press and developers. A brilliant analogy by Twilio&#8217;s Nathaniel Okenwa —&nbsp;image produced with Midjourney The printing press analogy further clarifies this progression: &#8220;If we think about the printing press, at first you needed to have lots of people who would sit down and handwrite a book in order for you to make 100 copies. The printing press came around, and the amount of effort and skills to achieve that shrunk considerably. But you still needed someone who could run that printing press.&#8221; The Inconvenient Truth: Not Everyone Ascends However, this progression toward higher abstraction levels raises uncomfortable questions about inclusivity and capability. Not every developer possesses the intellectual agility to continuously climb the abstraction ladder, and there&#8217;s value in acknowledging this reality. Nathaniel addresses this concern with characteristic optimism while maintaining realism. &#8220;I suppose there will always be people who remain comfortable doing what they are doing in the ways they are doing it. But with technology making so many more different things available, what&#8217;s going to happen is users, customers, and the general public are all going to expect more from our technologies and from us.” The market forces driving this evolution are relentless. As AI enables higher-quality experiences at scale, customer expectations rise accordingly, creating pressure on all technology providers to evolve or risk irrelevance. &#8220;The folks who aren&#8217;t meeting these higher standards of experiences will not be able to deliver the value that their customers and employers are expecting from them. If they don&#8217;t continue to meet that bar of expectation that is growing higher and higher, especially as AI helps people to develop new ways of doing this, they will be left behind.&#8221; The Digital Transformation Paradox This raises an interesting paradox about digital transformation that I&#8217;ve observed throughout my career in technology consulting. Thirty years ago, we predicted that traditional industries like banking would be disrupted by digital-native competitors. Yet established banks have largely survived, adapting gradually while maintaining their market positions. Nathaniel offers an insightful perspective on this seeming contradiction: &#8220;It didn&#8217;t kill banks, but I would argue that even if maybe they took a while to get there, the way we interact with our banks is completely different from the way we interacted with banks when we were younger. There are 18-year-olds who have no idea what a chequebook is.&#8221; The transformation happened, but more gradually and less dramatically than predicted. This pattern suggests that AI&#8217;s impact on development may follow a similar trajectory—profound but evolutionary rather than revolutionary. The GitHub Co-pilot Paradigm: Integration Over Replacement Perhaps the most practical insight from our conversation concerns how AI tools are being adopted by developers. The success of GitHub Copilot and similar tools demonstrates that integration beats replacement as an adoption strategy. &#8220;Sometimes there are engineers who are reluctant to use AI or don&#8217;t want to go out of their way to bring AI into their workflows. But when Copilot came out, when other tools that have that technology built into the applications, the interfaces they are already using, the adoption increases significantly.&#8221; This observation reveals a crucial truth about technology adoption: the most successful innovations enhance existing workflows rather than demanding entirely new ones. The future of development tools lies not in replacing programmers but in making them more effective within familiar environments. The Copy-Paste Continuum One of the most honest moments in our conversation addressed the reality of code reuse – something every developer practises but few discuss openly. The fear that AI will turn programmers into mindless copy-paste operators misses the historical context of how developers have always worked. &#8220;Copying and pasting has been an integral part of the engineer&#8217;s journey for decades, and it&#8217;s not going anywhere soon. Even if we are copying and pasting from a different place, and even if we are no longer using Ctrl-C and Ctrl-V, we have always been learning from the code that others write.&#8221; The key distinction lies not in the source of solutions but in the developer&#8217;s understanding of what they&#8217;re implementing. &#8220;The real issue is that of developers who go and find a solution online and copy and paste it without understanding what is going on. Now, if people do that with AI, they may create a black box, and that&#8217;s going to be great when it works. But when it doesn&#8217;t, when you receive a phone call in the middle of the night, and that production&#8217;s gone down, that&#8217;s a different kettle of fish. Having a black box is not going to be something that a huge company, a huge enterprise, is going to want to rely on.&#8221; The COBOL Conundrum: Legacy Systems and Human Resistance Our conversation touched on one of the most persistent challenges in enterprise software: the prevalence of legacy systems built in languages like COBOL that have resisted modernisation for decades. This serves as a fascinating case study in the relationship between technological possibility and human decision-making. I think we underestimate the power of people. People are sometimes afraid of change. They sometimes want to rely on what has worked for years. And so, even if AI presents a great solution, it&#8217;s going to be people who decide whether to take it on board or not. This observation cuts to the heart of why technological predictions often prove overly optimistic. Technical feasibility doesn&#8217;t guarantee adoption, and institutional inertia remains a powerful force in shaping the pace of change. Advice for the Next Generation of developers For students and emerging developers wondering how to navigate this uncertain landscape, Nathaniel offers pragmatic guidance that emphasises exploration. &#8220;The first thing is to explore and experiment. Experiment with the new technologies. Find out what those new job titles are going to be and chart the path to become an expert. Either a specialist in using AI as a tool, or an expert in creating AI, or an expert in maximising its performance, or an expert in building the tools that AI will use.&#8221; Nathaniel’s emphasis on APIs is particularly noteworthy: &#8220;APIs are nothing new. The better your API, the better the AI agents that will resort to it.&#8221; This suggests that developers who understand how to build AI-friendly interfaces will find themselves increasingly valuable. Most importantly, he advocates for focusing on uniquely human contributions: &#8220;Don&#8217;t just focus on the typical tasks a junior developer is asked to do because AI can do them really well. Instead, focus on the ways that you can bring a unique twist to your solution.&#8221; AI and Developers: Embracing Change While Staying Human As our conversation with Nathaniel Okenwa demonstrates, the future of developers isn&#8217;t about choosing between human programmers and AI systems—it&#8217;s about understanding how these technologies can work together to solve increasingly complex problems. The most successful developers will be those who embrace AI as a powerful tool while focusing on the uniquely human aspects of software development: creativity, problem-solving, and the ability to translate human needs into technological solutions. In a sense Nathaniel’s advice isn’t that different from Steve Yegge’s. You’ll have to become an expert to make good use of AI; it’s not artificial intelligence that will turn you into an expert. Rather than fearing obsolescence, developers should view this moment as an opportunity to evolve, much as previous generations adapted to new programming languages, frameworks, and paradigms. The future belongs to those who can harness AI&#8217;s capabilities while providing the human insight, creativity, and understanding that no algorithm can replicate. The future of developers isn&#8217;t about replacement—it&#8217;s about enhancement, evolution, and the continued pursuit of building technology that serves human needs. In that mission, developers remain not just relevant but essential. The post The Future of Developers in the Age of AI appeared first on Marketing and Innovation.

How do luxury brands maximize experiences in sports events? I attended the 2025 Monte-Carlo Masters, which showed a strong presence of elite brands fighting for high-end customer engagement. Brands such as Rolex, Sergio Tacchini, and Replay can be found advertised almost everywhere at the famous tennis tournament. These brands use the values of this tennis tournament&#8217;s identity, which are class, prestige, excellence, and exclusivity, to reinforce their brand image. In this article we will be looking into the strategy behind premium companies and their connection to the Monte-Carlo Masters Tennis Tournament.  Luxury Brands Maximize Experiences in Sports Events This photo was made using Midjourney and Adobe Photoshop. Sports Sponsorship in Luxury Branding Luxury brands have had a history of gravitating towards sports such as tennis, golf and equestrian sports because these sports emphasized precision, elegance, and tradition. Brands saw that it seemed like a good fit for their deluxe identity due to the traditional affluent audiences that these sports offered. Only the best of the best athletes competing at these events align with the values of the most luxurious brands that they are the best of what they do. These brands are able to prolong their exclusivity while opening up visibility to sports viewership. As brands become bigger and sports viewership grows, stylish brands are opening up to collaborations with bigger sports that may not have as much class or prestige, such as football and basketball.  Strategic Brand Positioning at the Monte-Carlo Masters What once was known as the Monte-Carlo Masters is now known as the Rolex Monte-Carlo Masters. Rolex has positioned themselves front and center at a prestige tournament. Not only are they in the title of the tournament, they are on the logo and can be found everywhere at the tournament itself. Another brand that has strategically positioned itself is Sergio Tacchini. Being at the tournament itself, it is impossible to miss; every ball kid and many employees working for the tournament wear a piece of clothing from Sergio Tacchini. Just being at the tournament, you are constantly being advertised to, whether you realize it or not; everywhere you look, you are reading another brand name. Other brands, such as Maserati and Emirates, help back the elitist and prestigious image of the tournament. Monaco, home of the tournament, is known for its wealth as well as its opulent residents, one more reason to advertise an elegant brand, as the target market is mainly wealthy individuals. “According to the World Population Review, Monaco is the richest country in the world in terms of GDP per capita and is regarded as the &#8220;billionaires&#8217; playground.&#8221; Celebrities and top-level athletes being at the tournament make being at the event feel like it&#8217;s only for those of wealth, class, and elegance.  This photo was made using Midjourney and Adobe Photoshop. Brand and Customer Experiences  Some of the most exclusive experiences at the Monte-Carlo Masters are sponsored by posh brands. VIP lounges and luxury suites are curated for high-end customers and guests. Additionally, behind-the-scenes access, meet-and-greets with athletes, and fancy gifting moments allow brands to showcase their exclusivity even more to only those that can afford them. Hospitality packages include gifts, discounts, special access, and events made to feel extraordinarily classy.  The Société des Bains de Mer (SBM), which is responsible for venues at the event, creates gourmet dining opportunities as well as private lounges mimicking luxury brands. Customers at the Monte-Carlo Masters are rewarded just by being at the event itself. These rewards include access to limited edition merchandise, entries to giveaways or raffles, and the opportunity to use the VR Tennis simulator. To further show status of class and order, boutiques at the event are limited to a certain amount of people at a time to prevent cluttering. When a customer buys a ticket for the tennis match, they are not just coming to watch one game, they are spending practically the whole day there. Even in between matches there are activities to be done around the venue. These activities include, finding something to eat (there are a lot of choices), VR tennis simulator, walking around and exploring the area, shopping in the countless boutiques and tennis stores. Scarcity and “Limited Edition” The value of going to a sports event comes from the electric anticipation of not knowing what will happen. Fans come to witness firsthand the action and to purchase limited edition products showing they were there. Rolex and other brands offer merchandise exclusive to the event itself, signifying someone was at the event. A piece of limited edition merchandise to show you were at the Rolex Monte-Carlo Masters is also an advertisement every time you wear it in public. Short-term promotions and exclusive collaborations build a sense of urgency used to encourage customers to buy their product. Brands use this to drive up their exclusivity, giving only people that were at the event a chance to purchase something that will remind them of how special that moment was.  This photo was made using Midjourney. Content and Social/Digital Media Social media has completely changed this world as we know it, and that is not only limited to the social aspect, but it has changed the business world drastically. Content creation is a great way to gain publicity, and what better place than a sports event to promote your upscale brand? Brands such as Replay and Malongo took advantage of this event sponsorship and made social media promotions showing their collaboration with the renowned event. Rolex and Tennis Rolex has been partnering with tennis for 46 years, since 1978. Made with Napkin AI. Key Statistics about Luxury Brands in Sports Events These statistics show that the tournament continues to improve their technology and assets. Furthermore increasing their brand identity relating to top class. Maximizing luxury brands customer engagement in sports events: facts and figures about the 2025 Monte-Carlo Masters &#8211; infographic done with Canva Conclusion Classy, lavish brands strive to take advantage of events such as the Monte-Carlo Masters tournament to build brand identity and to increase publicity. The Monte-Carlo Masters serves as a sort of “playground” for luxurious brands to strategize, promote, and attach themselves to the values that the event portrays. Brands that strive to align with excellence, class, prestige, and elitism promote themselves using the Monte-Carlo Masters to represent these traits. These companies take advantage of the tournament knowing that the audience is generally wealthier. The post Luxury Brands Maximize Experiences in Sports Events appeared first on Marketing and Innovation.

If you are dying to understand the various GenAI prompting methods, how AI interacts with your prompt, and why this is key to optimising your results, this free prompting guide was made for you. This manual describes GenAI chatbots and the different methods of prompting. It was put forward by Frederic Cavazza, a digital transformation expert, consultant and speaker with over 25 years of experience. A Free Prompting Guide for Aspiring GenAI Experts This free GenAI prompting guide written by Frederic Cavazza was translated and adapted by yours truly. I&#8217;ve known Frederic Cavazza for years and I&#8217;ve even had the pleasure of working on a few engagements with him. As we were on our way to a GenAI client workshop a few months ago, he showed me this guide and I thought to myself: &#8220;This is exactly what I&#8217;d like to share with my readers and students&#8221;. Hence this translation and adaption of Frederic&#8217;s prompting guide, with his kind permission. I tried his tricks myself and I can guarantee that the mega prompt he describes at the end of the guide is really something you should test, copy, paste, adapt and keep in your own prompt library. A GenAI Guide about the Art of Prompting Artificial intelligence is booming, and chatbots like ChatGPT are radically transforming the way we interact with digital tools, changing the way we work. This guide aims to introduce you to the art of &#8216;prompting&#8217;, a key skill for engaging effectively with these artificial intelligence platforms and making the most of their potential. If you&#8217;re researching a topic on the Web and you type in a simple key phrase, the result may not be very compelling. On the other hand, if you structure your search well, you&#8217;ll get more relevant answers. And so it goes with artificial intelligence tools like chatbots or digital assistants. Frederic Cavazza is the author of this great prompting guide entitled the ABC of prompting &#8211; photo portrait by antimuseum.com The more structured the prompt, the more relevant the outcome GenAI Prompting? The way you ask AI chatbots questions or give them instructions is conducive to more or less convincing results. This is what is called ‘prompting’, i.e., the art of formulating clear and precise instructions to guide the work of artificial intelligence models. In essence, a well-structured prompt is like a well-formulated search query. When done properly, it shall provide relevant results. There is no one-size-fits-all prompt methodology, as use cases differ from one user to another. However, we recommend you use one of these three methods based on your needs. Three Recommended GenAI Prompting Methods These three methods are entitled RTF (Role, task, format), CRAFT (Context, role, action, format, tone of voice) and COAT-SITES (context objective, acumen, task, specimen, impediments, tone of voice, encoding, scrutiny). Each technique works best depending on expected results. RTF was made for quick results, CRAFT, for simple questions with more accurate results and COAT-SITES, for clear cut questions and extensive results. So, what are these methods about? Here they are in more detail. 1. RTF Method With RTF, the prompts specify the role, task and format that AI should adhere to. It consists in a simple, “you are…, you must…, your answer must…” Role indicates who the AI bot should impersonate, providing a contextual framework. Task, gives AI the precise action or problem to be solved, guiding AI towards the expected objective. And Format specifies the type and structure of the outcome. 2. CRAFT Method Should you be looking for more accurate results, it might then be a good idea to expand your prompt to incorporate more context. The CRAFT method is therefore what you would have to resort to. CRAFT implies providing specifics to the AI chatbot in your prompt such as, “I’m in charge of…, you are… you have to…, your answer should include…, choose the following tone of voice.” With the CRAFT method, Context describes the overall situation or requirement. Role, tells AI the character it should enact. Action, specifies what AI must do, directing the LLM. Format, provides examples or details clarifying final expectations. Tone of voice, defines the expected style or category AI must follow, aligning your response with your objective and audience. Expanding your prompt to formulate a better structured and accurate result. 3. COAT-SITES Method In the case of COAT-SITES technique, your prompt is not only expanding the context of the situation but giving AI strict guidelines to narrow the margin of misunderstanding. Allowing for your results to be more accurate and extensive. This includes Context, Task, Tone of voice, as in the previous methods but also includes Objective, Acumen, Specimen, Impediments, Encoding and Scrutiny. Objective and Acumen, give AI the tools to reach your expected result with the correct level of expertise. Specimen and Impediments, provide clear examples and guidelines of what is wanted and what should be avoided. By providing models or illustrations, you clarify the expectations, just like defining what cannot be done narrows down your result. Lastly, COAT-SITES encompasses Encoding and Scrutiny. Encoding defines the output format syncing the results to your objective and scrutiny offers a final sanity check to ensure the results comply with your stated guidelines.  Test Drive the Prompting Methods Once you have familiarized yourself with these three prompting methodologies, give them a go. Here are some tips and tricks to keep in mind when navigating GenAI chatbots. Frederic details them all in the guide for you. After you have signed in to a chatbot, enter a few questions into the prompt window to get a feel for how it replies. After you&#8217;ve done that, try testing out the suggested prompting methods, saving a specific topic or tricky task for COAT-SITES. Tips and Tricks When interacting with your favourite chatbot, remember to select relevant keywords, stick to one question at a time, test and tweak your prompts and follow up. Don&#8217;t settle for half-baked answers and consider asking a different chatbot to critique your results. Lastly, remember at all times that the better formulated your prompt, the better AI can provide accurate and relevant results. So give these methods a test and see how your interaction with GenAI chatbots evolves. Download for Free the ABC of Prompting for Aspiring GenAI Experts by Frédéric Cavazza ABC-prompting english-V3 The post GenAI Prompting Guide for Aspiring Experts appeared first on Marketing and Innovation.

How is AI Search changing the Internet and what role are we playing in this transformation? In this article, I discuss the current state of adoption of AI-powered search engines. By reflecting on the perspectives of Kevin Roose, Matteo Wong and Joanna Stern, this piece explores what we gain—faster, more organized access to information—and what we risk losing—the diversity of sources, depth of content and our curiosity to go beyond a single answer. Breaking Up With Your Traditional Search Engine How is AI Search changing the Internet and what role are we playing in it? — image generated with Canva and ChatGPT Is it time to ditch Google and your other favorite search engines? In the past few years, AI has disrupted numerous industries in the digital sector, but arguably one of the most noteworthy shifts has been in the online search market. Think of the last time you searched the internet for information—did you sift through pages of websites, or did AI place the answer at your feet?  The decades-long reign of Google might just be challenged by this new age of conversational and contextually aware search power.  Three Voices on AI Search: Roose, Wong and Stern To examine this idea more deeply, this piece will consider three different articles written by Kevin Roose, Matteo Wong and Joanna Stern. As the conversation surrounding AI evolves, their observations offer unique points, from skepticism to adoption, reflecting the considerable development of AI capabilities and increasing adoption by users.  By breaking down their findings and looking at the numbers of adoption, I will explore the current and future landscape of AI-powered search. So whether you’ve already hitched your ride to the AI bandwagon or are still clinging to your Google tabs, here&#8217;s a glance at where our process of search, the internet, and information is headed.  AI-Search at the speed of light — Photography by antimuseum.com Kevin Roose: Continuing with Caution In February 2024, Kevin Roose wrote an article for The New York Times titled “Can This A.I.-Powered Search Engine Replace Google? It Has for Me.” As you might have guessed from the title, Roose’s experience was a positive one, but not without some hesitation. To test his theory, Roose gave up Google, instead opting for Perplexity, an AI search engine founded by former OpenAI and Meta researchers. Roose’s several-week adoption of Perplexity left him sufficiently convinced AI search engines were a valid competitor against traditional web browsers, but adjustments were needed if they are going to win the race.  The information retrieval and contextual understanding offered by AI proved more useful for the majority of his work. However, due to AI’s limitations, Google was not obsolete. Acknowledging the absence of credible sources, real-time updates, and the occasional lack of truth AI provided, Roose found his usage of AI had certainly become more prominent, but most successful when used alongside Google. The article suggests the adoption of AI will not be a bold movement, but a gradual and natural shift in user behavior. Still indecisive on the effects AI will have on journalists, publishers and others who create the internet landscape, Roose stated, “I’ll have to weigh the convenience of using Perplexity against the worry that, by using it, I’m contributing to my own doom.”  Matteo Wong: Exploratory Search Roose is far from the only one to have concerns about the growing popularity of AI. The Atlantic&#8217;s Matteo Wong placed a heavy critique on AI in his article “The Death of Search.” Wong’s piece focused less on the way in which a person uses AI and more on the way that AI changes our relationship with information. Should you cross traditional Internet Search off the list? Stern&#8217;s answer is a resounding YES ! &#8211; image generated with ChatGPT &nbsp; In his view, the concern is not AI&#8217;s credibility or factuality—those issues could be fixed as systems evolve—but the loss of an exploratory model of search. When people stop engaging critically with information, they lose the ability to evaluate and explore their own curiosity.  He states, “It could completely reorient our relationship to knowledge, prioritizing rapid, detailed, abridged answers over a deep understanding and the consideration of varied sources and viewpoints.” The indication of Wong’s argument is that by extinguishing that exploratory model of search and making information “too” accessible, it dismantles the fundamental idea of the web.  Joanna Stern: Embracing Convenience  In opposition to Wong&#8217;s take on the adoption of AI-powered search engines is Joanna Stern, who shared her full support in her New York Times article, “I Quit Google for ChatGPT—and I’m Not Going Back.” Stern’s piece rings similarly to Roose’s in that once she made the switch, the probability of going back was unlikely. Trying a variety of AI platforms, Stern found the ease and refinement of AI a refreshing break from sponsored links and promoted products offered by Google. Only in searching for a known product, website or article did Google still prove useful.  While she noted AI is consistent with the limitations of poor sourcing and inaccurate information, Stern&#8217;s main concern was the loss of visibility and traffic for the information&#8217;s original source.  What’s to keep AI from making these websites and publications obsolete, and who do you credit for the information you got? Stern wraps her argument into a neat bow by saying, “So, yes, I’ll encourage you to try AI for search, as long as you promise to click a link when you can.” My Experience with AI Search While I have familiarized myself with the platforms ChatGPT and Perplexity, my experience with AI-powered search engines is still limited. However, from what I have seen, I am impressed. The concise, summarized answers leave me satisfied and often without lingering questions. Still, despite the efficiency, I have found I do not stray far from Google. I am not sure if it is a habit or precaution. Looking to the Future  So where do you stand? Are you ready to commit to AI, or is apprehension holding you back? As each article shows us, the level of adoption is varying, and chances are you’re somewhere in the middle. But there&#8217;s no doubt the way we access and receive information is changing. The worldwide market size of AI jumped from approximately $50 billion to $184 billion in just one year. Additionally, a survey by Activate tells us the number of adults in the United States using AI first for their online search was around 13 million just two years ago and is projected to reach 90 million by 2027. The growth is telling, and it&#8217;s not limited to one platform. ChatGPT, Perplexity, Copilot, Claude—even Google is trying to reinvent itself with Gemini. So whether you&#8217;re a curious newcomer, cautious observer or full-on convert, it’s clear we are stepping into a new age of internet. Moving forward we will have to decide if the gains outweigh the losses and with continual adoption, new questions arise. Will AI search enhance or limit access to diverse perspectives? How much of a role will it play in our daily lives? I guess we will have to wait and see. AI search is taking Internet search a level higher — photo by antimuseum.com The post AI Search : Breaking Up With Your Traditional Search Engine appeared first on Marketing and Innovation.

Let AI handle the chores, and humans do the thinking: such should be the future of content marketing. In this piece, I try and debunk a few myths. Firstly, generative AI  can be creative — and often is. Secondly, AI doesn’t necessarily make us stupid; we don’t need it for that. And thirdly, becoming a prompting Guru isn’t necessarily the key to producing great content. The question of AI’s role in content marketing is actually more strategic than technical: it&#8217;s about why and for whom we create content. This is the major issue at stake for today’s and tomorrow’s marketers. In this presentation, I urge readers not to outsource their thinking to AI, and rather offload the chores of low-value tasks to machines. Unfortunately, it should be noted that they aren&#8217;t always doing a good job with that. Chores to AI, Ideas to Humans Since the machines started thinking, we’ve had more time to do the dishes, wrote Joanna Maciejewska. Like her, I&#8217;d rather it were the other way round. TL; DR Ms Bernard is an SEO agency avatar who adds links to Visionary Marketing on &#8220;her&#8221; website. Her “work” raises some fundamental questions.  Criticisms aimed at AI often miss the mark and overlook fundamental issues: why we write, for whom, for what purpose&#8230; We also dismiss a few myths such as &#8216;AI can&#8217;t be creative&#8217;, &#8216;AI makes us stupid&#8217;, and &#8216;mastering prompting is a silver bullet&#8217;. Hence, the question of AI’s role in content marketing is more about strategy than it is about tech. In this presentation, I urge content creators (and readers alike) not to outsource their reasoning and to leave the chores to AI. This piece owes a lot to Ms Joanna Maciejewska AI and Marie Bernard, the e-commerce Queen Ms Benard is adding links to Visionary Marketing. She is very nice but unfortunately she isn&#8217;t a real person. Let me introduce you to Ms Marie Bernard. This pretty young woman, somewhat artificial in appearance, exists only in Midjourney’s archives and on the website of “her” SEO agency. This supposed e-commerce expert found herself embroiled in a semantic mix-up that was both amusing and revealing. Taking inspiration from one of my articles, this visionary author mixed up &#8216;snow globe&#8217;, an expression used by one of my expert interviewees as a metaphor, and &#8216;snowball effect&#8217;. Thank God, she inserted a link to Visionary Marketing so that I could correct that fatal mistake. Far from being trivial, this anecdote raises a few fundamental questions. Who is writing? For whom? How? And for what purpose? In fact, it even poses bigger questions such as “what is humans’ place in society, and what sort of society do we want for our children and children&#8217;s children?” AI Information Overload  Content about generative AI is so ubiquitous that we have gone past information overload. AI content analysts are skirmishing via X (formerly Twitter) and LinkedIn posts, mainly on the technical front (this AI is better than that one), creativity (AI produces interesting ideas or rather, is dull and inferior to humans), and usage (“download my ultimate prompting guide!”). Yet all these debates (and sadly others that are less prevalent, like the poorly documented issue of energy consumption) fail to address other key questions: who are we creating for, why, and for whom do we work — or more broadly, what kind of society do we want in the future?  Generative AI at the Heart of the World’s Issues  AI, and in particular generative AI, have generated most of the noise on social media, blogs, newsletters, and chat around the pub. Traditional economy seems to be ignoring the phenomenon or treating it as incidental — a recurring habit when it comes to digital innovations, but online debates live on unabated.   Whether and how we should use generative artificial intelligence is now a central question in our modern societies, and that’s understandable. Machines have been able to play around with text since the 1950s, but computing power and large-scale training on such a vast and decent dataset — despite criticisms — have never been so strong. In recent weeks, engineers in London have even shown how two AI bots can talk to one another. Even if it’s only a demo, we’ve known since the early 2000s that machines can buy and sell stock (algorithmic trading roughly amounts to 60-75% of total trading in the most developed markets, and this was already true back in 2006 when I worked in that field). So, why shouldn’t an AI known as “agentic” buy train tickets?  Hence these legitimate questions.  A machine capable of writing “like” humans? The fact that a programme — literally a “machine” in the sense of a computer — is capable of writing like humans, or nearly, is disconcerting.  *[Machine] A mechanically, electrically, or electronically operated device for performing a task first entry from Merriam-Webster What’s even more unsettling is that humans often write more poorly than machines. This is what Loubna Ben Allal, a researcher at Huggingface and an expert in training generative AIs, describes in a video on the underscore channel, which is worth watching. She explains how content is filtered during training sequences and, surprise, surprise, she says that good Ai-generated content is often better than bad human content. Sadly, poor human content is everywhere.  Note that there are also texts, 100% AI-generated, aimed at proving that Loubna is right.  A text designed to show that separating the wheat from the chaff in content creation is a non-issue. Unfortunately, it was written by an LLM. Language, an operating system?!  If these mock texts are so disconcerting, it’s because language and the written word are indeed some of the fundamental characteristics of the human species.  In the beginning was the word. Language is the operating system of human culture. Yuval Harari — NYT March 2023 Yuval Harari, with a kind of reverse anthropomorphic twist, even calls it the “operating system of human culture”. Despite this idiosyncrasy, Harari is zeroing in on the real issue. The real core problem isn’t technical, but deeply philosophical, especially when the most famous generative AI tools are led by a maverick who’s trying all he can to put us in a Spike Jonze film. Ultimately, philosophy could or should redefine how AI is trained, explain Michael Schrage and David Kiron of MIT Sloan Management Review. The Real Problem With So-called Generative Tools The real problem with these generative tools isn’t technical, nor is it about creativity or even how well one uses the tool. It’s more fundamental, relating to the very essence of work and, more broadly, of human societies. Whatever human shortcomings and flaws there may be, and they are indeed numerous.  This is all the more important, given discussions about new tools such as Manus, which promise even more autonomous intelligence capable of “agenticity”, a direction that appears to be a goal for many of the creators of these programmes.    Generative AI is going to vanish? Really… There’s no point in playing down generative AI, as I saw here and there, by predicting their demise (you don’t just eliminate tools that the whole world has made their own, no matter how imperfect), nor in overestimating their potential (there are simply too many tools and possible uses).  Denying how astonishing these tools are is pointless. Likewise, describing LLMs as “stochastic parrots”, is no longer relevant. It used to be apt, barely two years ago. Yet, that’s no longer the case. Safety nets exist, the biggest pitfalls (such as asking ChatGPT to prove that the Earth is flat) as former Apple Siri cofounder Luc Julia claimed recently in a Swiss daily are old hat. The right way forward is hybrid systems combining the power of LLMs with more conventional computing. It&#8217;s a matter of time before this merger is done and it might not even take too long. Whoever has witnessed the development of IT and the Web over the past 40 years knows it takes time to innovate. Time is of the essence. Hence, even though the results we get today are still often disappointing, patchy, or downright wrong, GenAI models of 2025 hallucinate far less than they used to, provided you pay and pick your model carefully. You may check for yourself with Perplexity.ai, which will answer your question on this subject while delivering links (sometimes off-target, so you’ll still have to cross-check that information).  In short, four breakthroughs occurred from 2024 to 2025 in this field:  Reduced error rates from 1 to 3% thanks to techniques like Retrieval-Augmented Generation (RAG), drawing on existing documents.  Model improvements including the inevitable OpenAI, with its GPT-4.5 model, and others (I particularly recommend Claude.ai). Innovative methods like “deep research” or “chain of thought”, often flawed and slow, but give them time and they will improve dramatically.  Checks and adjustments: Tools like “Automated Reasoning Checks” introduced by AWS have been designed to detect and correct hallucinations before production use. Still, hallucinations remain common and won’t vanish soon. Again, it will take time before all control mechanisms are in place. Chain-of-thought is one example: it&#8217;s still a bit awkward, but it gives a flavour of future possibilities. That said, even if I’m not a big fan of AGI (see the following article), generative AI challenges human skills and abilities and as a consequence of that, our very place within society.  Three directions for deeper exploration Essentially, there are three areas that need to be investigated. First, our capacity to be truly creative. Second, AI’s impact on our cognitive and intellectual abilities and finally, there’s the question of usage. 1. Let’s start with creativity Obviously, one could wonder whether GenAI is creative or not. But above all, this very question challenges us, humans. Thus, the real question should read: are humans any more creative than GenAI? The answer isn’t straightforward, even if that may come as a surprise. One could argue that GenAI texts are good or bad, depending on one&#8217;s point of view. Yet, one shouldn&#8217;t discount that texts produced by humans aren&#8217;t always better. And that’s what’s disturbing. As we mentioned above, Loubna Ben Allal calls into question the notion that “human = good, synthetic = bad”.  The same applies to creativity. Alan Turing, in his 1950 piece Computing Machinery and Intelligence, had already invalidated a number of objections to the idea that a machine could be innovative. One of these objections claimed: “A machine can’t create.” Creativity is also, and above all, about combinations, de-combinations and recombination. A bit like a puzzle if you wish. One cherry picks from others&#8217;, or even one&#8217;s work, sometimes unconsciously, and recombine from this to build a new story, a new blog, a new project. Even artists aren’t necessarily all that &#8216;creative&#8217; in the sense of making something new entirely from scratch. They often rely on self-references. Tinguely with his zanyish machines aka antimuseums, Monet and his views of Rouen and his infinite variations on water lilies, Soulages with his black paintings, Rothko with his ubiquitous RED. Series are an integral part of Art, and one of the main creative mechanisms.  Jonathan Gibbs in Randall even states that Young British Artists, as all artists, can at best come up with four genuinely original ideas in their entire career, the ones we’ll remember them for.  ‘The way it works is that you’re only going to be remembered for four things.’ Gibbs, Jonathan. Randall or The Painted Grape And Gibbs is right. If artists give in to reproducing their own ideas, that’s also because it’s what people are asking for. That’s why, for instance, minimal music these days — once dubbed repetitive and lately rebranded &#8216;neoclassical&#8217; (Max Richter, Nils Frahm, Nicklas Paschburg, GrandBrothers…) — is so successful. It’s principally because it&#8217;s based on a never-ending repetition of fairly similar musical patterns. And I won’t even mention popular — as in &#8216;pop&#8217; inclusive of jazz — music, which is even more standardised (check rhythm changes if you don&#8217;t believe me).  Thus, the question of whether machines are more or less creative than humans is anything but trivial. 2. Is AI making us stupid? The next question is whether we end up being stupid from the misuse of these thinking machines (as one of my friends put it to me, &#8220;These tools are extremely addictive&#8221;). This question echoes what Nicholas Carr wrote a few years ago in The Atlantic: “Is Google Making Us Stupid”? In that piece**, he argued that even though he wasn’t raised in the digital age and learned to read “normally” in books, he ended up using search engines and found that they made him lazy, encouraging minimal effort rather than combing through documents for hours before forming an opinion. ** yet another AI-written piece, by the way. I only inserted the link out of mischief. Our dear readers will find the Atlantic link by themselves using old-fashioned search engines or Perplexity.ai.  With generative AI, all that Carr described is blown out of proportion. Perplexity.ai is the epitome of this issue. Instead of using search engines, one enters a prompt, and hey presto! Perplexity will gather the answers, summarise them and provide a list of links. The latter are not always relevant, but on average, they’re not that bad either. This process isn&#8217;t really less effective than wading through a so-called SERP (Search Engine Results Page) of questionable relevance or provenance, many of which results were written by &#8216;SEO experts&#8217; to trick the very same search engine (i.e. Google, see this post for details). Some years ago, those SEO experts had such low quality texts made by hand, often in low-wage countries, and now they create them almost entirely with LLMs (it’s estimated that about 19% of Google’s top 20 results are AI-generated). By the way, those people in low-wage countries must have been made redundant but who cares about poor people struggling to make a living. This is a dog-eat-dog kind of world, is it not? As to the question of whether generative AI is making us stupid, it’s a bit disingenuous, just as it was for Google. But what’s certain is it could be making us lazy (again, just like Google, especially since they introduced position zero).  Getting direct answers to our questions means we lose the habit of digging for them ourselves, and above all, we lose your critical thinking abilities. But it’s not that simple.  Concluding that AI alone is responsible for dumbing down the world — assuming that’s even happening — would be going a bit far. Intellectual laziness and lack of critical thinking aren’t new, and if you want to see evidence of that, I recommend you browse the site of the Reboot Foundation. 3. Prompting wizards Our third angle is usage quality of AI tools. Is it a real problem? There’s indeed a misconception about the usage of these tools by the population. Their usage is certainly widespread and it happened in a flash. That&#8217;s for certain. Now, whether most users are wielding these tools properly is another kettle of fish. Believing we&#8217;ve all become prompt experts overnight is spurious. I’m not seeing that happening in the field.  For starters, we have a massive digital skills deficit. How can people who struggle to remember a password or sign a PDF form could instantly be able to use generative AI effectively? I see far too much straightforward copy-pasting in class and elsewhere. Also, few people, as I noticed in the course of my training sessions (thousands of people and students), are able to take the necessary step back to refine the content produced by these algorithms — even when encouraged to do so. However much I regret this isn&#8217;t relevant. That’s why Steve Yegge is right: generative AI won’t help beginners nor average employees become brilliant, but it will help experts get rid of them altogether. Getting started in business won’t be easy in the coming years. Moreover, the generative AI scene is so hectic and unstable that even experts are losing track of which model is most effective. Almost every day, there’s some headline-grabbing announcement overshadowing yesterday’s.  And the &#8216;experts&#8217; keep dishing out their analyses and forecasts. Some foresee the demise of generative AI (but that&#8217;s total nonsense), while others predict that GenAI will on the contrary be an all-out revolution (which is equally silly).  The technology digestion curve is our own special way of highlighting the hype surrounding innovations. The truth is, as we can see in the field, that we are in a learning curve, which isn’t too different from what we’ve been through with other digital innovations in the past. Caption: Kathy Sierra once put forward the notion of “feature-itis”, which was spot on. As a system grows more complex, Kathy Sierra showed, you end up losing your grip, and a user who once felt in full control of the tool finds he or she loses that control and is going backward dramatically. More recently, Maurizio Bisogni described the fluctuation of knowledge in ChatGPT over time in relation to what he calls the Dunning-Kruger effect, a psychological phenomenon identified by David Dunning and Justin Kruger in 1999 in their paper: “We lack competence and we don’t know it: how difficulties in recognising our own incompetence can lead us to overestimate our abilities.”  This shows we have a tendency to overrate our abilities when we have too little information. A warning we might well direct at many of the analysts clogging up our social timelines with their views on the subject. Conversely, the most expert people tend to underestimate their competence. This is something we also know as the &#8216;impostor syndrome&#8217;. Perhaps I suffer from the latter myself, because I hate the term &#8216;expert&#8217;. Even though I’ve been working in digital marketing for over 30 years, started my career in AI nearly 40 years ago, and have been documenting these topics for decades, I still feel I don’t know much. It seems only natural and necessary, given how volatile and complex this environment is. Yet I see too many experts, some of whom are even behind some of these discoveries, such as Jeffrey Hinton, one of the discoverers of neural networks and winner of a Nobel Prize, who understand nothing about generative artificial intelligence, despite the fact that it is based on these very neural networks. In a BBC video, Hinton looks at ChatGPT and concludes that these machines can reprogramme themselves. In the long term, this is undoubtedly true. But it is not yet the case. I&#8217;ll come back to that later. So we need to have a certain humility when it comes to these subjects. With all due respect for Hinton’s outstanding achievements in machine learning, he shows a nearly childlike ignorance when he claims neural networks can feel emotions. […] Emotions are so complex, a bridge between thought and will, a gateway to shared understanding between people and the world. By saying the machine can experience feelings, Hinton shows that he doesn’t understand what he’s built. Robert M. Burnside &#8211; Robo Robert on Substack (2024) I’m not writing this to diminish the great talents of the renowned British-Canadian scientist, Jeffrey Hinton, a Nobel Prize winner in the field of neural networks, but to illustrate how siloed these areas can be and how no one can honestly claim complete understanding of the field of AI, if there is any such thing. As for LinkedIn influencers&#8217; rush opinions… Truth be told, regarding usage, I don’t believe that a science of prompting — which I see more as a practice of common sense, trial and error — is essential. What I find vital, rather, is taking a step back, thinking carefully, applying reason, and sharpening one’s critical thinking skills.  Besides, returning to prompts, I had already guessed we’d see prompt generators appear. And here they are, because that kind of interface is cumbersome and awkward. Prompting is powerful, but lengthy and tedious, requiring voice dictation abilities (which most people don’t have) or quick and accurate touch-typing, which is basically only for those who learned on an uncompromising typewriter, and/or how to touch-type without looking at the keys, like yours truly.  To be honest, I create all my Midjourney prompts on Claude or ChatGPT because I find the exercise quite tedious and slow, and LLMs are best placed to tailor a prompt for another generative AI in the required style.  Some folks even bet that chatbots will chat to each other in a language only they can understand (see this video. Careful! It’s not a product but a demo made during a hackathon).  In short, usage doesn’t strike me as a major problem, even if most users’ results are far from great — even when guided.  So, what’s the problem with generative AI? Let’s rule out a couple of areas at once. Ecological issues to start with. Apart from a few ritualistic mentions and greenwashing initiatives, not much appears to be on the menu in that area. As someone who’s been strongly committed to environmental concerns for ages, I’m deeply saddened about that. I will have to bite the bullet, nobody cares about that. And the current T**mpmania isn&#8217;t going to help. Bubble threats are real too, as Ed Zitron keeps hammering. Yet, the history of innovation has always shown that when some tech stuff is needed and the whole world is using it, money will always be found and invested. When there&#8217;s a will&#8230; The Web’s “enshittification” Web rot is probably a good avenue for our quest. I predicted it as soon as generative AI first emerged and GPT-3 was launched in 2020. Back then, I forecasted during a Pushengage webinar that the Web would be flooded with SEO content no longer created by humans but by machines. The latter deliver both quantitatively and qualitatively better (according to the standards of these &#8216;SEO experts&#8217;) than the armies of content creators from low-wage countries paid to boost webpage rankings through back-linking.  Five years later — a lifetime in Internet terms — what do we see? As it happened link-building requests died out instantly in 2023 and were replaced by proposals for AI-generated content creation. I saw them crop up on Visionary Marketing immediately, and the change was savage. SEO content became more professionalised and multiplied at a frantic pace, as The Verge showed in its 2023 investigation of synthetic content farms.  The result today is conspicuous.  What was foreseeable has indeed happened. It took five years. So much for those who talk about an overnight revolution. Even for something as simple as replacing human content writers in low-wage countries with LLMs that churn out copy at high speed with a few basic instructions, it still took five years. As for the rest, we may have to wait a bit. By the way, we’ve laid off hundreds of impoverished people unless they’ve retrained for AI-based content, which is likely but not proven. That’s the genuine underlying problem. And it’s why I created humansubstance.com with some friends. A group of stubborn bloggers who decided to write with their hands and their brains, not with machines. Like this 4,000-plus-word article that I could very well have churned out in three seconds using ChatGPT — assuming ChatGPT can count words and by Jove it can&#8217;t. Because there’s the hitch: we do need artificial intelligence to take out the rubbish and count words, fix our grammar, punctuation, and spelling mistakes.  But we don&#8217;t need it to think in our place. And if the Web is rotting, or &#8216;enshittifying&#8217;, to borrow Cory Doctorow’s term, that doesn’t necessarily mean the end of real content marketing (genuine content, not SEO fodder). It may not happen on the Web and this is sad news for Sir Tim Berners-Lee. Quality content will always find a way to be shared. If not on the Web, then somewhere else. Perhaps my vision is somewhat naïve, but I’ll own that. I’m inclined to believe good things can still and always happen. Let&#8217;s assume I&#8217;m wrong; at least I will die happy.   What’s the point of generative AI if it doesn’t relieve us of chores? If Gemini can’t deduplicate data, what’s it for? Examples posted by a LinkedIn user I also see plenty of players, analysts, and professionals around me who think, search, dig deep, and document beyond the surface. They don’t buy into the big headlines from generative AI evangelists, who increasingly come across like transhumanists, to quote Jean-Gabriel Ganascia.  Chores to AI Make no mistake, I have nothing against generative AI. I just want it to take out the bins instead of trying to think in my place. And when I see some of the results from these tools, I’m not convinced the game is over yet.  If ChatGPT can’t read an Apple Pages file and orders me around to switch to Word format, what’s the point? I use them a lot for preparing my lectures (most of which aimed at training students to keep enough distance to interpret these tools’ results rationally rather than emotionally), to summarise my articles for my students’ presentations.  But I’m always the one doing the thinking, and all I want from these tools is to take out the bins and turn my most relevant punchlines into PowerPoint.  Why? Because copying out your own words into PowerPoint is basically a chore. And that’s why, for my keynotes I refrain from using slides. Those addicted to PowerPoint can still download the slides from my blog if they wish. Finally, at the heart of this debate about AI’s role in content marketing lies a big confusion about the automation of creative processes, which aren’t continuous. It’s an illusion to think you can simply press a button to get a result. Sure, you get some sort of result, but which one and what value does it have? For an SEO content producer (I can&#8217;t get down to call them &#8216;authors&#8217;, sorry) it’s probably a thousand times better and faster than what a human being could write. But automating such tasks for true authors, those who write with their brains and for their readers, not a Google bot, gives you the impression you’re saving time, whereas reality is often radically different. Randall Munroe illustrates this brilliantly in his schematic about coding. And it’s even more apt for content marketing.  And all that AI SEO copy for what outcome? More efficiency? Neil Patel shows otherwise in the following chart.  So, to wrap up this article, I urge you never to relinquish your capacity to ponder nor your critical thinking skills. Certainly, humans are prone to error. Sometimes they’re even worse than LLMs, as Kevin Roose demonstrated in the New York Times. And that’s the real tragedy.  Even if general artificial intelligence is probably an overstatement (we can’t define it anyway), insisting the opposite — that all humans are brilliant — is an even bigger mistake. But despite these flaws, it was us, humans, who built these machines. It’s our job to use them for the better, not for the worse. It’s up to you to do the thinking and let AI do the chores. That’s what ought to be. The post Chores to AI, Thinking to Humans appeared first on Marketing and Innovation.

Isn&#8217;t the notion of &#8220;disruption &#8220;, aka disruptive innovation, used and abused by analysts and technology experts? And by dint of abuse, aren&#8217;t we in the process of deluding ourselves? At a time when some are fretting about the volatility of the business generated by &#8216;unicorns&#8217; or even centaurs, it is perhaps worth asking whether we have not entered an innovation bubble, yet accentuated by that of generative AI, marked by the correction of technology values and a return to more traditional values. Yet it may be too early to find out about the reality of such disruptive innovations. Here are my thoughts about the subject with a few references to sources and books I found interesting. Disruption: Is Disruptive Innovation Overhyped? I find reviews of supposed disruptive innovations in the media, and especially social media, somewhat lacking consistency. One minute everyone is vowing that a revolution has happened. The next that a bubble is about to burst, yet no one is able to predict the future properly. Thus, is disruptive innovation real or a pie in the sky, or does it emerge over time? Is, by and large, IT causing disruption in our lives, or are there more important thing on earth? In short, how can we ensure that our vision for innovation is accurate? &#8211; image produced with Midjourney The so-called GenAI revolution While some have been claiming that we are living in a bubble of innovation (here, here and here for instance and here and here with AI), it has to be said that not everyone always agrees. Especially with the advent of the so-called GenAI revolution. I&#8217;ve been asked whether electric cars were a disruptive innovation. For those of you who don&#8217;t know the history of innovation, let me introduce you to the &#8220;Jamais Contente (&#8220;Forever unsatisfied&#8221; literally), which broke the 100kph speed record near the Fulmen factories in Saint-Germain-en-Laye, France in &#8230; 1899 [photo in public domain]As I felt like tackling the topic of disruptive innovation, I thought it would be interesting to revisit an article by Joanne Jacobs from a few years ago about this subject: &#8216;Are we in a disruptive bubble?&#8216;. In this piece, she explains what role disruptive innovation is playing in contemporary markets. She argues that disruption is not just a fad, but something more profound. Forget all about unicorns, here come the centaurs! Bessemer Venture Partners &#8211; State of the Cloud 2022The hype surrounding disruptive innovation is overwhelming. Here is what I found here and there: Disruptive innovation is deemed to impact businesses and employment . with all-out automation a major source of job destruction; Integration of productive innovation is supposed to have enabled some companies to reinvent themselves ; Organisations are said to be reshaped through the introduction of collaborative networked business approaches ; Profound changes in traditional markets (as for banks for example). A spanner in the works Despite this, and the spectacular performance of some companies that have established themselves in just a few years to the point of throwing a spanner in the works of well established markets and provoking defensive reactions, some observers maintain that we are facing an innovation bubble. Disruption: bubble or not bubble? &#8211; image Midjourney And these same observers point out that the expectations they have of these disruptive innovations are not in proportion to what they could deliver. And when expectations exceed what innovation can deliver, disappointment occurs. As described by Gartner in its &#8220;Hype Cycle&#8221; with what the US analyst group calls &#8220;The trough of disillusionment&#8220;. Here the Gartner Hype Cycle of 2008 technologies And the 2021 update. In the meantime many of the key groundbreaking technological innovations of the 2000s have fallen by the wayside, to be replaced by other, more trendy ones. With AI at the height of the current craze and without Web 3 (even Gartner have given up on it even though they wrote a favourable report on it). Disruption strategy: not just a buzzword &#8220;The reality is that business disruption is not a fad. It is not a set of buzzwords you need to use in planning meetings, and it is not a way of positioning a brand in the marketplace. ,&#8221; explains Joanne Jacobs. In her view, the only real break is the one that results from the convergence of three elements: Emerging technologies; Changing customer needs; The availability of resources. It is the combination of these three ingredients that she believes makes disruption a reality or a fiction. Disruption is the result of risk-taking. Often, it means that you should be making the most of a legal loophole. We could, however, add a few important ingredients to this recipe: Significant market share to the point where it weighs on the incumbent players (in mass market, for a market share to be stable, the bar is set at 20%, with dominant products often achieving 70 or 80% in mature markets); A valuation that is not based solely on market cap (by its very nature volatile and speculative, with recent unicorns being punished for being valued at up to 50 times their sales, which is unreasonable); The ability to make a lasting mark on a market by changing practices, evolving buying patterns &#8211; even society and lifestyles. And lastly, the ability to survive market re-regulation, as was the case for ecommerce with Amazon. The Seattle firm, for example, sold VAT-free until 1996 in the UK (a little later in Europe). Re-regulation did not kill Amazon, it continued to thrive for many years. A second tier re-regulation is happening now with the implementation of VAT for all vendors on marketplaces. Let’s challenge the challenge  In recent years, we have seen the emergence of new entrants in markets that were thought to be stable and saturated: &#8220;AirBnB entered the top ranks in terms of hotel market capitalisation and Uber represented the world&#8217;s fastest-growing car rental with driver service,&#8221; Joanne wrote in 2015. These businesses were seen as a real challenge to such established markets.  But what is the situation today? There was a clear wake up call in the high-tech industry. No more valuations without sound business results underpinning. Well, maybe. Here&#8217;s to disruption &#8211; the 1880s according to Vaclav Smil &#8211; Numbers don&#8217;t lie &#8211; p 98. So the question arises, is disruptive innovation overhyped? Vaclav Smil, the famous author of &#8220;Numbers Don&#8217;t Lie&#8221; answers bluntly that we are mistaken. According to him, the most innovative period in human history was the&#8230; 1880s ![/caption] According to the worshippers of the e-world, the late 20th century and the two opening decades of the 21st century brought us an unprecedented number of profound inventions. But that is a categorical misunderstanding, as most recent advances have been variations on two older fundamental discoveries: microprocessors [&#8230;] and exploiting radio waves, part of the electromagnetic spectrum. Smil, Vaclav. Numbers Don&#8217;t Lie (p. 97) A growing bubble of innovation? According to Smil, and others, we may be living in a context that reflects a flowering of innovations, an accumulation of gadgets that are more or less important or distracting, but in which we are unaware of the importance of the underlying innovations. The mobile: cause for wonder or plain incremental innovation? To mimic smil’s deliberately cursory demonstration, we marvel at our little computer phones, but fail to take into account the importance of the work of Nikola Tesla to whom we owe the industrialisation of alternating current. Tesla died in debt and lonely, but without him, none of these gadgets would exist! Amazing innovations, real breakthroughs? Many of the innovations we use are undoubtedly incredible &#8211; and I never cease to marvel at those communication tools we wield. Yet, does that mean that all these contraptions are truly disruptive? Reading this article from 2015 today as the clouds gather over tech stocks and others are towering up is interesting. Where do we stand on &#8220;disruption&#8221; at a time when, as Smil has it, there are many more important things than that in the economy. A disruption bubble or a hyperbole of disruptive innovation? In 2015, &#8220;[&#8230;]A whole range of disruptor companies from DropBox and SurveyMonkey, to the secretive Palantir Technologies and audacious SpaceX&#8221;, were redefining the way organisations were communicating, researching and developing products, Joanne explained. Even then (2015 seems a long time ago) she was rejecting the idea that we were living in an &#8220;innovation bubble&#8221;. At the very least, she recognised &#8220;a bubble of disruption hyperbole&#8221;. 30 years after the development of the commercial Web, we have the necessary hindsight to see what has really changed under the impetus of these &#8216;disruptive&#8217; companies. And I have miwed feeling about the result. Both naysayers and proponents of disruptive innovation mays disagree with me, though. Regardless, I have looked at both sides of the equation, and the pros and cons of that so-called disruption. Signs of evidence of disruption According to Eric Van Susteren (Momentive&#8217;s head of Brand Content strategy), there are 5 pieces of evidence that this disruption exists: The Great Resignation could be the proof of a real and profound change, even if it is not solely driven by technology; This very big resignation have brought to the forefront employees&#8217; expectations in the areas of diversity, equal opportunities and inclusion; A majority of new IT purchases were sourced from new suppliers; Nearly half, of (US) consumers say they are buying more online today, even though the ecommerce boom appears to be over including payment innovations that have fizzled out; The boom in the use of digital continues unabated with consumers ever more inclined to use online services. Better still, McKinsey proved to us back in 2019 that the pace of disruption was accelerating in its report &#8220;navigating in a world of disruption&#8220;: Disruption is accelerating according to McKinsey &#8211; Navigating a world of disruption &#8211; a 2019 report But what will be left of all this in a few years&#8217; time? Now that Covid has been forgotten, what has become of the &#8216;great resignation&#8217;? Will the wave of wealthy urbanites fleeing to the country last? We saw flock to the Pyrenees a few years ago and now they are all returning home to make a living. Nonetheless, only time will tell. WFH policies And how will the exiles in the far-flung suburbs survive such long commutes? Already, businesses are massively scaling down WFH policies. I&#8217;ve be a fan of remote working for 35 years, but I&#8217;m not sure it is made for everyone in the same way. And what about the AI boom, noted by McKinsey in its report. In short, how can we tell the difference between disruption and non-disruption? How can we avoid, to use Joanne&#8217;s expression, this &#8220;hyperbole of disruption&#8221;? NFT, Web3 and other pipe dreams Some readers may think that &#8220;all this doesn&#8217;t matter, it&#8217;s all theory, what matters is what happens in the field&#8221;. There is truth in that. It doesn&#8217;t matter that disruptive innovation is all the rage, the proof will be in the bacon and even in eating the bacon. Let&#8217;s mention Web 3, for instance. Pundits are telling us that the blockchain is nothing but a lie and, ultimately, nothing more than a glorified spreadsheet. It&#8217;s [&#8230;] not surprising that whenever &#8220;blockchain&#8221; has been experimented with in a traditional setting, it has either been thrown in the bin or turned into a private permissioned database that is nothing more than an Excel spreadsheet or a misleadingly named database Nouriel Roubini, the big lie of blockchain &#8211; 2018 Others, no less knowledgeable, tell us that this is a major change and a fundamental breakthrough. Web2 vs Web3 Thealien.design tutorial Web3 is not vapourware, it&#8217;s a vision that encompasses different principles based on practices and technologies that enable new applications. Frédéric Cavazza &#8211; Sysk &#8211; 2022 &#8211; white paper Gartner, as far as they are concerned, issued a midlle-of-the-road statement about Web3. Admittedly, blockchain is only one of the components of Web3, but not the least. So who should we believe? How can a layman navigate this world of &#8220;disruption&#8221;, to paraphrase MacKinsey? What if we were living in a workd which has become too complicated for us to understand? Even experts are losing touch I&#8217;ve wondered a few times lately whether the world has really become complicated or whether it&#8217;s engineers and marketers that are messing things about so much that noone can understand anything anymore? Unless it&#8217;s Google — and AI soon — which made us stupid? It reminded me of a lecture by the late Bernard Stiegler, who sadly passed away in 2020. His in-depth thoughts on the proletarianisation of our hyper connected society [see his presentation in French] hit the nail on the head (his diagnosis was more impressive than the solution he suggested). The proletarisation of the world according to Bernard Stiegler (visual taken from one of his presentations) We have lost the ability to understand the world around us And once again, it&#8217;s Vaclav Smil who sets the record straight. The Manitoba hermit joins the French philosopher in a slightly different way. Experts and facemasks He begins by pointing out the extent to which &#8216;experts&#8217;, during the outbreak of Covid, by dint of hyperspecialisation, had been unable to help us deal with the pandemic. According to the Canadian thinker, there is an underlying explanation for the fact that it has taken so long for so clever experts to agree on something as simple as wearing a facemask. [&#8230;] explanations of this comprehension deficit go beyond the fact that the sweep of our knowledge encourages specialization, whose obverse is an increasingly shallow understanding—even ignorance—of the basics. [&#8230;] and unlike in the industrializing cities of the 19th and early 20th centuries, jobs in modern urban areas are largely in services. Most modern urbanites are thus disconnected not only from the ways we produce our food but also from the ways we build our machines and devices. Smil, Vaclav. How the World Really Works (p. 3). Penguin Books Ltd. Kindle Edition Smil&#8217;s sentence rings true, but not only to describe our ignorance of the world in its main components. Proletarianisation also strikes experts in innovation, the Web, digital and the economy, and in the analysis of so-called &#8216;disruptive&#8217; technologies. I don&#8217;t know whether Roubini is right or whether, on the contrary, the aficionados of Web3 or AI (or of the next high-tech fad) will win the battle. They probably don&#8217;t even have a clue themselves. And innovation takes such a long time that it&#8217;s possible that we&#8217;ll have to wait a decade or two before we know the whole story. Internet-banking.com, 30 years on And so it is with &#8216;disruptive innovation&#8217;, and I used inverted commas quite intentionally. If someone had asked me &#8211; back when I created internet-banking.com &#8211; whether the Internet would &#8216;disrupt&#8217; the banking sector, I would probably have said yes. And there&#8217;s a good reason for this: this selfulfilling prophecy was giving me job. Truth be told, I sincerely thought so. Nearly 30 years on, my feelings are rather mixed. Almost all the neo-banks have been taken over by big financial institutions, and even if new ones have been created, we&#8217;re all keeping our fingers crossed for feare they crash before we&#8217;ve had time to withdraw our money (it&#8217;s already happend twice with me, once with ING and once with banque directe. Now my business is with Shine but after a year, Socgen got rid of them and this is a bad omen) . Disruption or continuous improvement? Not so easy Despite the above, Chris Skinner was predicting bank closures in droves nearly 10 years ago. And reality has finally caught up with many of today&#8217;s banking institutions, even in more traditional and stable markets like France. &#8216;Disruption&#8217; did happen, but not in the way we expected originally. If there has been disruption in the banking sector, and this is only the beginning, it is because of the impact of automation, a universal movement that affects all professions and that is linked to a natural trend in our societies since the miraculous 1880s, to put it in Smil&#8217;s words. Wherever a machine can do the job, it will. After all, human beings cost money, and they always complain that working is hard. The answer to the question &#8211; is this technology disruptive or not &#8211; is not trivial, and is above all a matter of judgement and time. The only thing that is certain is that the proletarianisation (in the meaning that we are losing the ability to understand how things work) that we are experiencing is not going to help us decode the technological innovations of the years to come. And that includes Generative AI in the first place. The post Is disruptive innovation overhyped? appeared first on Marketing and Innovation.

Growth hacking can often be perceived as toxic, but you can sit back and relax, it is possible to practise ethical growth hacking but it requires time and energy, growth hacking expert Frederic Canevet explained to Visionary Marketing. In a nutshell, it may be a little harder than you think, but it is well worth the effort. Fred, who ate his own dogfood to sell his bestseller on the subject, tells us everything we should know about whte hat growth hacking. Ethical Growth Hacking Is Not an Oxymoron White hat, back hat? Growth hacking often suffers a dire reputation fuelled by unscrupulous individuals, some of whom have built real fortunes on questionable approaches. But ethical growth hacking is possible, explains Fred Canevet &#8211; image created with Midjourney on our personalised mode. Could you mention historical examples of growth hacking? Frédéric Canevet: There are two tale-telling cases that illustrate the controversial practices of growth hacking perfectly: In America, Airbnb got its start by exploiting data from Craigslist. The company developed an automated system to extract property listings and contact owners, offering them the chance to earn $500 a week by listing their accommodation on Airbnb. In France, the founder of Telecom operator Free Mobile Xavier Niel, back in the days of the Minitel, created a tool to send automated mass messages to Minitel users. Lonely hearts messaging services on the Minitel being most profitable at that time, he launched a competitor to a leading dating service and diverted their traffic through targeted messages. Although this practice earned him legal action and a lost court case, the profits generated helped him build his initial fortune, notably through a network of sex shops linked to this Minitel business. Can Growth Hacking Be ethical and responsible, though? FC: Yes, it&#8217;s feasible but it requires time and effort. Ethical growth hacking: white hat ends up paying better than black hat &#8211; image made with Midjourney on our custom mode. How should our vision of growth hacking evolve overtime? FC: Our approach has to evolve considerably in the face of today&#8217;s economic challenges. In a tense economic climate, we can no longer afford traditional marketing with its long-term plans. This is precisely what inspired growth hacking in Silicon Valley, where startups had to, as the time-honoured slogan went, &#8220;live or die&#8220;. In a world tending towards the end of consumerism, at least in Europe, the challenge is to do more with less. There are three levels of growth hacking. &#8220;White hat&#8221; represents legal and ethical practices, similar to the &#8220;Fosbury flop&#8221; in athletics &#8211; a revolutionary innovation, but one that abides to the rules. This approach is based on business cycle analysis using the AARRR method: Acquisition, Activation, Retention, Recommendation and Revenue. &#8220;Grey hat&#8221; is sitting in the middle. For example, automation on LinkedIn, although prohibited by the platform, is still widely practised. I have personally experienced the risks of resorting to this approach when I was suspended for managing two separate profiles. Finally, &#8220;Black hat&#8221; encompasses strictly prohibited practices: i.e. creating fake accounts, identity theft, or unauthorised recovery of personal data. These methods may seem tempting in the short term, but prove disastrous for a company&#8217;s reputation and long-term survival. How can we guarantee efficiency while remaining ethical? FC: Sending mass unsolicited messages in LinkedIn serves no purpose. Instead, effectiveness lies in forging true connections. The strategy I adopt lies in the daily publication of high added value content, demonstrating real expertise. It&#8217;s not an aggressive sales approach, but rather inbound marketing based on trust. In fact, email spam is a no no. You send 10,000 emails and what you get is a 0.5% open rate and a slightly lower click rate. All in all, as you sent tens of thousands of messages, you may get some sort of result. But very soon, all this is bound to dwindle. Not to mention how damaging all this could be to your reputation. As for &#8216;black hat&#8217;, I absolutely forbid it. In particular, fake accounts attached to the name of a company, solely for the purpose of recovering data from lists of people and companies that follow the page of the target company. This is illegal, because it&#8217;s identity theft. The same applies to those individuals who seek to recover the email addresses or telephone numbers of people with whom they have no relationship. I refuse to do that, especially as it often involves personal data. Facts and Figures About Ethical Growth Hacking FC. To illustrate the effectiveness of the approach I recommend, let&#8217;s quote some figures from the company I work for, Eloquant: over 20% of the 1,200 people who signed up for our interviews with customer relationship experts this year came from LinkedIn, out of an industry of around 15,000 professionals. Our aim is to unite this community, establish our legitimacy, and then convert the members of this community into visitors of our various events such as webinars or our &#8220;All for Customers&#8221; trade show in Paris. The omnichannel approach is becoming essential as traditional channels become saturated. The numbers are not adding up: the rate of participation in webinars has fallen from 35% 3-4 years ago to around 25% today, 30% at the most. The rate of viewing replays has also fallen, from 15% to 12%. These figures show that it is no longer viable to rely on a single channel. Omnichannel is of the essence, and face-to-face meetings especially, that are more effective than ever. How important is personalisation in your approach? FC. Personalisation is vital. For birthday wishes, we take a two-step approach: an initial message automated by my assistant, followed by personal interaction on my part. And I send the messages one by one. AI won&#8217;t replace humans, but professionals who master AI will outperform those who don&#8217;t. Our experience with Smart Tribune illustrates this principle: during a joint event, we decided to pull together and appeal to our respective networks and approach each potential participant individually. Success depended on pre-existing relationships and established personal links. This then led to a white paper written jointly with Apizee and Smart Tribune [note: in French only], based on an OpinionWay survey of 1,000 interviewees. Our partners used AI to kickstart the writing of this project. While AI was impressive at the start, it was quite obvious after a while that all this was more artificial than intelligent: the formatting was bland, transitions were artificial. All that was typical ChatGPT gibberish. We had to substantially correct all these initial sections. AI remains a valuable tool, particularly for copywriting. I use a custom GPT to generate drafts of posts about my events. The result, while not exceptional, provides us with a straw man, which can then be adapted and personalised. How is the digital landscape changing with these new AI practices? FC. There has been a significant drop in SEO traffic on Google these past few months. Well-established blogs have been massively hit by the arrival of AI and the automatic generation of content. As Google is struggling to distinguish authentic content, it then started to favour more specialised and industry-specific sites. I&#8217;ve noticed that many content creators and bloggers I know have given up or scaled down their online writing. Personally, while blogging used to be my number one priority, it has now slipped into second place. I now prefer to concentrate on LinkedIn. &nbsp; The post Ethical growth hacking is not an oxymoron appeared first on Marketing and Innovation.

AI is redefining retail for good, bringing in the kind of automation and professionalism once implemented in the manufacturing industry. In this case, it’s mostly revolving around data-driven marketing decisions and in-store retail media capabilities. As shown by Axians, a VINCI group company, AI isn’t a mere toy for undergraduate students who are failing their tests and need better inspiration. It’s a robust, state of the art high-tech engine for growth and better in-store management. Yet, as often with technology, there are two sides of the same coin. The other one is more ominous, though, depicting a future of retail where layoffs will continue to rise, mostly for those retailers who missed that boat of AI-driven customisation. Here is the account of our discussion with Hugo Rocha Gonçalves, Axians&#8217; head of Smart RetAIl, at Tech for Retail 2024.  AI in retail: shrinking queuing times today, headcount tomorrow Zooming in on AI in retail with Axians&#8217;s Hugo Gonçalves at Tech for Retail 2024 You’re in charge of the smart retail solution at Axians. What is it? Hugo Gonçalves. We developed the Smart retAIl concept to address the main challenges that the retail industry is facing today. There is a strong need to better understand in-store consumer behaviour, profile and shopping habits. We provide this knowledge to improve store efficiency, and to enable data-driven decision-making. Can you describe the process of Smart RetAIl? H.G. We are using AI and computer vision to accomplish this.  The first step is to understand how the stores are organised, what the shop floor looks like, and also how we can capture this data anonymously — for obvious GDPR compliance reasons — to fuel a data-driven decision process.  After capturing this anonymised data through computer vision, there are a couple of things we need to understand. Such as footfall, who are the buyers, when they are buying, and their paths through the store. We need to map, with the help of AI, the hot and cold zones within the store. Within these zones, we can understand if people are proper shoppers or if they are merely passers-by, and how much time they spend doing their purchases. In a sense, this is some sort of heat map within the store H.G. This is precisely what it is. And with this heat map, we can also understand what products people are looking at, how much time they spend. With AI we are taking this to a new level. This new level includes product tasting and testing. Two good examples are chocolate tasting, where we need to understand through computer vision when a customer is tasting something, which is very important in chocolate stores, and perfume stores. With this technology we can detect if the customer is testing the perfume and then understand if he or she will buy it or not afterwards. AI in retail : Axians had set up a heat map showing how their system was monitoring footfall in front of their Tech for Retail booth This means you are automating the work of market researchers who used to observe in-store consumer behaviour H.G. Indeed. It used to be very tedious work to have someone watching hours and hours of video, trying to understand customer behaviour, customisation, and buying habits. Now we have AI that can process 24 hours of video, covering all the opening hours of a given store. We can process all this data and obtain valuable insights as well as data enriched by AI and computer vision. So you are capturing a flow of images through in-store cameras, how is it working? H.G. This entire process demonstrates the beauty of machine learning and AI. No need to resort to supplementary intrusive devices in the stores. We are using existing in-store CCTV cameras. We subsequently apply AI image processing, frame by frame, on the existing footage. The data is recognised and categorised by the AI automatically. The resulting data provides a lot of KPIs like passerby/buyer qualification, hot and cold zones identification, as I said already. We’re also interfacing with other information systems such as CRM, ERP, or point of sale systems. Doing so we are able to match our data with the sales data. How do you adjust your setup for sales optimisation vs shoplifting prevention? H.G. Indeed, the technology is also helping us in that direction. All the innovation and sophistication lie in the AI processing the image. With the evolution we’ve experienced in computer vision, we no longer need specific hardware to do this. We simply need AI to help us with good machine learning and AI models to process it. What kind of AI are we talking about here, certainly it’s not ChatGPT! H.G. This system has demanded a great deal of knowledge and experience. We have a large group of data scientists at Axians. It’s also important to mention that this solution originated from an AI program launched by VINCI Group called the Leonard program (editor’s note: named after Leonardo da Vinci). This program focuses on solving real challenges we face as citizens in our daily interactions. It’s aimed at using AI to solve real challenges. One of these challenges involves using human expertise and knowledge in conjunction with AI. Her me we are talking of a different kind of AI (coupled with computer vision), not generative AI.  Hence it’s either machine learning or deep learning. What does the training process involve? H.G. Typically, we have a learning curve for these types of systems. We train the model using what we call manual labelling. Manual labelling helps the model understand what a person is. There are already modules that assist us. We don’t need to start from scratch. We have existing models, open models that identify a human in a shop and their interactions. On top of this, we use not only our retail clients for assistance (they help us with the training of the model), but also to understand and label the data correctly. It’s important to note that ours is not an unsupervised process. Here we are talking of supervised AI image processing. Supervised learning ensures the correct labelling of data and effective leverage of AI capabilities. What’s sort of work was involved prior to launch?  H.G. Beforehand a lot of preparatory work was required. We have extensive experience developing AI solutions, especially in computer vision, data processing, AI processing, and data quality. This represents at least two or three years of intensive work, collaborating, testing and trying to understand how to move forward. Whenever the packaged solution doesn’t suffice, we propose POCs to our clients. Such POCs help us reduce overhead related to testing. For example, we are currently testing queue times AI management. From experience, we’ve found that normally when customers are buying something, they won’t wait more than 10 minutes. If the wait exceeds 10 minutes, they’ll leave the queue and give up on their purchase. We’re addressing issues such as these by providing data driven insights. Can you share a real-life business case with our reader? H.G. We have launched a POC in Italy. We’re assisting a large retail client over there. This retailer had realised it was losing sales and that their conversion rate was decreasing because their staff wasn’t supporting their customers, even though that was part of their onboarding training.  The end gain was significant. They’ve reduced queue time by 50% and increased sales in some stores by 12 to 15% due to this implementation. It was sufficient to break even and they are now challenging us with new use cases, including some very complex AI problems. How long does it take to break even with that kind of solution? H.G. It depends greatly on the size of the stores. It’s not a one-size-fits-all solution, but we can say that recovering the cost of the investment in the platform typically takes between 6 to 12 months.  Any examples from Portugal? H.G. Regarding queuing times, we have another example in Portugal involving high-tech retail solutions. The main issue was the identification of the most profitable areas within the store. When selling technology hardware like smartphones, etc., hot zones are of the utmost importance. They are areas  where consumers spend extra time, allowing retailers to sell media space to vendors. This what is known as in-store retail media. In this particular case in Portugal, we achieved great results with a retailer who started to monetise the hot zones in its stores. This wouldn’t have been possible with our platform. Now they know which areas provide more return on investment and can charge more for product placement in these zones. We’re still in the early stages with this client, a major retailer in Portugal. Already, the return in euros is between four and five figures per store. Can your solution help struggling retailers in the current economic environment? H.G. Absolutely. We’re living in a data-driven world. Decisions should all be made based on data. This platform provides extensive in-store data and enables many well-informed data-driven decisions. In the near future, retailers failing  to consider data-driven marketing and AI will have to layoff staff and make other last minute haphazard decisions. Our solution helps uncover KPIs and metrics that were previously hidden. Through data-driven approaches, we’re confident we can help reduce redundancies and facilitate better data-informed decisions. What will retail look like in five or ten years from now? With all these AI solutions, will it still be a labour-intensive business? H.G. It won’t be. There will be a major reconfiguration of stores. Luxury stores will continue to have staff assisting us with purchases. For everyday retail purchases, there will be a significant reduction in staff.  In the future, retail will no longer be a labour-intensive industry The future of retail will also be about extensive customisation. We’re already experiencing this level of customisation in streaming services that trace our personal and behavioural data very well. This means that each consumer will have its own bespoke catalogue tailored to his or her needs. To stay in business, retailers must possess in-depth knowledge of their customers. Moving forward, beyond this extensive level of customisation, a personalised care experience for each customer.  The post AI in retail: shrinking queuing times today, headcount tomorrow appeared first on Marketing and Innovation.

Data-Driven AI is the future of customer experience, François Ajenstat told us at a recent interview. François is Chief Product Officer at Amplitude, the company behind a digital analytics platform aimed at helping B2B and B2C businesses build better products, websites and ecommerce experiences through behavioural data. François stressed the significance of data-driven AI within analytics but also delivered a clear warning: Don&#8217;t fall in love with what you have built! Focus on delivering second-to-none customer experiences instead. He emphasised that implementing chatbots without purpose isn&#8217;t beneficial, noting that too often, in this new world, businesses rush to add chatbots but it doesn&#8217;t make &#8220;anybody happier. No. People are still frustrated.&#8221; Data-Driven AI Is the Future of Customer Experience Data-Driven AI Is the Future of Customer Experience — Image generated with Midjourney and our special personalised mode AI Integration and Implementation in analytics, what does it mean? François Ajenstat. While AI capabilities have existed for years through statistics and machine learning, generative AI has opened new possibilities. We&#8217;ve integrated this through &#8220;Ask Amplitude,&#8221; allowing natural language queries with visual responses. Thus, users can simply ask questions about their most engaged users and receive actionable insights. I could ask the system, “Who are my favourite readers, for instance?” Absolutely. And you’d get the answer in a flash and the system would suggest what actions you should take and how you should engage them. Alternatively, it could help you visualise the journey of those users. This is what you call the 3 key aspects of Data-driven AI Implementation F.A. Indeed, we focus on three different areas with a massive potential impact. Simplification first: removing complexity through natural language interfaces. We&#8217;ve had speech-to-text capabilities for years, but users often found this feature intimidating. There used to be a learning curve before you could use it properly. Now it’s a lot easier. You just ask a question in natural language and it brings the result for you automatically. Augmentation is the second area: it’s about enhancing human capabilities rather than replacing them. A great example of that might be if you&#8217;re analysing some data and you want to understand the outliers* or what the key drivers are. Help me understand the root cause of this problem. This is where you can unleash AI to really drill into the data on your behalf and come up with insights. So we&#8217;ve added those capabilities in our product. We&#8217;ve also added what we call a data assistant, which will tell you automatically where there are data quality issues or improvements. Last comes Automation: this is where you find workflows and you ask AI to execute tasks on your behalf. It could be about automatically engaging users. It could be around guiding those users by delivering the right content, images, text, based on given use cases. Enabling 24/7 execution of routine tasks while allowing marketers to focus on strategy. The key thing is to engage the user at every single touch point and use AI to make every interaction a little better so you can drive a better outcome. *Outliers (statistics): a data point on a graph or in a set of results that is very much bigger or smaller than the next nearest data point. The 3 key aspects of data-driven AI are simplification, augmentation and automation — visual produced with Midjourney Do you think that AI is made for beginners or super experts like Steve Yegge? F.A. Every time new technologies emerge, it causes fear, uncertainty, and doubt about the jobs that are going to be eliminated. Think of word processing. In the 50s and 60s, the only people who would type were office secretaries. That was a specialised job. When WordPerfect and Word came out to the market, that job got removed. But at the same time, it empowered millions and millions of other people to be able to perform new tasks by themselves. And that was extremely liberating. It doesn&#8217;t eliminate the fact that some people are good writers and some people are not. You still need the core skills to know how to write properly. And I think that in our jobs, whether you&#8217;re in marketing, engineering or product management, you still need to grasp the fundamentals to understand what is happening. But you can eliminate some of the more basic work and spend more time on the higher level. Yet, Focusing on the Higher Level Requires Skills F.A. Indeed, it does. Think of these new programming languages where you don&#8217;t have to learn all the basic hard-coded engineering. You are therefore facing a higher level of abstraction. The same goes with AI. It is merely providing a higher level of abstraction. It makes it possible for you to focus on building greater software versus knowing all the mechanics below it. Data-driven AI means you should be obsessed with user experience, not with AI — image generated with Midjourney. Will AI become a staple of user experience? F.A. AI will become a core capability in all software, driving faster innovation and creativity. The focus on user experience becomes even more critical as expectations rise. Success depends on delivering value to users, regardless of the interface or platform. User expectations are going to grow. And we will all have to compete more effectively or more aggressively on winning the rights to be able to serve those users. I think that changes the equation a lot. We now have a higher responsibility to deliver better quality experiences. But the core of all these experiences is data. We have to be able to collect more and more data to understand what&#8217;s working and what&#8217;s not working. Just delivering a chat experience on a website doesn&#8217;t mean it&#8217;s a good experience. Too often, in this new world, businesses rushed to add chatbots. Is anybody happier? No. People are still frustrated. But the real question now is “how do you continuously use that data to deliver better experiences?” To better understand your funnels and user journeys and drive customer retention. Where is user experience headed in the future? That&#8217;s the $300-million question. If your website experience is clearly positioned but you are delivering your chat capability through a third-party interface, how do you actually differentiate? All that matters is how much value you are delivering to your users. Don&#8217;t fall in love with what you have built. Fall in love with your customers and this should guide you every single day. Could we imagine, in a not-so-distant future, self-programming software? F.A. Users want software that&#8217;s adaptable, continuously monitoring itself to drive the right outcomes. I think one of the keys to achieving that is the ability to express the metrics, the goals that you have. Because the software will never know what ‘improvement’ means. Thus, if you were to say, &#8216;My goal is to increase signup conversions,&#8217; then the software could look at the data and improve itself, change terms, add new buttons and new capabilities that will drive that outcome for you. I think the world actually is shifting from websites to metrics and outcomes. And that&#8217;s how AI can come through. There&#8217;s a lot of gibberish that comes out of AI because it doesn&#8217;t know your intent. It doesn&#8217;t know your domain. But if you&#8217;re able to start with the intent, then everything else makes a lot more sense. We have a project in development right now where we analyse all the sessions on a given website. We&#8217;ll create screen recordings of everything. And from that, we will be able to infer which industry you&#8217;re in, where users are frustrated, how users are navigating your site. From that you can start ask AI to suggest changes for you. The whole online world is going to change, is it not? F.A. We&#8217;re at an exciting inflection point, like the PC revolution or mobile transformation. AI is going to be a whole new world, but we&#8217;re in the very early days. And now&#8217;s the time to start dreaming, trying, experimenting. My advice to everybody is to lean in. Don&#8217;t be afraid of it. Be the first ones to try and fail and test and really dream of what&#8217;s possible because there are incredible opportunities ahead of us. Those who don&#8217;t adapt risk being disrupted by those who do. The post Data-Driven AI Is the Future of Customer Experience appeared first on Marketing and Innovation.

Ever heard of cookie pop-ups? It&#8217;s true that it&#8217;s hard to escape them. Following the 2011 Cookie Directive, sites have finally complied. But rather than deleting cookies, they have installed cookie pop-ups, which throw annoying messages at you and prevent you from surfing the web. They&#8217;re useless, mostly because they don&#8217;t really improve data confidentiality. Their main purpose is for sites that track your data for advertising purposes to cover their tracks and pretend they&#8217;ve become virtuous. Here&#8217;s how to get rid of them. How to get rid of the cookie pop-ups Midjourney has imagined Darth Vader devouring cookie pop-ups for us You&#8217;re all familiar with those messages that warn you when you&#8217;re visiting a website stating that your data will be used, and that your consent is required to do so. 50 times a day you click, but not to get rid of cookies, but rather the cookie pop-ups. Internet users&#8217; knowledge of cookies Thanks to Statista, we can see that more than half (73%) of internet users in the United States are somewhat or completely unknowledgeable about cookies. Using this knowledge, websites take advantage by simply having a cookie pop-up, forcing users to click through to access the content. Europeans are highly exposed to cookies Despite this low level of awareness among French Internet users, ad blockers are used in significant proportions (25%), and irritation with online advertising is also very much in evidence (42%) What are the countermeasures against cookie pop-ups? So, what is there to do to reduce these repetitive, irritating messages? A content blocker sometimes won&#8217;t be enough to protect you from these cookie pop-ups. This list is not exhaustive and may evolve. Don&#8217;t hesitate to suggest additional solutions, which we&#8217;ll add to our benchmark. Firefox for those who still use it Let&#8217;s start with this extension for Firefox. Developed by Alessio Capponi, the No Cookie Wall extension claims just 26 users. Brave, the brave Web 3 browser that takes care of cookie pop-ups for you Brave, one of our favorite browsers, has also taken to blocking cookie pop-ups. Here&#8217;s what Brave has to say about it in the release of the latest version of the Web3 browser: &#8220;You know those annoying cookie consent notifications that pop up every time you visit a new website?&#8221;. Newer versions of Brave can hide them and, if possible, block them completely. Simply update to the latest version of Brave.&#8221; If you miss the prompt to block cookie consents the first time, you can visit brave://settings/shields/filters and easily enable/disable the EasyList-Cookie List option. Safari or the hunt for cookie pop-ups Safari (exclusive to Apple products) has an even more radical option: block all cookies. It&#8217;s so violent that when we activated it this morning, we lost much of the content of this post, which we had to recreate. Handle with care. Because it also deletes session cookies, which are essential if you want to remain connected to a site. This is my case here on WordPress. I&#8217;m there most of the day and I don&#8217;t want to log in again 20 times a day. The Safari option to block all cookies and cookie pop-ups&#8230; deadly and to be handled with care. Session cookies are not involved in cross-site tracking Edge on the verge of an anti-cookie pop-ups meltdown On Edge (Microsoft): Edge is Microsoft&#8217;s new browser, also available on Apple hardware. It&#8217;s very fast, extremely well designed, and includes Web3 subtleties such as a Wallet. What&#8217;s more, it lets you try out GPT4 coupled with Bing (aka Bing AI. Nice, but&#8230;). So here&#8217;s the CookieBlock extension, straight from Zurich. CookieBlock is a browser extension that lets you automatically delete cookies that don&#8217;t respect your privacy. Using advanced machine-learning technology, it classifies cookies into four distinct categories. These are &#8220;necessary&#8221;, &#8220;functional&#8221;, &#8220;analytical&#8221; and &#8220;advertising&#8221; cookies, which the user can then authorise or reject individually. Unlike cookie pop-ups, which interrupt your browsing experience, CookieBlock only asks you to define your policy once and guarantees that the types of cookies you reject actually get deleted from your browser. What&#8217;s more, CookieBlock works on all the websites you visit, and doesn&#8217;t depend on the site publisher&#8217;s goodwill. Thank God for that! Cookieblock, the Edge add-on that says goodbye to cookie pop-ups DuckDuckGo: hunting bad ducks DuckDuckGo (the browser): the famous privacy-friendly American search engine is the one we&#8217;ve been using on a daily basis for many years. DuckDuckGo&#8217;s browser is a good starting point for getting rid of all these little beasts. With DuckDuckGo (duck.com) you have nothing to fear from cookie pop-ups. Data confidentiality is included as standard, as is cookie destruction. DuckDuckGo also provides an extension that exposes and rates the ethics of the website you&#8217;re visiting (A+/B/B+, etc.) Shining like Chrome&#8230; Chrome: Here I&#8217;ll pass, if you use Chrome, no need to pretend to hide cookies, you&#8217;re the product. In conclusion This overview of anti-cookie and anti-cookie pop-up devices is not intended to be exhaustive. There are many others. It&#8217;s a good starting point, however, for avoiding those unbearable and unnecessary messages. The post Protecting your privacy and avoiding cookie pop-ups appeared first on Marketing and Innovation.

Is GenAI content marketing-friendly? Adobe organised a round-table discussion during their Experience Makers conference in Paris at the end of last year. The debate brought together a few digital experts. During this debate, I mentioned that there were limitations associated with GenAI image production and that they weren’t technical. Others contended that it was just a matter of prompt engineering. In my opinion, proper prompting may be recommended, but the limitations of GenAI image generation tools extend far beyond that. Such is my point, which I substantiate in this piece with insights derived from a two-year practice of such online tools while editing this very website. GenAI and Content Marketing Lessons From Experience This debate on GenAI and content marketing was an opportunity to take hindsight about images and their power to illustrate and differentiate our brands. Here we look at how generative AI was used to illustrate the Visionary Marketing news website. This debate on GenAI and content marketing was an opportunity to take a step back and think about images and how they can illustrate and differentiate our brands. Here we look at how generative AI was used to illustrate the Visionary Marketing news website. This debate was organised by Adobe at the Louis Vuitton Foundation in Paris. The main topic was GenAI and its impact on content marketing. This discussion turned out to be an opportunity for me to take stock of a year&#8217;s experience of using generative AI to produce images for Visionary Marketing. GenAI and Content: Excitement and Second Thoughts At first, as we discovered Midjourney and its clones, at the end of 2022 we all were very excited. And boy! Did we have fun producing images for all intents and purposes. Then came a moment when one needed to hold our horses. It was indeed high time to take a step back from it all to ponder over the use of GenAI with regard to content marketing. As I explained during the debate, it reminded me of these HDR filters I discovered when I started using Adobe Lightroom 12 years ago. At first, I resorted to them on almost a daily basis. Five years on, in hindsight, I removed all these HDR pictures. Our round-table on GenAI and content marketing: From left to right: Caroline Mignaux, yours truly, Frederic Cavazza, Adobe’s Lionel Lemoine and Fabrice Frossard. Thus, here are a few thoughts on the use of these tools which, in my view, are more than ever, worth investigating. Yet, one should look at them in the context of the widespread use of GenAI tools by both Web users and the Media. Firstly, what was initially pleasurable, at a time we felt like trailblazers, ends up being repetitive and bland. We come across too many of these pictures in the Media and on the Internet. Some of my readers pointed this out to me. My co-author even said he can&#8217;t understand why I don&#8217;t make more use of my own photos, whereas I am a photographer. He&#8217;s both right and wrong, and I&#8217;ll come back to that later. In the meantime, I insist that the featured image of this post is an original (and deliberately cryptic) photo by yours truly. Secondly, these pictures, often produced in haste, end up looking the same. They are also often rather garish, with saturated colours that are very characteristic of virtual images. They&#8217;re also rather banal and sometimes vulgar. I realise that this is a personal and biased statement. After all, though, when it comes to images, there is no such thing as objectivity. There&#8217;s also a general trend towards &#8216;heroic fantasy&#8217; images, a genre I have nothing against. Even though it&#8217;s not to my liking. Regardless of personal tastes, this does seem to add fuel to the fire of the trivialisation of images. To this one may add sci-fi-like illustrations, which are sometimes quite successful, but also confer a déjà vu aspect to your content. Lastly, a feeling of unease about images that are very realistic but at the same time are not. It&#8217;s a phenomenon known in the digital world as the Uncanny Valley. We’ll deal with this topic on this site in more detail at a later date. Using GenAI: Three Main Stages In fact, at Visionary Marketing, we went through several stages. In the beginning, we only used images from my personal stock. All the Visionary Marketing content writers had to go through this limited stock of images. These photos are personal, and therefore unique. Yet a feeling of déjà vu soon also set in. And above all, we were often unable to describe certain concepts using those images. It makes sense since this stock doesn’t include all the possible metaphors one would require. Fishing for the right picture amongst 12,000 of them isn’t always a piece of cake. Not to mention the crafting of the right captions, a real challenge that was! Stage 2 of our own discovery process consisted in adding stock photos to our content. This made it possible for us to bypass the above-mentioned limitation syndrome. However, it also made our illustrations look more commonplace. This could have been damaging in some cases. Fortunately, we were using Jumpstory, an image data bank that stood out from the rest. Thus, we avoided this pitfall to some extent. It was good while it lasted, for Jumpstory went under this year. I wouldn&#8217;t be surprised if GenAI killed it there and then.  Jumpstory images, like this one, were sometimes quite good. But you had to look hard to find the right one. Anyway, it sadly went under in 2024. Since the end of 2022, Generative AI From the end of 2022 onwards, this is stage 3, we started making more intensive use of generative AI to produce illustrations for our articles. In all cases, whether it be the first, second or third stage, we&#8217;ve come to the same conclusion: using the same image source all the time leads to a feeling of repetition, fatigue and trivialisation. I dote on abstract painting for illustrations and Midjoiurney in personalised mode (&#8211;p switch) enables just that. I&#8217;ve had a lot of positive feedback after publishing this picture even by people who were blatant AI sceptics. Innovation and creativity with AI are also possible, like it or not. Many artists have discovered that too. At the end of the day, to successfully illustrate your content, you are recommended to opt for a mix of the different techniques. Above all, as I explained during the Adobe debate, you have to be able to master the prompt so as to produce illustrations that are clearly differentiated from what other content publishers usually publish on the Net. As explained with the above example, this has been made easier as of mid 2024 when Midjourney started to implement the personalised mode. The more abstract the prompt, the more eye-catching and the more different the image produced. That’s what makes you stand out from the crowd. This is rather counter-intuitive. Indeed, most self-proclaimed AI pundits on LinkedIn and elsewhere will be adamant that such prompts should be banned. What life has taught me, though, is that when the crowd produces A, producing non-A will make your work — and yourself — more distinctive. Besides, advanced mastery of all available image tools (GenAI, Lightroom Classic, Photoshop, Illustrator, or all of them combined), means that you can retain total control over your pictures. Thus, you should be able to produce less commonplace pictures or illustrations for your content. More than ever, marketing is not about getting things done. It’s about getting things done differently. Whether you resort to GenAI or not, you should always bear that in mind. Last but not least, don&#8217;t hesitate to revisit your content to change illustrations that, with hindsight, seem too trivial, too stereotyped or too garish. Unless, of course, you like it that way. The post GENAI and Content Marketing: Learning from experience appeared first on Marketing and Innovation.

The state of influencer marketing in Europe 2024 is a survey conducted by Kolsquare, a leading European influencer marketing agency. It provides a particularly interesting perspective on influencer marketing budgets, how influencer marketing is handled and its future trends. Besides, its comparison of Europe&#8217;s main markets for IM is clearly enlightening. It&#8217;s one of the first if not the first of its kind and it sheds light on the way that businesses are conducting marketing with Key Opinion Leaders, at least for business to consumers. One of the most striking takeaways from this study is the sheer size of the average European influencer marketing budget which is evaluated at a whacking €3.375 million annually.  European Businesses Spend nearly €3.5m Annually on Influencer Marketing The Kolsquare/NewtonX 2024 European survey shows that Influencer Marketing has clearly become pivotal in the B2C marketing mix. Methodology of the 2024 Influencer Marketing Survey This 2024 European Influencer Marketing (IM) survey was conducted by Kolsquare and NewtonX. It involved 385 decision makers representing medium to large organisations across various sectors (Beauty and fashion, IT, SaaS and Telecommunications, Retail food and beverages, entertainment …). All respondents had more than two years of experience in influencer marketing. The sample is relatively large for that kind of B2B survey with five countries surveyed (France, Germany, Spain, Italy, and the United Kingdom) and approximately 80 respondents in each of these countries. Influencer marketing 2024 survey methodology The European influencer marketing landscape Thanks to this survey, we now have evidence that the influencer market is really significant with €3.375 million spent on influencer marketing by European businesses annually, and Germany topping the list at €5.74 million per annum. Micro influencers (10,000 to 100,000 followers) being the most popular partners for the surveyed European businesses. Respondents’ expectations on growth are very optimistic with 54% of them expecting to increase their influencer marketing budget next year. Unsurprisingly, the influencer marketing landscape has shifted towards three main platforms: Instagram, TikTok, and YouTube. More than ever, influencer marketing is here to stay with 27% of respondents saying that it will become more important in the marketing mix. And even 6% stating that it will become the most important part of the overall marketing spend. UK marketers seem less prone to spend huge chunks of their budgets on influencer marketing with a yearly average of £848,000 (still a whopping €1.02 million!) Brands are also declaring that they will become more selective in the influencers with whom they work (56%). Ethics is topping the list of preoccupations in Italy and France, but less in the UK and not that much at all in Germany. Indeed, Germany is described by Kolsquare as &#8220;the big spender”, but not very keen on ethical considerations. Unlike the French and Italians, who said to be prioritising corporate ethics when selecting influencers. This emphasises a significant shift in the market, whereas four or five years ago we were stressing the fact that ethics weren’t really on French influencer marketing managers’ priority list. Influencer marketing landscape Social network usage by marketers and Key Opinion Leaders When it comes to social network usage by influencer marketers, the shift towards Instagram, TikTok and YouTube is significant. However, Facebook has not disappeared from the IM landscape completely, as it is still the platform of choice in the UK. X has slipped down the ladder and further down one can find niche platforms such as Twitch, Pinterest, Snapchat and a flurry of Chinese platforms that are clearly less attractive to European marketers. What is surprising, though is that LinkedIn is definitely not part of this list, meaning that the survey is mostly geared towards Business to Consumer marketing. For all intents and purposes, one should emphasise once more that Business to Business amounts to approximately 80% of the production of wealth worldwide. When it comes to size, One can spot a good balance of macro, mega, micro and nano-influencers in marketers’ choices of partnerships. Whereas patterns are relatively similar across countries, micro influencers definitely top the list in almost all of them. One-Shot Versus Long-term Influencer Marketing Collaborations It seems that in Germany and France businesses prefer to work in the long-term with Key Opinion Leaders. However, the structure is rather similar in all countries with a three-tier pattern: approximately 30% of collaborations with long-term partners, 30% for a mix of new and existing influencers, and another 30% of new kids on the Instagram block. This pattern varies slightly according to countries. Content Forms: How the Brands Collaborate With Opinion Leaders The variety of content types that is offered by influencer marketing is noteworthy, with sponsored posts and influencer events as well as product reviews topping the list. However, there are differences according to countries with sponsored posts not being very popular in France (only 23% of respondents vs. 58% on average across all countries). Approximately one third of businesses are keen on performing co-creation with influencers and even nearly 25% of respondents are conducting product creation with them. In conclusion This study is instrumental in showing how pivotal influencer marketing has become in B2C marketing. Barring a few variations, one can say that IM patterns are relatively similar across the five main European countries surveyed. Spending levels are stellar, with German businesses being on a buying spree. One can only hope that IM will help them fight the current economic slump in Europe’s biggest economy. France and Italy are keen believers in IM too. The United Kingdom and Spain are lagging a bit behind, or can be considered more reasonable, it depends on the point of view. The most important indicator (KPIs) for influencer marketers is not the number of followers but the quality of the engagement. And as it suits B2C, it&#8217;s even shifting towards sales and conversions. Last but not least, there are a number of challenges to influencer marketing such as striking the right balance between influencer freedom and brand control. A major issue we have consistently highlighted for the past 20 years we have spent in that domain. It’s a significant pain point in most European countries and especially in France, where brand control is tightening on influencers. Measuring ROI and ROAS (Return on Ad Spend) is on top of Italy&#8217;s list of issues related to influencer marketing. Apart from that, authenticity and the quality and tone of voice of influencer content is definitely what entices European brands to work with Key Opinion Leaders. What is also most striking is the significance of ethics according to countries. The results seemed very counter-intuitive to me but reassuring, with Southern countries showing a lot of concern for ethics compared to Northern ones. About KolSquare Kolsquare is Europe’s leading Influencer Marketing platform, a data-driven solution that allows brands to scale their KOL Marketing strategies and implement authentic partnerships with KOLs (Key Opinion Leaders). Kolsquare’s technology enables marketing professionals to easily identify the best Content Creators’ profiles by filtering their content and audience, and to build and manage their campaigns from A to Z, including measuring results and benchmarking performance against competitors. Kolsquare was founded by Quentin Bordage in 2018.  The post Influencer Marketing: Average European Spend at €3.5m Annually appeared first on Marketing and Innovation.

The Cyber threat landscape in Europe is quite worrying. A recent survey by Cloudflare was conducted amongst 4,261 IT executives responsible for cybersecurity in Europe. 24% of the sample is made from small enterprises (150–999 employees), 24% from medium-sized businesses (1,000–2,500 employees) and 52% from large organisations (above 2,500 employees). All major European countries were surveyed by Cloudflare in their study entitled Shielding the future: Europe&#8217;s cyber threat landscape. The report paints a rather bleak picture but stresses that solutions exist&#8230; as long as leadership teams understand what new Cyber threat countermeasures like Zero Trust are about. All in all, this will require all management teams, not just IT, to better understand the ins and outs of such dangers. European Cyber threat Landscape: a bleak picture but there is still hope European Cyber threat Landscape: Cloudflare paints a very bleak picture but there is still hope The sample of this survey is quite comprehensive given its high profile and B2B orientation. The Sample of the 2024 Cyber threat survey by Cloudflare Some of the takeaways from this report on the European Cyber threat landscape include: – All kinds of businesses are impacted by cyber threats with 72% of respondents reporting at least one incident in the last 24 months, – 84% of respondents reported more incidents compared to past years. With a staggering 43% of those organisations experiencing 10 or more attacks in the past 12 months, – Attackers are resorting to a variety of methods, with phishing and Web attacks on top of the list, – A remarkable low number of respondents (29%) state that they are well prepared for future incidents, therefore leaving 71% out of that picture, – Over half of respondents anticipate that their organisation will dedicate more IT budget to cybersecurity, – There is a growing concern that &#8220;adding numerous point solutions is not the answer&#8220;. With nearly half of respondents ranking &#8220;simplifying and consolidating their cybersecurity stack&#8221; as one of their top three priorities, – Moving to zero trust security could help but 86% of respondents reported that their leadership teams do not yet fully understand this model. Let&#8217;s face the music, How many IT execs are feeling comfortable with the understanding of how our complex online systems should be protected? Well, not that many. However much I hate the idea, it seems that too much openness of such systems isn&#8217;t making our lives easier. It seems that an increasingly dangerous cybersecurity landscape is causing more and more aggravation within organisations. The growing complexity of open networks with access to increasing amounts of money is too big a temptation for most cybercrooks to resist. Besides, the staggering complexity of IT, networking and especially cybersecurity solutions such as zero trust explain why there are so few companies that are ready to implement such solutions. However much sense they may make. However much I hate the idea, it seems that too much openness of such systems isn&#8217;t making our lives easier. The post Cyber threat Landscape Europe, 2024 appeared first on Marketing and Innovation.

Content creation with artificial intelligence is already old hat as it’s been going on for a few years and, unfortunately, slop is now populating the Internet at an increasing pace. Yet, when I received this message from a good friend of mine last week regarding Google’s new app entitled NotebookLM, I was shellshocked. I tried it and tested it and felt immediately overwhelmed. Having slept over it for a few days, I’m just recovering, so here are my impressions.  NotebookLM by Google: Artificial Voices, Real Concerns NotebookLM by Google: Artificial Voices, Real Concerns — an image produced with Midjourney and our own special personalised mode The other day, a friend of mine sent me a message about Google’s new NotebookLM AI application. As I always do that kind of thing, I tried and tested it immediately. The catchphrase for NotebookLM is “Think Smarter, Not Harder” and Google presents it as “The ultimate tool for understanding the information that matters most to you, built with Gemini 1.5”. NotebookLM: made to &#8216;understand&#8217; information? I wonder about that. Is it really a tool made to “understand information”? This sounds a bit dubious.  The aim of NotebookLM is to turn a piece of text, a video, a web link into a conversational podcast. It is semi customisable and not quite finished. But it gives you an idea of what the future has in store for us, content creators.  On the one hand, the technology is great and works fine, barring a few glitches, on the other hand, it’s a window on a very weird and dark future  (once again, it’s not the tool that is the problem but the people using the tool, as Bradbury remarked).  For my initial test, I selected one of my English pieces about artificial intelligence (AGI). I copied and pasted my text into the window and hey presto! a few seconds later, a proper conversational podcast between an American man and woman was available. Here it is  https://visionarymarketing.com/wp-content/uploads/2024/10/2024-10-21-raw-agi-ganascia-notebook-version.mp3 I must admit I couldn’t believe my ears when I heard this podcast generated from a mere piece of text. It was both brilliant and daunting. I immediately thought that anybody could produce an audio conversation out of anybody’s blog piece and I suppose that some of the laziest of content creators will do just that. When I looked into the podcast in greater detail, I spotted that there were a few glitches here and there and especially the quote by Ray Bradbury which is definitely not taken from Fahrenheit 451. It’s clearly mentioned in my text.  Man | 01:24.308 It&#8217;s like that line from Fahrenheit 451. I&#8217;m not afraid of robots. I&#8217;m afraid of people, people, people. Woman | 01:28.691 Yeah. Well… nope, sorry (and by the way, I hate your “yeahing” at me). This was taken from Bradbury’s 1974 letter to Brian Sibley. It’s clearly stated and the link to the source file is explicit.  One more test This very morning, I went back to the application and tried it once more. I inserted a YouTube video and it failed a couple of times. So I gave up and copied a web URL and it worked wonders. This time I used my fraud and AI piece with Fujitsu.  https://visionarymarketing.com/wp-content/uploads/2024/10/fujitsu-fraud-in-retail.mp3 I tried to customise the podcast but I couldn&#8217;t change the American voices nor the tone of voice which isn’t consistent with mine. I tried to turn it into a more professional, less casual, tone of voice. This didn’t work as planned. But my instructions aimed at making the podcast more factual and to focus on the numbers were executed correctly by the AI.  NotebookLM by Google lets you customise the result, well&#8230; almost That said, when you listen to the entire podcast, especially towards the end you will realise that the AI is adding quite a lot of content to it and making its own commentary.  A few worrying signs This experiment raises quite a few questions. To start with, a lazy content writer could start publishing its own podcast channel from scratch using other content creators’ content without even mentioning their names; Second, hallucinations are still part of this equation and they are quite wicked and hidden and hard to track. Once again, if you are a lazy content writer, then it doesn&#8217;t matter at all. On the contrary, if you are a conscientious content creator then it all makes the difference.  The fact that this AI app is adding content is another kind of hallucination even though the text is perfectly sensible compared to the original piece. But I didn’t write that, I didn’t think that and I’m not even sure I want to add it. This is not only annoying, but downright worrying. Last but not least, the casual tone of voice used by the application and the voices and accents that aren’t customisable yet are a showstopper as far as I&#8217;m concerned. But I suspect that this can be easily corrected. In conclusion As I mentioned already, I find this kind of application a little worrying. On the one hand, it is great to be able to produce a conversational podcast which is very engaging, very quickly. In hindsight, this is probably conducive to producing even more slop on the Internet, which will probably end up collapsing in on itself. It&#8217;s a matter of years if no one stops this nonsense. I&#8217;m not sure that Google will maintain this application nor that it will even let you use it for very long. They have a track record of crucifying innovations. The KilledbyGoogle.com website. Please note that Google didn&#8217;t manage to kill Squarespace (yet). This is an add. Will Notebooklm be added to this list? Time will tell. It could be quite tempting to use NoteboookLM to produce conversational podcasts without any efforts. But I will never use because we have this 100% human content commitment on visionary marketing. I may end up being the last of the Mohicans in that concern but I intend to keep the upper hand in this content creation process. I’m the one doing the thinking here, not the tool I’m using to write it. Transcripts of both NotebookLM podcasts 2024-10-21-RAW-AGI Ganascia notebook version.pages Fujitsu – fraud in retail.pages The post NotebookLM by Google: Artificial Voices, Real Concerns appeared first on Marketing and Innovation.

Whereas Ed Zitron is castigating the major Tech players responsible for the peak of inflated expectations surrounding AI, many tech pundits are still touting that AGI (Artificial General Intelligence) is within reach. To find out if AGI is a myth or a reality, I interviewed J.G. Ganascia, a long-time AI researcher and philosopher. In the course of our discussion, I gathered that the singularity and AGI weren&#8217;t the same thing. This interview set a lot of the record straight, particularly regarding the notions of intelligence and sentience or consciousness. But its striking conclusion is undoubtedly that, like Ray Bradbury, we should certainly be less wary of pseudo-intelligent AIs, let alone AGI, than of the wily intelligent humans behind these technologies. General Artificial Intelligence (AGI), Myth or Reality? In J.G. Ganascia&#8217;s opinion, it is absolutely essential to retain control over the machine. And be wary not of artificial intelligence, but of the people behind it. An advice previously delivered by Ray Bradbury in his time &#8211; Image produced with Midjourney, whom I trained not to show robots. But in this instance, it was hard to avoid. At least this one is under human control, which is reassuring&#8230; The Singularity, AGI and Superintelligence J.G Ganascia. Transhumanism led to many projections about artificial intelligence, of which the technological singularity was one of the avatars. There are others today like Nick Bostrom&#8217;s Superintelligence. The singularity, AGI and Superintelligence are very different notions. Above, the cover of the book on Superintelligence by Swedish author Nick Bostrom But these terms are not interchangeable. The singularity, technological dream or nightmare JGG. The technological singularity is an idea from the 1950s. It claimed that at some point machines would become as powerful as humans, causing a shift in human history. This meant that at some point, machines would have taken over. Either they would overtake us completely, at which point humanity as we know it would disappear. Or humanity would submit to the power of machines, and humans would become their slaves. J.G. Ganascia at an AI conference in Paris in March 202. AGI isn&#8217;t on the agenda according to him. But beware of those pulling the strings. Another possibility was that we grafted ourselves onto machines and downloaded our consciousness onto computers, and that this consciousness could then be reincarnated onto robots. According to this theory, we could then continue to exist beyond our biological bodies. This is what I described in a novel written under the name Gabriel Naëj, this morning, Mum was uploaded (in French only). This is the story of a young man whose mother decides, once deceased, one should download her consciousness and reincarnate her as a robot. What is very disconcerting for this young man is that she has chosen the most beautiful body possible, that of a sex robot! AGI and superintelligence JGG. What we call AGI, Artificial General Intelligence is a different kettle of fish. It&#8217;s the idea that, with current artificial intelligence techniques, there are specific human cognitive functions that can be mimicked by machines, and that one day we&#8217;ll be able to emulate them all. It means there is a way of deciphering intelligence, and that once we find it, it opens up infinite possibilities. In essence it&#8217;s a gateway to superintelligence. The very principle of the technological singularity assumed that there was a general intelligence and that all cognitive capacities could be emulated by machines. General intelligence isn&#8217;t quite on par with the technological singularity and at the same time suggests it&#8217;s the ultimate goal. AGI has nothing to do with downloading human consciousness, though. this is just the ability to build machines with very high intellectual power. This ties in with Nick Bostrom&#8217;s plans for superintelligence, which focuses on the day when the intelligence of machines is greater than that of humans. There are links between these concepts, but they&#8217;re not quite the same thing. As of 2024, is the singularity still a myth? JGG. The early science fiction writers who mentioned the technological singularity, including Vernor Vinge, predicted that it would happen in 2023. Now, clearly, it&#8217;s not here yet. Unless we&#8217;ve all already been downloaded onto machines without knowing&#8230; And yet these AIs are amazing! JGG. Artificial intelligence has made considerable headway. Machines are capable of mastering language to the point where, when asked a question, they generate texts that are well formulated, even though not always relevant. We can also produce images of people that bear an uncanny resemblance to real humans. Videos too. It&#8217;s all very intriguing. Until now, one thought that language was first and foremost a matter of grammar, then syntax and vocabulary. Now we are realising that these linguistic abilities can be reproduced with just a few probabilities. It&#8217;s really exciting from an intellectual point of view. But that doesn&#8217;t mean that the machine will suddenly take over, or that it will have a will of its own. It doesn&#8217;t even mean that it will tell the truth. These AIs almost write like humans. Most of the time their content is based on common knowledge. But sometimes this &#8220;common knowledge&#8221; is a little absurd. And as soon as you shift the situation a little, they produce results that are completely wrong. I&#8217;m often playing tricks on them with logic puzzles and I&#8217;m having great fun as they fail. It&#8217;s understandable , in fact, because that&#8217;s not what they were made for. They are just made of modules capable of selecting words based on probabilities. Yann Le Cun is dead against GenAI, yet he believes in AGI. Are you prepared to change you mind about the subject? JGG. Absolutely not! I think there&#8217;s a misunderstanding regarding the meaning of the term &#8216;intelligence&#8217;. Besides,  artificial intelligence is a scientific discipline. What AI does is stimulate different cognitive functions. What are they? Perception, reasoning, memory (in the sense of processing information, not storing it) and communication. We have made considerable progress in these areas. Take perception, for example. AI is capable of recognising an individual out of hundreds of thousands, whereas we ourselves can&#8217;t always remember the people we met a day before. These performances are extraordinary. But where there is a misunderstanding when one states that the machine will be more intelligent than man. Intelligence is a set of cognitive abilities. It may well be that each cognitive capacity is better emulated by machines than by humans. Yet, that doesn&#8217;t mean that machines will be more intelligent than us, since they have no consciousness. Machines do not &#8220;see&#8221; things nor have a will of their own. In any case, consciousness is the crux of the problem. There&#8217;s another meaning for the word &#8216;intelligence&#8217;, which is related to ingenuity or inventiveness. An ingenious or clever pupil is said to be &#8216;intelligent&#8217; because he or she can solve everyday life or mathematical problems. Are machines more clever than we are, though? It depends. There are some cases, of course, where they outdo us. We&#8217;ve known for a very long time, 25 years now, that machines play better chess than we do. More recently so for the game of Go. Thus, from that point of view, of course, they are more intelligent, but that doesn&#8217;t mean they&#8217;re better than we are. In any case, they have no willpower per se. Blaise Pascal, just over 400 years ago, explained that his calculating machine came closer to thinking than anything animals could do, but that there was a limit to it. 340. The arithmetical machine produces effects which approach nearer to thought than all the actions of animals. But it does nothing which would enable us to attribute will to it, as to the animals. Blaise Pascal, Pensées (Musings)- page 69 As it happens, computers are like Blaise Pascal&#8217;s arithmetical machine. Their effects are closer to thought than anything done by any animal, including humans. But there&#8217;s nothing to say that they can have willpower like animals. I think that&#8217;s where the misunderstanding really lies. After that, of course, you can list all the performances of the machines, and you&#8217;d be right to label them as extraordinary. But it can&#8217;t be compared to man&#8217;s thinking. When it comes to consciousness, we can dig a little further. One of the AI pioneers, Yoshua Bengio co-authored last August a long 88-page article in which he explained that machines today are showing signs of consciousness. He has taken up the work of neuroscientists on consciousness and declares that this is a possibility. Above all, he suggests that machines will soon have such sentience. Once again, this is the result of a misunderstanding. The term sentience, or consciousness, like the term intelligence, is one of many meanings. First of all, we can say that a machine is sentient in the sense that we project an animal onto it. This is what happens with your mobile phone when you say &#8220;Siri is completely mistaken today&#8221; as if Siri were a real person. Or with a robot vacuum cleaner when you say &#8220;Well, he went there because he knows there&#8217;s dust out there&#8221;. One tends to assume these inanimate objects are like humans, but they aren&#8217;t. This is called, in technical terms, a cognitive agent. An American philosopher, Daniel Dennett, calls it intentional systems. And there&#8217;s nothing wrong with that. The second meaning of sentience or consciousness is that of &#8216;musing&#8217; or &#8216;reflecting&#8217;. It&#8217;s sentience as self-knowledge as in &#8220;Know thyself!&#8220;.  In other words, we are in the process of becoming aware of ourselves and wondering, &#8220;I&#8217;m doing this, now is it the right thing to do?&#8221; That&#8217;s why we talk about moral awareness, where we can say to ourselves, &#8220;I&#8217;ve done this or that in the past, and I can do a lot better now&#8221;. We can have machines, for example, that learn by looking at what they have done in the past, and then try to ensure that their future behaviour will be more effective moving forward. If they have hesitated between different possible paths before, in a similar situation, they will no longer hesitate, but will only take the right path. The same applies to moral consciousness. My team is working on computational ethics, which means that before acting, the machine tries to look at the consequences of its actions, and from that moment on, it will take the decisions that are most in line with the prescriptions given. There is also a third meaning of sentience or consciousness, which is very likely to be the most important: that of emotion. Can a machine experience emotions? And what does that mean? If a machine were to feel this way, it might think: &#8220;I want those good vibes!&#8221;, and if you ask it to do something at that moment, it won&#8217;t give in. So you ask an autonomous car, &#8220;I want to go to the beach&#8221; and it says, &#8220;No, because there&#8217;s too much sand over there. I&#8217;m going to take you to the pictures, to a place where there are very clean car parks.&#8221; Such a machine would be a disaster. Fortunately, it doesn&#8217;t exist. It&#8217;s absolutely essential that machines don&#8217;t make decisions on their own; they must always be submitted to our will and control. When major AI players like Sam Altman tell us that these machines are going to take over, we have to be wary. It&#8217;s a bit like them telling us We&#8217;re the ones with the knowledge, because we&#8217;re the pundits of artificial intelligence, and you don&#8217;t know anything. So leave it all to us and we will help you! Like many of the engineers working for major digital companies, Altman is fascinated by these machines. So he thinks there are no limits to what they will do in the future. He simply means that they will do all sorts of tasks better than we can. An open letter was signed by some major Internet players over a year ago. Sam Altman was not a signatory. But this initiative did include Yoshua Bengio, Geoffrey Hinton, Elon Musk&#8230; They told us we had to stop Generative Artificial Intelligence because it&#8217;s a potential threat to us. Should we develop non-human minds that could one day be more numerous, more intelligent, more obsolete and replace us? Should we risk losing control of our civilisation? Pause Giant AI Experiments: An Open Letter I&#8217;m sorry, but I disagree strongly with this vision. I&#8217;ve been working on artificial intelligence for years on end. I have never seen a &#8220;non-human mind&#8221;. These machines are competing with us on high-level tasks. And more generally, cognitive science has been telling us for a long time, and Howard Gardner in particular, that there are multiple intelligences. There are as many kinds of intelligence as there are people. Multiple intelligences according to Howard Gardner &#8211; source: Simply Psychology Functional neuroimaging allows us to visualise the active areas of our brain according to the tasks we perform, and these areas vary according to each individual. Similarly, when we map them out, we realise that the areas of the brain are not developed in the same way for all individuals, depending on their upbringing, genetics and so on. All this suggests that intelligence cannot be general, since it varies for each individual. The machine could, however, reprogram itself or correct some of its errors JGG. That&#8217;s exactly the definition of machine learning. It&#8217;s a machine that is capable of rewriting its own programme based on a certain number of observations, experiments. From that point of view, it&#8217;s nothing new. The question is rather whether this machine has a will. That&#8217;s why Pascal poses the problem admirably. Other philosophers like Daniel Andler aren&#8217;t sure that machines are not sentient, though JGG. I think we also need to go back to the definition of the term sentience. Scientists have been musing about creative machines for a very long time. Alan Turing, in his 1950 article Computing Machinery and Intelligence, contradicted a number of objections to the idea that a machine could be intelligent. And among these objections was one that said &#8220;A machine cannot create&#8221;. And his point was that a machine can very well create. But what is creation? It&#8217;s about producing something that will take us by surprise. But he added that he could easily devise a very short programme of just a few lines whose behaviour could not be anticipated. From that point of view, one can make machines that create. The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false. Alan Turing, The Computing Machinery and Intelligence, 1950 There is a whole history of creativity in machines that predates generative AI. The first poems, incidentally, date from 1957 JGG. In the musical composition programme Illiac Suite by Lejaren Hiller and Leonard Isaacson (1957), the final movement included elements of random programming and creativity. Indeed, the use of randomness in this context was seen as a means of producing something &#8216;new&#8217; or unpredictable, emulating a form of creativity. Some artists have also used computers. This is the case with Pierre Barbaud (1911-1990), who was a great pioneer in that field. Painters too, including Vera Molnar (1924-2023), who created some magnificent paintings with her machines. One could debate about the quality of what is generated by AI. Just because I made a fake Van Gogh with AI doesn&#8217;t mean it has anything to do with Van Gogh or that it&#8217;s interesting. But that&#8217;s beside the point. Does this machine have a will of its own that would contradict ours? In other words, at a given moment, that it could decide to stop for no reason or to take you to a place that you hadn&#8217;t imagined and that doesn&#8217;t correspond to a given objective. I don&#8217;t think we need to worry about that. Machines are not going to become autonomous. But society is changing. And the major issues are political, and that&#8217;s what we need to be very aware of. In particular, we should be wary of those who own these technologies. So it&#8217;s Mr Sam Altman we need to be wary of. He has a tendency to mesmerise us, to cast a kind of smokescreen behind his intentions. Sam Altman, in fact, is the danger! Similarly, when Elon Musk wants to protect us against artificial intelligence by enhancing our cognitive abilities and putting chips in our heads. If we go his way, it will be Mr Elon Musk who decides what will be in our heads. And it will be the worst dictatorship we&#8217;ve ever imagined. That&#8217;s the danger for the future! You have to be vigilant, but you have to know where to look and what to be wary of. The pseudo-intelligence of AIs, less dangerous than the harmful intelligences of humans? JGG. Absolutely! Ray Bradbury, the author of Fahrenheit 451 wrote this famous line: &#8220;No, I&#8217;m not afraid of robots, I&#8217;m afraid of people, people, people!&#8221; Letter to Brian Sibley, 1974 Quote to be found on azquotes About Jean-Gabriel Ganascia Jean-Gabriel Ganascia Chairman of the CNRS Ethics Committee A professor at the Paris-based Université Pierre et Marie Curie (UPMC) and a member of the Institut Universitaire de France, Jean-Gabriel Ganascia was appointed chairman of the CNRS Ethics Committee in September 2016. An IT expert, holder of a PhD and doctoral thesis from the Université d&#8217;Orsay (Paris), he specializes in artificial intelligence. His current research work focuses on machine learning, text mining, the literary aspect of digital humanities and computational ethics. An IT professor at the UPMC since 1988, he heads the Cognitive Agents and Symbolic Machine Learning (ACASA) team at the LIP6 computer science research laboratory. He also set up and led the Sciences de la cognition (&#8220;cognitive science&#8221;) scientific interest group at the CNRS. Jean-Gabriel Ganascia is a member of the CERNA (ethics in digital science research commission) at the Digital Science and Technologies Alliance, Allistene. The post AGI (General Artificial Intelligence), Myth or Reality? appeared first on Marketing and Innovation.

Our reporters attended the Paris Retail Week 2024 event, a trade show of which we are media partners, to take stock of fraud and the role of AI. We collected a lot of valuable feedback on threats (both in-store and e-commerce) and the countermeasures proposed by artificial intelligence. To do so, we interviewed Gilles Bijaoui, head of CX at Fujitsu (Customer Experience being the name of the retail division chosen by the Japanese company). During this discussion,  which took place at the opening of the show, he gave us a wealth of information on fraud and described the AI solutions designed to combat it. Such solutions have now been deployed for almost two years in retail chains of all sizes. When AI fights fraud in-store and on websites AI and fraud detection in retail: Fujitsu&#8217;s Gilles Bijaoui, Head of Customer Experience at Paris Retail Week on 17 September 2024 in Paris Isn&#8217;t AI-driven fraud reduction deja vu? Gilles Bijaoui. Indeed, this has been a recurring topic over the last five years, whether at NRF or Paris Retail Week. But it wasn&#8217;t really implemented in the field. But over the last two years or so, that has completely changed. Post Covid, we find these solutions in production in many retailers of all sizes. There are several types of AI involved, including generative AI. This has been rolled out by several large retail chains and the feedback is fantastic. Be it regarding performance, reducing employee theft and shoplifting, whether for physical retail or e-commerce. What kind of fraud are we talking about? GB. There are two types of fraud in retail and e-commerce: Firstly, in-store fraud, just over half of which is linked to theft in the shop and around 40% is due to shop assistants themselves. When it comes to e-commerce, it&#8217;s more likely to be linked to electronic flows of information, either payment fraud or identity theft (or phishing). It&#8217;s more complex and also more damaging. Fraud on e-commerce platforms accounts for almost 20% of e-commerce sales worldwide. That&#8217;s a huge amount! If you compare this figure with in-store theft or fraud, it&#8217;s down to only 2-3%. We caught up with Gilles Bijaoui at Paris Retail Week to talk about AI and fraud detection. We&#8217;re definitely on a different scale with e-commerce. It&#8217;s also linked to the volume of transactions, which soared during and after the Covid crisis. But the good news is that, thanks to AI technologies, we&#8217;ve seen a reduction in the growth of these fraudulent activities over the last two years or so. Previously, electronic fraud was growing at a rate of around 20% a year, but thanks to AI we&#8217;re now down to less than 15% per annum. And it&#8217;s getting even better with the education of individuals and businesses. Are there any regional variations regarding fraud in retail? GB. Not in Europe, all countries are pretty much the same. Only one country is an exception, and that&#8217;s the UK, which has even more fraud, as in the US, whether it&#8217;s physical or e-commerce. That&#8217;s a difference of around 10 points over France. What&#8217;s more, the figures are reversed. Physical retail theft in the United States and Great Britain is higher than in the rest of Europe. And conversely, e-commerce is better controlled there than on the continent. Most likely because they decided to deploy these solutions before other countries. On the other hand, given the massive in-store fraud in the United States, many everyday consumer products are under lock and key. Let&#8217;s now talk about combatting fraud with artificial intelligence GB. For physical stores, there have been two eras, and two different systems. Previously, artificial intelligence was based on data. The more data we had, the more artificial intelligence systems learned, and were able to identify recurring patterns. This has changed to the extent that now, we&#8217;re relying more on the definition and identification of behaviours and movements. Some of these modern solutions have recorded hundreds of human behaviours that can help determine a person&#8217;s intentions. Based on what we call &#8220;patterns&#8221;, we are able to tell, with the in-store camera system and artificial intelligence, whether a person intends to buy, steal, or just potter around. This is a starting point for AI to trigger personalised in-store promotions. Personalisation With AI GB. For example, if you&#8217;re standing in front of the aisle dedicated to formula milk in a supermarket and you&#8217;re hesitating, artificial intelligence will advise you based on the information available. It can also provide you with recipes based on the food you are buying or suggest that you virtually try the garments you are about to purchase. We can also offer promotions tailored to the person based on their loyalty cards, and instantly offer a coupon on an item a consumer is looking at. These marketing and merchandising efforts are no longer restricted to online commerce. Now, they are also available at the point of sale. And all this, thanks to artificial intelligence. How can one avoid hallucinations on such recommendations? GB. We have to fight this idea that humans will be disappearing because of AI. These systems are not autonomous. They need to be controlled. Certain words must be banned, for example. The way we address people has to be calibrated so as not to offend or be too intrusive. All this has to be strictly supervised. Marketing messages must be constantly calibrated, both by us and our customers. Let&#8217;s take an example. We&#8217;re currently working on fitting rooms. A lot of fraud is happening there, both by consumers and shop assistants. And RFID doesn&#8217;t really help fight this issue. We have therefore combined several solutions so that we can identify that if a person comes in wearing red and goes out wearing black, something is clearly wrong. Similarly, if that person goes in wearing a size 12 and comes out with size 24, something isn&#8217;t quite right either. Behaviours are also analysed, as I pointed out earlier. At the end of the day, though it&#8217;s always the customer who decides where to draw the line. There are also laws governing the use of these technologies. For example, one isn&#8217;t allowed to recognise faces in continental Europe. Is there a good business case you implemented with a retail chain? GB. We&#8217;ve worked with a well-known German hard discount chain for which we have deployed anti-theft solutions at the checkout. The solution makes it possible to identify fruit and vegetables in a relevant way. If you take a bottle of wine and you change the label or the barcode is not the right one, the system is able to detect it. With this solution, we have succeeded in reducing thefts by 60%. Into the bargain, improving in-store communication, both towards shop assistants and customers, also helps to reduce thefts by around 20%. How does it work in practice? GB. For automatic checkouts, a camera is placed on top, coupled with sensors that can measure not only the weight, but the typology of the product, its firmness, size, colour and even its density. The progress made with cameras is considerable. We&#8217;re able to spot the right product with around 90% success rate. And it&#8217;s the same at the physical checkout, in other words, it also prevents certain employees &#8211; let me remind you that this accounts for 40% of fraud &#8211; from passing the wrong products, or forgetting to pass a product. If this happens, an alarm rings. And once a certain number of alarms have been triggered, human intervention is required. We also know that most checkout fraud occurs in the last two hours before closing time. The pace picks up enormously at that point, because there are longer queues and dishonest employees figure it&#8217;s going to be easier to get away with theft. Yet it is precisely where controls will be tightened, alarms will be more frequent, there will be greater vigilance over data reconciliation and a speeding up of the process. What is very important to bear in mind at all times is that all these systems are controlled and validated by human beings. In the event of a mistake or problem, human intervention will either trigger apologies or reverse an incorrect identification. Artificial intelligence is an ally of commerce, but it is not inhuman if it is properly implemented. What about e-commerce? GB. On the e-commerce systems that we deploy, we observe that fraud often comes from the same IP addresses and the same geographical areas. Fraud is often linked to an initial failure to enter a credit card number or make a payment. This triggers alerts accordingly. In response, we will either block the payment or notify the end customer&#8217;s bank. The computing power is such that fraudsters can hardly get away with it. And banks have also made efforts in this direction, with 3D secure and dual authentication of purchases via mobile applications, email or text messages. All online businesses, even the smallest merchant sites, are equipped with these solutions, which help to prevent attacks, the theft of customer files and phishing. Could fraud ever disappear with AI? GB. We&#8217;re not that far off. Especially as we develop new technologies, including fingerprinting, retinal scanning and even palm scanning. Probably a lot of e-commerce sites or personal computers will soon be equipped with them. In partnership with Ingenico, we have developed a system that recognises only the palm of the hand. The venous system is unique to each individual. And you can&#8217;t reproduce it with a photograph. Add to this heat measurement and you have an ultra-secure system that will further reduce fraud. About Fujitsu Fujitsu is a company that is both well-known and little known. It is a large Japanese technology company that started out over 100 years ago with telecommunications and switched to services in the 2000s. It employs just over 130,000 people worldwide, and has a turnover of 33 billion dollars. Its retail division (aka Customer Experience) accounts for just over 20% of the Japanese company&#8217;s worldwide sales. Its activities are now focused on services and platforms for points of sale and e-commerce, not forgetting integration services, consulting and infrastructure for retailers. Fujitsu is even the 8th largest technology services company in the world. The post Harnessing AI to Combat Fraud in Retail and E-Commerce appeared first on Marketing and Innovation.

What is the likely impact of AI and GenAI in particular on jobs, especially in Europe? Two recent reports on the topic, one in the UK and another one in France shed light on this question. According to the French report, such impact could amount to 5%. Yet another case for precision vs accuracy. That figure seems counter-intuitive when so many self-proclaimed AI gurus, especially on LinkedIn, are hailing the GenAI &#8220;revolution&#8220;. Besides, the authors of the UK report don&#8217;t agree at all with that. As ChatGPT would have it, let&#8217;s &#8220;delve&#8221; into those reports and find out more. GenAI impact on jobs: boon or doom? What impact will GenAI have on jobs in Europe? The answer to that question unmistakeably depends on which job and which associated tasks you are talking about. The French Commission on AI reassures us on this point &#8211; photo: a tailor’s workshop in the 10th arrondissement of Paris &#8211; photo Yann Gourvennec antimuseum.com. Our own empirical analysis suggests a positive effect of AI on employment in companies that adopt AI, because AI replaces tasks, not jobs. In 19 out of 20 jobs, there are tasks that AI cannot perform. Jobs that can be directly replaced by AI would therefore represent only 5% of jobs in a country like France. What&#8217;s more, the generalisation of AI will spur job creations, in new occupations as in old ones. To sum it all up, some industries or geographies could experience net job losses, therefore requiring Government support, but this does not mean that AI will have an overarching negative effect on national employment in France. French Commission on Artificial Intelligence report, March 2024 &#8211; p. 41 Anxiety in the eyes of some of my younger students I often talk to young students from all areas about the impact of GenAI on jobs and careers. I often sense a bit of reticence and even anxiety in them at a time when young adults are still asking themselves many questions about the future and aren&#8217;t necessarily clearly determined about what they want to do in the future. Beyond that, the current state of hype around GenAI further blurs these students&#8217; vision by making them feel the weight of an uncertainty that is already difficult for some to stomach. The impact of generative AI on employment is not easy to assess. And we&#8217;ve had to struggle with our image-generating AI tools to get them to avoid a doomsday view of the future of work with robots everywhere&#8230; What if, in the end, the future of work was a mere evolution of today&#8217;s work practices? Image generated with MidJourney Recent reports have added fuel to the fire, such as this one from the IMF. Almost 40 percent of global employment is exposed to AI, with advanced economies at greater risk but also better poised to exploit AI benefits than emerging market and developing economies. In advanced economies, about 60 percent of jobs are exposed to AI, due to prevalence of cognitive-task-oriented jobs. A new measure of potential AI complementarity suggests that, of these, about half may be negatively affected by AI, while the rest could benefit from enhanced productivity through AI integration. IMF 2024 report on AI and the impact on employment and the future of work The British government has also published a report on this subject. Its more task-oriented approach is a little more nuanced, but still fairly unappealing. Advances in Artificial Intelligence (AI) are likely to have a profound and widespread effect on the UK economy and society, though the precise nature and speed of this effect is uncertain. It has been estimated that 10-30% of jobs are automatable with AI having the potential to increase productivity and create new high-value jobs in the UK. Gov.uk report on the impact of AI on jobs, Nov 2023 It&#8217;s worthy of note, however, that the authors are resorting a great deal to the conditional tense. This undoubtedly urges us to interpret these results with caution. A more nuanced report on the impact of AI on jobs The French report is much more nuanced and refers to a large number of interesting studies, starting with the one by Antonin Bergeaud (an economist and professor at the Paris H. E. C. School of Management), from which I extracted an important schematic. The approach of the French report makes a clear distinction between GenAI and AI, and even automation in the broad sense (i.e. aimed at the manufacturing industry). It&#8217;s a distinction that seems crucial to me, given the many misconceptions linked to the measuring of the impact of AI. Which AI? Generative AI? Machine Learning, deep learning, neural networks? Or even just plain good old IT, unless we are mentioning robotisation, automated supply chains&#8230; In short, AI is everything and everything is AI. That seems to me a silver bullet for generating panic among the general public and especially young students who are trying to find their way in the future. A More Thorough and Subtle Report The French report is therefore more precise than the others I&#8217;ve read, in that it makes a clear distinction between GenAI and the others. It is also focusing on tasks rather than jobs. This approach has also been that of the British government. Impact of AI on jobs: Antonin Bergeaud&#8217;s projections are extremely smart and way above my mathematical abilities. In the top left-hand corner one can see the jobs of accountants and telemarketers, professions of which I&#8217;ve been reading about the disappearance since the 1980s (accountants) and 1990s (telemarketers). It&#8217;s bound to happen one day, but is ChatGPT to blame? It&#8217;s doubtful, and you don&#8217;t need a PhD in Quantum physics for this &#8211; diagram taken and adapted from Antonin Bergeaud&#8217;s report. The report is in disagreement with previous approaches, pitting them against each other and pointing out that, in the end, there may be no need to panic: This approach using the exposure of tasks, vs jobs, to GenAI makes it possible to estimate aggregate effects at the level of the economy as a whole, and to allow comparisons between countries. However, it has several limitations. Here are the two main ones. On the one hand, it is a static approach: the studies are based on existing tasks and therefore do not take account those tasks that could be created as a result of the development of AI [&#8230;] On the other hand, it is based on an estimate of the probability of different tasks being replaced by AI (see above diagram). In short, even if you think it&#8217;s a better approach, thinking of the impact of AI in terms of tasks isn&#8217;t really possible. It&#8217;s like painting a picture of a landscape from the window of your intercity train at 100 miles per hour. On top of that the painter has left his glasses at home and is therefore making assumptions about whether he should add cows, or sheep, in the meadow in his painting. [&#8230;] overall, the deployment of AI in the economy should have a positive effect on the number of jobs. Catastrophic predictions about the end of work are no more credible than similar predictions made in the past. Especially as even the task-based approach represents the upper limit for the impact of AI. Indeed, it makes the assumption that it is profitable to automate all the tasks that can be automated. But this assumption is far from being true today. The diminishing cost of AI systems and the possibility of distributing the same AI system to a very large number of users will be key factors in determining the impact of GenAI on tasks and jobs. Antonin Bergeaud In conclusion, if the result is not negative, it must be positive, even if it is undoubtedly just as difficult to prove as the opposite. Five percent impact of GenAI on jobs&#8230; why not 5.2%? As for the 5% figure announced in the French report (see the quote above), I suppose it should be taken as an order of magnitude. There is a nuance added to the report in that respect. The authors mention that these 5% may vary from one occupation to another. What I take from this is that for the vast majority of occupations, this figure of 5%, is probably not to be taken at face value. Some occupations will not be affected by artificial intelligence at all, especially generative artificial intelligence. This doesn&#8217;t come as a shock to us. It takes us back to our work on jobs in 2030, where we already showed the prevalence of non-automatable occupations (surface technicians and others) in the most sought-after professions. Automation Is neither Easy Nor Happens Overnight Occupations that are apparently easy to automate, such as bookkeeping, for example (if we fail to take its more consultancy-like aspects into account) have been on the chopping block for years. But despite the doomsday predictions, including our own, it has to be said today that the jobs of chartered accountants remain among the most in demand. Yet all the technology is available to automate both bean counters&#8217; tasks and data transmission. Nowadays, almost all invoices are dematerialised even though they are only unstructured PDF files. And yet most of the work of accountants remains manual whether we like it or not. Whether it&#8217;s ticking boxes between reconciliation systems or copying figures into a general ledger. The change lies mainly in the declining technical nature of the job. Ditto for banking. Experts have been naming banks dinosaurs for years. Here again, we have to make amends. And yet there have been many restructurings, and they didn&#8217;t wait for OpenAI&#8217;s ChatGPT and its clones. But here&#8217;s the thing: changes don&#8217;t happen overnight. Besides innovation in organisations isn&#8217;t governed by wizardry but resistance to change. Finally, let&#8217;s return to an occupation that was in the top left-hand corner of Antonin Bergeaud&#8217;s schematic. I mean that of secretaries. An occupation that has already been largely transformed since the 1990s. It has also been steadily declining to the point of disappearance at least in the United States (they only amount to a fraction of European employees now, i.e. a small proportion of 19% of all jobs). And yet, the impact of artificial intelligence between the 1980s and the year 2000 was bound to be close to zero. I should know, I was in charge of an AI project in those days. In that same period, though, I witnessed and even played an active role in the boom of the deployment of IT in businesses. GenAI and jobs: looking at the big picture We therefore need to get back to these forecasts with a critical look. Starting with those of the IMF. And this report by the French Committee on Artificial Intelligence deserves credit for playing down the most hairy-fairy statistics on this subject. In conclusion, after reading all these reports, the future isn&#8217;t any more predictable than it was before that. We might even venture to say that we are even more confused. Admittedly, as the authors of the French report point out, we are already seeing, and will continue to see, employees that are made redundant in professions where business models are already being jeopardised by ICTs, such as journalism. But is this sufficient for us to reckon that what we are going through today is a &#8220;revolution&#8221; in terms of employment? There are no indications on this. All we could surmise is that a minority of jobs will be hit — be it 5%, less or more. Time will tell whether this figure or that of the International Monetary Fund was the right one, but I&#8217;m inclined to believe that the ballpark figure quoted by the French Artificial Intelligence Commission is closer to reality. Predictions lie but figures don&#8217;t Finally, to end on an intellectual note, let&#8217;s quote Vaclav Smil in his book Numbers don&#8217;t lie. Being realistic about innovation Modern societies are obsessed with innovation. We are to believe that innovation will open every conceivable door: to life expectancies far beyond 100 years, to the merging of human and machine consciousness, to essentially free solar energy. This uncritical genuflection before the altar of innovation is wrong on two counts: It ignores those big, fundamental quests that have failed after spending huge sums on research. And it has little to say about why we so often stick to an inferior practice even when we know there’s a superior course of action. Vaclav Smil, Numbers don&#8217;t lie It&#8217;s this last sentence that I think is important. All forecasting exercises start from an assumption: that which state that when a technology improves our lives, it&#8217;s bound to be implemented. It may seem like a no-brainer at first glance. What I have learned in the field throughout my career, however, is that when a solution is better, especially when it is better, resistance to change is all the greater. And it&#8217;s rarely the most obvious and cost-effective solutions that win. Especially because human decisions are seldom rational. Thus, assuming that generative AI is without contest a boon to productivity gains, a theory I&#8217;m not at all sure I buy into, it would be wrong to believe that the mere fact that it exists guarantees its rapid and universal implementation. Here again, time will be of the essence. download the 2024 IMF report on the impact of AI on jobs download the UK report Nov 2023 on the impact of AI on UK jobs download Antonin Bergeaud&#8217;s report [in French] from 2024 on the impact of AI on tasks and jobs download the 2024 artificial intelligence commission UK report donwload the 2024 report by the French commission on AI The post GenAI impact on jobs: doom or boon? appeared first on Marketing and Innovation.

Whether it&#8217;s music and AI, or innovation in the broad sense of the term, at Visionary Marketing we like to look back in time. A few days ago, while doing the housekeeping of some of our 3,000 articles, we rediscovered this post by Mia Tawile written in July 2016. Eight years is the equivalent of 8 dog years on the Internet, to use that hackneyed motto from the early days of the Web. That is to say, 64 years and 3 months. And at a time when Suno is sending shivers down the spines of every musician on the planet wondering what will become of them, there are two lessons to be learned from this post that demonstrate that we don&#8217;t understand the history of innovation as Scott Berkun would have it.  Music and AI: Back to the Future What better than an image, reminiscent of the 1970s for this evocation of the first attempts to compose music with computers &#8211; music and AI nonetheless take us down a much more tortuous path. A real philosophical, artistic and economic challenge for creators &#8211; image produced with Midjourney and retouched and enhanced with Photoshop and Firefly Beta. &#8220;Hello dear human friends&#8221; is the introduction to this striking video by Laurent Couson, a French composer who analysed the capabilities of Suno, a popular application that lets you compose music of almost any style in 30 seconds. &#8220;Before, you had to learn music theory, orchestration and instrumentation &#8211; at the very least, ten years of practice &#8211; to become an accomplished composer,&#8221; he continues. He could have added, &#8220;Provided you&#8217;re gifted.&#8221; 900,000 Pieces of Music per Day This piece of software,&#8221; he went on, &#8220;generates 900,000 pieces of music a day, while no well-known composer, not even the most prolific, has 1,000 in his catalogue&#8221;. And it&#8217;s true that the results are amazing. 900 000 pieces of music being produced every day. Imagine that! Midjourney dit it for you (with our help) We went there too, and as rumours of the death of cyberspace grow louder, we decided to launch Suno on this theme with a song entitled: The Dying Cyberspace. [Verse] In a world of bytes and tangled wires The cyberspace that once glowed with fire (with fire) Now fades away, its brilliance lost As darkness falls, at such a cost [Verse 2] Once a realm of endless possibility Now echoes silence and fragility The Internet, a dying art Fading now, tearing us apart [Chorus] Oh, the dying cyberspace (cyberspace) Once so full of life and grace (life and grace) Now it withers, slowly dies (slowly dies) Leaving us with empty skies (empty skies) https://visionarymarketing.com/wp-content/uploads/2024/09/the-dying-cyberspace.mp3 A very basic prompt Admittedly, the lyrics are a bit cheesy, but considering the time spent (less than a minute), the result is more than satisfactory. All the more so as the prompt used was really minimal. A song on the death of the cyberspace, neoclassical The possibilities are endless with this tool, you can even invent Russian songs in Post-Punk mode. And if you ask Deepl to translate the lyrics, you&#8217;ll realise that they&#8217;re pretty creative. Maybe not on par with Pushkin, but certainly well above the average of what you hear on Spotify (well I can only surmise because I subscribed to Qobuz). An enamelled vessel A window, a bedside table, a bed It is difficult and uncomfortable to live But it&#8217;s more comfortable to die Эмалированное судно Окошко, тумбочка, кровать, &#8211; Жить тяжело и неуютно Зато уютно умирать (I can&#8217;t guarantee the translation from Russian into English, so I&#8217;ll take deepl&#8217;s word for it). According to popular belief, music is linked to mathematics, even if this interconnection is not completely proven. As a result, it&#8217;s not totally astounding that a computer manages to do this. In fact, computers have been making music since PopCorn (1969). Note that the dancers are slightly out of sync, no doubt baffled by the technological prowess of the end of that decade. The First Computer-Generated song I remember well, when I was 7, the announcement of this song on the radio: the first song produced by a computer. It was extraordinary, and way ahead of its time. But AI in music also raises a whole range of questions: First of all, the machine has virtually every style at its disposal. You can ask it to imitate one without having to master it and especially not by working for 10 years. This raises the question of the value of creation. How much could we pay Mr Couson to produce a song like this? We could even go further, and get Suno &#8211; or its clones &#8211; to compose a symphony or an opera. It might have to go through a few steps, but it&#8217;s a lot less tiring than inventing Einstein on the Beach or Die Zauberflöte from scratch. And the associated question: if there is no longer any value in creating music, how many musicians will still have a go at it? There also arises, and this is the third point, the question of creation itself. If it&#8217;s so easy to create music that isn&#8217;t all that bad, aren&#8217;t we in danger of going round in circles? Also, can we innovate, in content and form, if the basis is a collection of existing music data? Won&#8217;t the novelty wear off? Some would say that this is already the case to some extent, I suppose, but this will just finish the job. The training data for these programmes is based on the work of hundreds of thousands of musicians over hundreds of years. It is &#8211; as with Midjourney &#8211; the plunder of our cultural heritage that raises the question of the protection of intellectual property. Or rather, it might make such IP redundant, unless legal proceedings are successful (but justice is slow, and AIs are fast). It also raises the bar for tomorrow&#8217;s content creators who will want to show their creativity and beat the machines. This will really demand a lot of imagination. Democratisation or the end of creation? When everyone becomes a creator, does that mean that creation no longer exists or, on the contrary, that everyone has become a true creator, even without talent? And is pressing a button and waiting for a program to produce a result a creative act? Is &#8220;prompting&#8221; sufficient? Tomorrow, will humans become the blue collars of artistic creation whereas machines produce all the thinking? There are many questions raised. And the undeniable fun that one can have when dealing with this type of programme should not allow us to forget about them. What&#8217;s more, it&#8217;s a guilty pleasure. If we are endowed with a conscience, it&#8217;s hard not to feel, as with tools like Midjourney, one feels as if one were faking artistry. IA and music: a long-standing innovation From Gershon Kingsley to Wally Badarou (who was composing on the Mac in the early 90s) to Klaus Schultze (and his fabulous Ludwig Zwei von Bayern with his fully synthesised string orchestra in 1978) or Zoe Keating, who records and plays her sound loops thanks to a pedal connected to a MacBook Pro, artists&#8217; experiments with computer music have been numerous. But producing music with AI goes a step further. However, here too, these attempts are not recent. Digging around on this site, we found an old article by Mia Tawile written in 2016 about a Google project named Magenta and of which there are still a few scattered traces on the Internet. Interesting Examples There&#8217;s also some pretty interesting music here, provided by artificial intelligence, the fruit of Google&#8217;s early work in this area. The results are promising but without a future, like so many aborted attempts by this Internet giant, which seems so focused on its business model. So much so that it may be suffering from the innovator&#8217;s dilemma. https://visionarymarketing.com/wp-content/uploads/2024/06/magenta-mix.mp3 Example of chamber music produced by Magenta. Not quite Haydn, but it sounds a bit like it (Midi style). My optimism leads me to believe that we will still need Mr Couson and his colleagues. If only to host concerts. Of course, in these artistic performances, it would not be surprising to find a few computers and loops invented by AI. In this respect, these artists will no doubt be the worthy heirs of the pioneers I mentioned earlier. After all, didn&#8217;t musicians like Wim Mertens and Philip Glass imitate repetitive computer music with real instruments? And more recently, haven&#8217;t Nils Frahm, Nicklas Paschburg or Grandbrothers included these technologies in their music to the point we end up forgetting about them? Creators always find a way of circumventing issues like these. The 2016 original post on AI and Music Below is Mia&#8217;s post from 2016. My two cents about this with nearly 10 years of hindsight. Innovation tales time. And no, it won&#8217;t happen overnight (lesson number one); Google missed the boat (again I daresay), even though the transformer researchers were working for Alphabet at the time. Enjoy the AI Time Machine. We have all heard of Mozart, Chopin, and Beethoven, but not all of us know Google&#8217;s artificial intelligence and its ability to generate music with AI. Yes, a robot has joined the club. And yes, it plays music. (If the song We are the robots by Kraftwerk is playing in your head right now, it is completely normal, don’t worry.) This new robot/artist that creates a lot of debate is called Magenta. You might have seen in my previous article about Facebook&#8217;s artificial intelligence how machine learning works on images and videos. This article will describe a concept that is similar yet different. The main question here is: Can you use machine learning to create a music piece? That&#8217;s exactly what I will touch on in this article. Google&#8217;s Magenta and its music band AI music is on Google&#8217;s agenda Magenta is Google&#8217;s Brain Team project that answers the question mentioned above: Can we use artificial intelligence and machine learning to play music? Two goals Google developed this project with two goals. The first is to explore machine learning even deeper and take this concept further. Indeed, this type of artificial intelligence has been used to recognise pictures, speech, and translate content. Facebook too has a similar algorithm that has been used to help blind people hear their newsfeed. This feature is called Facebook Read. No plans yet for Facebook on the AI music front but they are using AI so that blind AI music according to Mia TawileFor Artificial Intelligence researchers, the sky is the limit. They always look for new features to develop, and new ways of developing machines. So why not create algorithms and teach machines how to play the piano, for example? Robots are good students. Indeed, blind tests have shown that people have been fooled by machines: Peter Russel, who is a musicologist, listened to a music piece played by Iamus, a classical music robot. Surprisingly enough, he did not know it was created by a machine. The second objective of this Magenta project is to build a community with people interested in music and technology such as artists, coders and researchers. Google is inviting people who are interested in this project to join the community. Actually, a part of the project is accessible to the general public, and is waiting for people’s input. AI Music: Beyond Limits A lot of people are scared of such technological growth. Well, they might be right. When machines start recognising pictures, and videos, and describe them to us, it means that technology is taking these robots beyond their limits. The good news is that there is a use to this technology. It is not only developed to win a challenge, or defy the limits of research and technology. As I mentioned earlier, Facebook uses artificial intelligence to expand its community, without excluding anyone. When it comes to artificial music, some applications identified this new trend and worked around it. There&#8217;s a mobile app called @life that plays music according to your state of mind and your mood. Y ou might ask yourself, how does a machine know what one is feeling in real time? The machine gathers information about the person’s behaviour or their location and analyses their mood. Some data analysts use Instagram filters for example, to identify the user’s mood: dark colours reflect sadness, whereas bright colours represent happiness. This music mobile application is said to help people in pain by distracting them and using the popular benefits and virtues of music. Maybe machines will help us create new music genres, by combining different algorithms. Or maybe this new invention will help people manage their stress or heal their pain, music being the cure to everything! We can see the bud of that technology today with Spotify that can detect your running speed and adapt the music type and tempo. The post Music and AI: Back to the Future appeared first on Marketing and Innovation.

Thomas Deneux is the founder of Learning Robots whose aim is to help pupils, students and businesses to learn AI, with the help of home-made self-driving gizmos. These little machines on two wheels are more serious than you&#8217;d think. They are all about the teaching of advanced computing. Thomas described his philosophy to me during this interview conducted at the heart of the Neuroscience Institute of the CNRS (French National Centre for Scientific Research). In essence, a no-nonsense approach to teaching and learning AI.  When AI and robots join forces to teach artificial intelligence Behind Learning robots’ self-driving gizmos &#8211; seen on their training track here in Saclay &#8211; there is a teaching philosophy and a full-fledged training corpus. Who are these friendly colourful robots? We&#8217;re overwhelmed with social media posts and news about AI. Often, pundits will tell you that you need to know how to use ChatGPT and make prompts. That&#8217;s all very well, but we must free ourselves from the tech giants who build these models. At Learning robots, we want to spark vocations among people who are interested in finding out how it works and want to use AI better. What is artificial intelligence anyway? AI gave birth to these fantastic tools and programs. Yet, at the same time many people are scared. Our aim, with these user-trained robots, is to make AI accessible and friendly. What&#8217;s behind these robots? In our introductory activities, the user drives a robot as if it were a remote-controlled car. But behind this robot is an AI that will record all the necessary data. Next, the robot takes over from the user in autopilot mode and drives around the circuit. Then we organise a race between the robots that have become autonomous in this way, and users may therefore observe that not all of them will perform equally well. It&#8217;s natural because performance depends on the quality of the training. The aim is to make people understand that AI machines do not become &#8220;intelligent&#8221; out of the blue. Behind AI, there are humans who have gathered data. And AI will only be as good as its data. We&#8217;ve been training our Midjourney AI to produce an infographic based on Thomas&#8217;s interview and here&#8217;s how it came up with these AI self-driving car races&#8230;. This one isn&#8217;t as nicely organised as those by Learning Robots. Today&#8217;s AI is still at the stage where it reproduces patterns. It&#8217;s a mere &#8220;stochastic parrot&#8220;. The stochastic parrot as seen by Midjourney, who is definitely very creative. In the early days of AI, there were expert systems, which worked with ever more sophisticated knowledge bases. Then we realised that rather than predicting all the potential situations, we could simply feed the AI with samples based on existing data sets and implement self-learning algorithms. With large language models (LLMs), humongous quantities of text have become available. So much so that AI has become capable of generating text by itself. But the principle is the same: the basis is those samples provided by humans. With AlphaAI, everything is very simple. A sensor will tell the machine what to do, for example turn left when there is light on the left. Or turn right when there is light on the right-hand side. This helps users understand the basics. After that, it&#8217;s just a matter of scaling up to more advanced AI. What prospects can we expect from this kind of robot? When you interact with a Large Language Model (LLM), you are essentially producing text, even though you could also generate images, music, videos, etc. Robotics is the future of AI But what I see emerging is that the future of AI is about robotics. The Figure start-up has just raised $675 million and has signed agreements with OpenAI, Microsoft and Nvidia to develop humanoid robots. It&#8217;s flavour of the month. Our role is not to enter this competition, however. Small but powerful. The AI robots by Learning robots &#8211; source Leaning Robots Our vocation is educational. We want everyone to be able to get to grips with these technologies. Our aim is to enable people to train their own AI, so that they can easily develop their own ideas, such as home automation projects for instance. And also make AI accessible to SMEs. Our development plans could evolve in the future to move away from teaching and training, towards a plug and play solution for introducing AI and automation into the business world. What is your philosophy behind all this? I&#8217;m a technophile, yet I&#8217;m not at all a techno enthusiast. I think there are some really pertinent questions being asked. And that&#8217;s why I think we need to focus on training and education. We need to keep as many people as possible informed, to debunk all the myths about AI. On the one hand, AIs have their limits; Secondly, users feel immediately more comfortable with a tool after getting to grips with it. AI can be fun Let me tell you about an anecdote. We work with a well-known luxury goods company in Paris, France, for whom we run autonomous robot races. Their employees train their robots for the race. The first feedback from learners on these training courses is: &#8220;I&#8217;ve realised how much AI is fun!&#8221; It&#8217;s true that digital tools also have their downsides, such as creating addictions. But if you get to grips with them, you can achieve great results. We need to evangelise about AI adoption, there are so many exciting potential applications for it. I&#8217;m involved in a number of AI think tanks and I&#8217;ve realised that what the general public expects from researchers is to be told what the future will be. In fact, it&#8217;s very hard to predict the future. Innovation is about trial and error. Sometimes its adoption is faster than we think, at other times it&#8217;s not. Always the unexpected happens. Can we imagine a world, where chores are all carried out by machines? I think so. We&#8217;re already seeing it in the home construction business. Tomorrow, it&#8217;s very likely that AIs will be performing a certain number of tasks. However, I hope there will still be room for humans&#8217; creative skills. For instance, manmade products are highly valued by consumers these days. Mass-producing widgets is easy. But creating something unique is more rewarding. There will always be room for human creativity. Finally, there is hope for human beings I think so. Some people are depressed because they think they are going to be dominated by AI. But look at self-driving cars: they were supposed to be ubiquitous by 2010, and it didn&#8217;t happen. But we shouldn&#8217;t be wearing our rose-coloured spectacles either. Both citizens and politicians need to get to grips with the issues related to AI. As far as I am concerned, I remain optimistic about what can be achieved with these tools. The post Learning AI with the help of robots appeared first on Marketing and Innovation.

Walking down the roads of strategic thinking for professional networking or business building, most of us would agree to name LinkedIn as a well-built pathway to tread on. What’s better than understanding the dynamics of LinkedIn as a professional networking platform from someone who trains consultants and is an expert regarding that platform. Daniel Alfon from Tel Aviv wrote the book entitled, “How to Build a LinkedIn Profile for Business Success&#8220;. For most of us in the B2B marketing or consulting space, it’s a great read on what LinkedIn has become and where it&#8217;s going. I interviewed Daniel to find out about the future of LinkedIn, and of business networking at large. Networking and Growing Your B2B Business with LinkedIn Networking is the essence of Linkedin, so let&#8217;s get back to basics with Daniel Alfon, author of “How to Build a LinkedIn Profile for Business Success.&#8221; Note; this post was first published in October 2021. Tell us about what LinkedIn really is for B2B business folks. LinkedIn networking LinkedIn is what you want it to be for your business. If you&#8217;re a hypergrowth company and all, you&#8217;re interested in is finding talent, then you should focus on sharing jobs and ensuring that your existing employees are using their network to tap into candidates. If you are a content publisher or a marketer, then you should focus on the networking aspect of it. The strength of LinkedIn simply comes from deciding what you would like to leverage the platform for. This simple, straightforward question is one that a lot of LinkedIn users do not ask themselves. What is your top marketing priority: recruiting, publishing or getting more views? Or, do you need referrals? Stop piling up content, think networking when publishing on LinkedIn! It could be that you went there almost exclusively for publishing content, but now you could use it for networking. If your marketing department is producing high-quality content, then leveraging LinkedIn to get more exposure for it is a great idea. It requires some coordination, and it can&#8217;t be top down. How do you establish business success in B2B, is it being able to sell more? One way to look at it is at the top of the funnel. Transactions would not happen on LinkedIn. However, it could be the platform where more people are made aware of your solution via a webinar or a demo. Then, they get in touch or something else happens outside of LinkedIn. You want people to find your solution, and within seconds understand that it is something interesting. When I look at your profile and I see that we have a number of mutual connections, then what I think about those people will affect and influence the way I think of you. If I think highly of them, even if I don&#8217;t ask them about that at all, then something of some of the stardust would actually be above your head. LinkedIn: a battle for visibility or a tool for B2B networking? Lately, while I was doing a training session for KPMG, I had mentioned three key points which I think could be relevant. When an employee of your firm shares or likes or comments, there are three dimensions to it. One, he is about the way others perceive that action. Let&#8217;s say that we are connected, and I see that you like some content. In many cases, ‘liking’ is perceived to be the lowest trust characteristic of your engagement. It doesn&#8217;t mean that you even read it. It could simply be that you like the person who published that content. Sharing is more valuable in the eyes of many people. But if an employee is doing it, then I would encourage her to not just share, but add something of her own. This could be a sentence with an explanation of why it’s so compelling or interesting or why you disagree with the author&#8217;s claim. So if you decide to share someone else&#8217;s content, then that means you think highly of it. It makes sense to take a few more seconds and add your own perspective. Now, it becomes more genuine and interesting for the reader. The third is the algorithm. Since it is the darkest secret of LinkedIn which it never shares, we are only left with the other two elements. Some inside information says that LinkedIn doesn&#8217;t think highly of shares because it considers it as duplicating content, not marketing. Re-shared published content by employees may get limited additional views, whereas if you only liked it or commented about it, LinkedIn will show it to more people. Let&#8217;s say we have an employee who is happy only re-sharing stuff, and does nothing else. Then even in the eyes of the algorithm, it’s not worth showing to five hundred extra people. We would be better leveraging the actions that people are organically willing to do rather than trying to chase the algorithm. It is going to change anyway in the next few days or weeks. POLLution? on Linkedin [Visionary Marketing] David Hughes, who has a huge following on LinkedIn, posted something on LinkedIn about what he calls pollution polls. There are polls everywhere. Are we going to have to send polls all the time or are they going to change again? Tell us what does that mean? It&#8217;s so nicely introduced that at one point people are going to be fed up with 99% of the polls. We may have already reached that point. Let’s focus on being interesting for our B2B customers and audiences. Before we pull the trigger of a poll, we should ask ourselves, why are we sharing this? There could be a number of reasons for it. Try and suggest a simple flowchart before sharing the poll. Is this content with the question so compelling that many of your connections will feel intrigued to go and see the answer in the vote? I am afraid that most people and content creators will not find it easy to produce such content on a daily or even weekly basis. So, one interesting poll a month or a quarter would give you more engagement than five polls every week. Daniel&#8217;s Advice to Consultants for Business Networking [VM] People like us put in all our efforts and create a 70-page white paper. But when we post it on LinkedIn, we can’t get a certain number of views unless we insert a poll. When experts are striving, what is your million dollar advice to the average consultant out there? Let me try and give you a number of nuggets and maybe all of them together will form the million dollar answer. What you should post on LinkedIn is something reverse engineered. In other words, find a quote, highlight an image and you do something that&#8217;s more the user-friendly. People are not necessarily going to consume the content as it is seventy pages. When you try out what I have suggested, you will see that half a dozen interesting quotes or figures or something else is going to fetch a lot more responses and engagement than say, page 43 in the white paper. About ‘snackable’ content on LinkedIn When you share a snack piece of content, the idea is not for people to consume it on LinkedIn, but for more people to discover the content itself. Let me suggest two other ways. The initial thought that all of us have is to go to LinkedIn home page and share our fresh content. But when you&#8217;ve spent so much time creating it, then there&#8217;s another place to house it. If it is evergreen content, then it could be part made part of your profile &#8211; the consultant&#8217;s profile. This would ascertain that everyone who would visit your profile shall see that PDF or the cover of that PDF. That alone in the long run, will help you gain more downloads or views than trying to share it across the board. Lastly, if we don&#8217;t want to rely on our connections only, then there&#8217;s another feature LinkedIn offers: LinkedIn groups. For every consultant or prospect you are connected with, there could be dozens of other interesting prospects you are not going to connect with, but who could consume that content. Here’s when groups come in handy. Use your profile and your staff’s to highlight evergreen content. Not next week&#8217;s webinar, but something that you&#8217;ve worked hard for. That could be a way to gain a lot of exposure. The second idea would be to make sure that everyone in your team is sharing it. That too, should be done at different times. Going back to the good old days of LinkedIn networking Should an average consultant or salesperson, who doesn’t work in a team necessarily, go back to networking and square zero and start building a network or a community? Yes, he should. One can try adopting LinkedIn creative mode, even though that has some problems, or have a connection strategy. Many people don&#8217;t have a connection strategy, they merely react. When you send them an invitation, they accept it or not by thinking about it in the short term. Because now I am doing this, so I would accept the invitation. But if six months ago I was into something else, I wouldn&#8217;t have. It&#8217;s important to get back to the networking aspect of things. Networking is far more important than LinkedIn in my eyes. LinkedIn is a formidable and powerful tool for sure. There is no silver bullet, though, no shortcut to run your networking. There&#8217;s no way anyone has found on LinkedIn or outside of LinkedIn to make it automated, easy, free and successful. The question about how an average person can differentiate his post from ten thousand other blogs was there before LinkedIn. What makes a blog special? So what makes their blog special in the first place? Finding that special niche would mean that the consultant only focuses on maybe one percent of his audience and still be able to make a living. Everyone should think of themselves as at least a nano-influencer in their specific niche [VM] So networking is the real issue for an average consultant. He has to realise that one needs to grow a community before he starts selling stuff. You spot an invitation on LinkedIn, and then the person tries to sell stuff immediately afterwards. Consultants who are able to help or educate their audience are in a better position than others who don’t I don&#8217;t think one should have a LinkedIn personality per se. After all, our personality in real life exists. LinkedIn can, however, reflect the better aspects of one’s professional identity. But as the saying goes, you can&#8217;t fool everyone all the time. In other words, if your LinkedIn personality is all shining bright and people who meet you in a Zoom call or in person think you&#8217;re a jerk, then what wins is real life because they will tell everyone about it. When I see that you and I share a mutual connection, then both of us can actually reach out to him and ask pertinent questions about the other person. If he tells me that I should speak to you, and if I trust him, then even if you LinkedIn profile is nowhere near good, I would still do it. I&#8217;m not interested in your LinkedIn presence. I&#8217;m interested in you or your business. This information can influence the way people think about you. Eventually, it dictates if they wish to do business with your company or not. On this note, if I want to wrap up in a few words, at the end of the day, what matters is not how you master LinkedIn. What matters is how good a human being you are. For those of our readers who would like to read more about LinkedIn and succeeding in their business through it, can get their copy of Daniel’s book from Amazon. Happy networking! The post Networking and Growing Your B2B Business with LinkedIn appeared first on Marketing and Innovation.

What are the most outstanding new features of LinkedIn in 2024? Reid Hoffmann&#8217;s professional network was created almost 21 years ago (in May 2003) and acquired by Microsoft in 2016. Visionary Marketing invited Bruno Fridlansky to talk about this platform, of which he is an expert. Together we were able to answer a few basic questions about using the tool and review some of its latest features. It&#8217;s worth noting that Bruno isn&#8217;t ecstatic about most of them. The New Features of LinkedIn Under the Microscope We&#8217;ve been using LinkedIn professionally for 20 years, a tiny bit less than the age of the application, which will celebrate its 21st birthday in May 2024. Nevertheless, this gives us a lot of hindsight on the use of the leading business-to-business application, which over time has managed to do away with all its competitors. LinkedIn &#8216;s new features: working with our favourite AI tools, we headed for a benevolent illustration that describes an optimistic and innovative corporate world. At the end of the day, Midjourney (with a little help from ChatGPT) got it right. LinkedIn is a bit like a business district where you walk around and pick up information, but also where you meet people. All in all, even if we are fairly critical of some of LinkedIn&#8217;s new features, particularly those related to AI, on the whole we remain very keen on this platform, which has no competitors. LinkedIn gone rich and the enemy of reach As a key player in this sector, LinkedIn is a flourishing platform with annual revenues of more than $15 billion. What is particularly remarkable is the rapid growth in recent years of this turnover, which has even almost doubled since 2020. A business volume that, compared to Microsoft&#8217;s total revenues is still small (around 7% of the Redmond firm&#8217;s $211 billion), but substantial in the world of social networks, even with a very business-to-business positioning. To give you an idea, it&#8217;s just over 10% of Meta&#8217;s turnover in 2023, but around 5 times as much as the advertising revenue generated by X in this period. LinkedIn&#8217;s surprisingly expanding turnover probably has little to do with the advent if new features on the platform, but much more to do with a business model that has found its audience and the fact that there are no competitors today But beyond these staggering figures, there are a number of questions to be asked. First of all, there&#8217;s the recurring interrogation about &#8220;reach&#8221;. Many users are wondering how and when their publications will be seen and by whom&#8230; LinkedIn is rich but its reach is poor A question that is increasingly hard to answer. So much so that the platform seems to be playing a game of cat and mouse with its content creators. Recently, a large number of the latter has opted for content creation techniques favoured by B2C influencers. Selfies flourished but have been heavily criticised on the B2B network. This trend seems to have been halted, Bruno Fridlansky confirms. Some of us continue to complain that many self-focused publications are still populating their feeds, however. Perhaps the measures put in place by LinkedIn are not being deployed in the same way or at the same time for all users, which seems to be customary according to Bruno. But that&#8217;s not the most important philosophical question: what is a tool made for, and ultimately, in this platform economy, are these social tools at our disposal, or have we become their slaves? Bruno&#8217;s answer to this question struck me as particularly apt: It&#8217;s a platform that was originally made available to us but now we are the ones feeding the beast. Beyond these considerations, most readers are interested in the tool&#8217;s new features. No matter how hard we try, repeating over and over again that a tool is just a tool, we might as well bite the bullet. Hence our review with Bruno of the various LinkedIn features that have been added to the tool recently. Bells for a Better View of Content on Your Network LinkedIn added a feature some time ago, a bell icon, that you can activate if you don&#8217;t want to miss anything posted by someone you follow. But things aren&#8217;t quite as simple as that. First of all, try and click on your 25,000 followers&#8217; bells! Good luck with it. But that&#8217;s not all. The bell is one of LinkedIn&#8217;s new features, but not everything works exactly as it seems. The bell doesn&#8217;t always work as planned. It&#8217;s just supposed to send a notification about a publication. And I&#8217;ve seen comments that, when you exceed 20 or 25 bell activations, it grinds to a halt. The bell won&#8217;t solve our reach issue it seems. LinkedIn expert Bruno Fridlansky gave us his thoughts on LinkedIn&#8217;s new features &#8211; Photo by Yann Gourvennec antimuseum.com New LinkedIn Features: Certified Profiles Recently, LinkedIn introduced user verification via third party to its users. Bruno, as well as yours truly, has a verified account. This feature, according to Bruno, has not been fully deployed across Europe. Clear Secure is the US company used by LinkedIn to establish identity verification. Its presentation on its website is not the clearest or most transparent. To find out more go to good old Wikipedia which tells us, I don&#8217;t know if it&#8217;s reassuring that this company first went bankrupt six years after it was founded. To validate one&#8217;s account, one has to enrol with an American-based service that certifies one&#8217;s identity. This entity is based outside our European countries. Above all it is vulnerable, like all technological activities, to cyber attacks that could make our confidential information and personal identities visible. It should be noted that this observation applies, and perhaps even more so, to European-based identify verification services such as the Gov.UK ID check app or France Connect + in France and other European equivalents. Just because these services are based in the UK or Europe is no guarantee that they will ever be targeted. Maybe we could have thought about that before getting our accounts validated. Clicking before thinking is never a good idea and we all fell for it. It doesn&#8217;t take much stretch of the imagination to wonder how a cybercriminal could use our identities and steal them to commit a crime. If I had to do it again, I&#8217;m not sure I would. Especially as it is not certain that this functionality will be maintained given the extreme volatility of LinkedIn&#8217;s features overtime. However, if the platform decided to link the ability to add contacts to one&#8217;s network with this certification (to avoid fake accounts), this would change the overall picture. Nevertheless, one should be able to choose one&#8217;s provider and switch to Gov.UK or France connect + or whatever tool promoted by one&#8217;s country&#8217;s government. Have We Become Slaves to AI? Another feature discussed with Bruno was the ability to reply to collaborative or supposedly collaborative articles. Bruno was very critical of this feature. The questions posed by LinkedIn, which are pushed to users in order to make them believe they could be granted a hypothetical &#8220;expert&#8221; badge and status, are in fact produced with generative AI. Bruno even pointed out that some of these questions sometimes missed the point, and especially the rules for using the service. Asking, for example, &#8220;How do you &#8216;scrape&#8217; data from LinkedIn?&#8221; This expert status, while relative, is only temporary, Bruno explains. In short, it&#8217;s best to avoid wasting your time. This exercise involves providing content to the platform free of charge and becoming a slave to the social platform, for the benefit of an algorithm. It&#8217;s likely that no one will read your publications, which even you will find hard to recover. (Bruno provided a tip to explain how to recover them by logging out, but it was a trifle complicated&#8230;) If I don&#8217;t recover the content, I produce so that it is at least visible on my profile, what&#8217;s the purpose? At the end of the day, if a subject uncovered in a collaborative article appeals to you, take up the question and deal with it in a post on your profile independently of the &#8220;collaborative&#8221; article. Writing Posts With Artificial Intelligence A feature not yet deployed in all languages (for instance, it&#8217;s not available to French-speaking audiences as we are writing these lines), is the possibility of writing articles or correcting them rather, using artificial intelligence in LinkedIn. Bruno doesn&#8217;t see much point in it. In his opinion, there are enough text-generating AIs here and there to allow you to compose a post without having to resort to a wonky feature inside LinkedIn. But Bruno went on. If it&#8217;s a question of using AI to write something that you will copy and paste into a publication, I consider that to be shooting yourself in the foot. Assuming that the AI makes you a superb publication based on a prompt, what&#8217;s going to happen the day we meet in real life? As a result, you had better waste five minutes of your precious time, so you avoid looking silly later on. Reacting to Publications with AI A new feature offered by LinkedIn, is the ability to react to a publication, directly using suggestions offered by artificial intelligence: &#8220;Very good comment!&#8221;, &#8220;I fully agree with you&#8221;&#8230; Etc. Here again, Bruno&#8217;s reaction is rather lukewarm, if not downright negative. It&#8217;s a downward spiral. I agree that LinkedIn members sometimes need encouragement to speak up and post comments, but providing such ready-made answers is bad. It reminds me of “LinkedIn pods at their worst&#8221;. LinkedIn Not So New Feature: Newsletters The ability to write newsletters directly in LinkedIn has been available for a few years. It has had its ups and downs, with the platform bringing it forward, then withdrawing it, then putting it back, and so on. A rather erratic practice of innovation, yet not unusual when it comes to Internet giants. This is an interesting feature, but Bruno advises us to use it in copy-paste mode. Our own content should always remain ours, he added. It should be considered as ancillary content, and your newsletter should not be based on LinkedIn. They decide at will whether your content should be shown or hidden. Yet this content is yours, the benefit should be too. It should be up to us to decide who we want to show it to. Insofar as the platform decides and has the right of life and death over our publications, it&#8217;s about time content creators took back control of their work. This newsletter feature on LinkedIn is interesting. However, if we put all our eggs into one basket, the day LinkedIn decides that everything has to be paid for, how will we manage? Videos and LinkedIn: A Love-Hate Relationship! This isn&#8217;t a new feature as such, but the question regularly arises as to whether putting a video in a LinkedIn post improves its visibility or reduces it. Unfortunately, there is no absolute answer to this question. But Bruno&#8217;s makes perfect sense. Are videos a plus or minus on LinkedIn? I republished a video recently and it &#8220;hit the bull&#8217;s-eye&#8221; compared to other publications. And yet I&#8217;ve often been told that you shouldn&#8217;t republish a post! Was it the video that gave the post more impact? I think it&#8217;s mainly the message you&#8217;re putting across that counts, the quality of your content and who you&#8217;re addressing. In conclusion, It doesn&#8217;t matter what content mode you use. What&#8217;s important is that you have an important message to get across. Interesting and enriching content. Here&#8217;s Bruno&#8217;s conclusion. Let&#8217;s make videos and see how our audience reacts. Let&#8217;s do carousels otherwise. And if we want to do text, let&#8217;s do text! The idea is to get a message across to our fellow human beings, rather than wondering whether a particular format will clinch it better than another. At the end of the day, what does &#8220;reach&#8221; mean? Is it about our customers? The people we find interesting, those who will read our stuff, watch our videos, read our content or our carousels? And anyway, the algorithm keeps changing. Native video posting used to do the job, and later it didn&#8217;t because the algorithm had changed. At one point surveys were a big thing and LinkedIn was pushing them. We then had surveys about this and that and the other. Most were completely inane. I created one that made no sense just for fun. And it ended up being a big hit! Then it stopped working. The algorithm had changed again. In my mind, you have to use a humane tone of voice then favour one format over all else and stick with it. The post LinkedIn&#8217;s new features under the microscope appeared first on Marketing and Innovation.

What if hiring luxury venues during the 2024 Olympics were a good opportunity for brands, even small ones?  The Olympic Games are only six months away from now and I was wondering how much of an opportunity it was for brands and which ones. To find out I invited Tanya Bencheva, the CEO and founder of Native Spaces, to share her reflections on that subject. Little did I imagine, when I first contacted Tanya, that there were so many options for smaller brands. It&#8217;s certainly up for grabs and our readers should definitely give it a thought.  2024 Olympic Games in Paris: Luxury Venues are a Good Bargain for Smaller Brands Olympic Games &#8211; Luxury Venues Paris are certainly a much better bargain than I originally thought when I interviewed Tanya Bencheva What are the expectations six months ahead of the Games Tanya Bencheva. It&#8217;s certainly a very exciting event. I really hope for everybody and for the world that it&#8217;s going to be as big as we expect it to be because we are all in need of a reminder of the values brought together by the 2024 Olympic Games. How successful do you think will it be from a business point of view? It would be a no-brainer not to use that event to get more visibility. Now how big it will be, I couldn&#8217;t say precisely but there are a lot of expectations and buzz. What we know for sure is that a lot of property owners are increasingly coming to us to try and monetise their buildings and venues via this big happening. Why should a brand bother about luxury venues and the Olympic Games at all? There are many, reasons for this. There will be millions of people from all over the world and with a high level of income who will be staying in Paris during the Olympic Games. Hence it&#8217;s an excellent opportunity for visibility. On top of that, these people will share content with their communities, and that adds other millions of viewers to the lot. And then obviously the games are televised, therefore generating more listeners. So it&#8217;s an incredible possibility to build brand awareness. And beyond that, engagement. I think it&#8217;s a no-brainer. Luxury venues are up for grabs. Be it a yacht on the river Seine, a homey place in a luxury loft at the heart of Paris or the lavish reception hall of second empire monument, there are plenty of opportunities for brands to surprise their clients and organise memorable events — image taken from the native-spaces.com website. Is it just for big, renowned brands? I think they can all benefit in different ways. That&#8217;s a unique opportunity to advertise internationally. Established brands can bring together their fans, they will already be in town for the Olympics, so they can increase customer engagement. There are particular sectors that would be more prone to profit from this visibility, though. Anything that has to do with sports and also maybe apparel and consumer brands. But that being said, the Olympic Games are about much more than just sport. It&#8217;s a huge event that is based on positive values that any marque may want to be associated with. So, I think any label may try to find creative ways to align itself with the Olympic spirit. If I were selling plastic tarpaulins, could I jump on the bandwagon too? You could! There are always creative ways to grab the public&#8217;s attention. The Paris Olympic Games have a special focus on sustainability. This could trigger an idea for you, but you&#8217;d have to do that with authenticity, though. If you don&#8217;t live and breathe such values, it would be more difficult to align your brand with them. Are such luxury venues only for B2C businesses? Of course not! B2B companies can also resort to these kinds of events not only for visibility but mostly to show special attention to their larger clients. For instance, inviting key customers to join them in their private lounges. They can invite them to outstanding locations overlooking some of the sports venues or lavish apartments overlooking the river Seine. Or inviting their clients to watch the opening ceremony in a specially branded environment. How do brands, and especially lesser-known ones stand out from the crowd? You don&#8217;t need to spend billions to engage your, audiences because there are many creative ways to build personalised experiences. Younger people feel more comfortable in small groups and very niche communities. And brands are more and more inclined to acknowledge this. Intimate spaces, remarkable spaces for those events or experiences that will suit smaller groups. That requires preparation and the greater the surprise, the more unique the experience, the bigger the impact. For a very small business wanting to attract people&#8217;s attention, what kind of venue would you choose? I would select a venue whose authenticity is suited to my brand values. Is my brand very digital or experiential? What is my audience like? I would choose the venue that reflects the personality of my audience. So it might be a homey place if this is a small brand where the community spirit is very important, or something completely out of the ordinary. For example, a brand like Jacquemus that will set up events in vast natural spaces. The buzz created before, during and after the event also enables the brand to extend its visibility. It&#8217;s not always a good idea to throw a lot of money at exceptional venues overlooking the Eiffel Tower. There are other ways and means. What about online and hybrid events? I think one doesn&#8217;t quite realise how much hybrid events have become. By the way, we don&#8217;t call them hybrid events anymore. Businesses have realised that, in essence, the audience for an event is bound to be hybrid. I think one doesn&#8217;t quite realise how much hybrid events have become. Hybrid events are no longer the same, though. In the beginning, we were trying to push the same content in the same format to our online audiences, for example, streaming conferences. That really didn&#8217;t resonate well with our audiences. Online audiences will consume content differently. They are different people, they belong to different segments from those that will be present on location. Maybe both populations were mixed during those Covid years because they had no choice but to be online. But those audiences&#8217; expectations are very different now. Often, those who attend online are those who wouldn&#8217;t have been there in person. So you need to think about them in a different manner. We have also witnessed a significant change in event management platforms. It&#8217;s incredible what you can do online nowadays and how you can deliver that hybrid content. Besides, even those people who are physically there are experiencing a hybrid event. They are attending the event on location and are connected at the same time. They are engaging with brands and other users on their phones. It&#8217;s an omnichannel experience. Could you give us some examples? Recently I was attending a big tech conference for start-ups and investors in Helsinki. They did an amazing job of bringing the virtual and physical worlds together. Delegates were able to organise their meetings on the fly with the app wherever they were and on location. Meeting places were geolocated with the app and so on. Digital really was instrumental in making such a big event more successful. The post Luxury Venues during the 2024 Paris Olympics appeared first on Marketing and Innovation.

Just like the Trojan war, the AI &#8220;revolution&#8221; will not take place*. Today&#8217;s topic is the inevitable generative AI. This is the perfect opportunity for us to discuss what a technological revolution is or isn&#8217;t. Here are our thoughts, and we might as well warn you that we are putting our trotters in the trough in a very counter-intuitive way. Admittedly, we&#8217;re taking a bit of a risk here, but keeping up with the Joneses isn&#8217;t a valid option for Visionary Marketers. As we know the subject well from having trained and certified a thousand students, we are also well aware of the limitations of GenAI tools. The AI &#8220;Revolution&#8221; Will Not Take Place It&#8217;s an AI ‘revolution?&#8217; How many times have we heard pundits say that? But what is a revolution and is it true? &#8211; The above touched up and cropped photo taken from an old PWC advert. Note that the agency mistook the Chateau of Fontainebleau for Versailles. It&#8217;s true that seen from afar all chateaux are more or less alike. * this is a literary reference to Jean Giraudoux &#8220;Generative AI&#8221; has been buzzword of the year in 2023 for better or worse. I have wanted to dig somewhat deeper, though. So, what is and isn&#8217;t a technological &#8216;revolution,&#8217; and is AI one of them? That is the question. A few days ago, I came across a LinkedIn post that argued, through a widely shared video, that &#8220;nothing was like ever before.&#8221; It showed a large crowd filming the 2023 fireworks on the Champs-Élysées with their mobile phones. A technological &#8220;revolution&#8221;&#8230; really? In addition to the ubiquitous mobile screens, there were giant LCD displays on the sides and the Arc de Triomphe itself was turned into a mammoth screen. Similar pictures could have been taken in London, New York or Ulan Bator. In a nutshell, what&#8217;s new in 2024 is that everyone owns a screen. The mobile technological revolution is nothing new. In a sense, neither is the AI &#8220;revolution.&#8221; Probably even less since the first poem generated by a computer dates from 1956. It&#8217;s laughable in more than many ways. It reminds me of my 2013 presentations on social media. I was already showing the same photo (above, taken from an NBC broadcast), which was supposed to prove a change in society. Enough of that, let&#8217;s get back to our main topic, i.e., generative AI. The 2023 obligatory buzzword has undoubtedly been &#8220;revolution&#8221; as in &#8220;GenAI is a technological revolution.&#8221; This term is ambiguous, though. As Merriam Webster state, a revolution&#8217;s first meaning is that of a planet turning on itself, a kind of standstill in fact. A revolution&#8217;s first meaning is &#8220;back to square zero.&#8221; In other words, as Alphonse Karr would have it, &#8220;The more things change, the more they stay the same.&#8221; On the other hand, it&#8217;s a term so widely used in innovation that it&#8217;s almost embarrassing. 1 (a) 1 Revolution the action by a celestial body of going round in an orbit or elliptical course. also: apparent movement of such a body round the earth [Merriam-Webster] Mobile revolution: the 2024 version according to the Huffington Post. A recurring theme Try to play down this &#8216;revolution&#8217; business and people will mock you. It has happened to me. However, one should wonder what, in our daily lives, is bound to change so radically in the coming years. Fear Is Irrational Firstly, fear is everywhere, but is it justified? Should we consider AI is a &#8216;revolution&#8217; merely because we fear we might lose our jobs. What are the facts that substantiate that feeling? Secondly, uncertainty, or rather a feeling of uncertainty is ubiquitous. One hears of a VUCA (Volatile, Uncertain, Complex, Ambiguous) world as if it were new. Yet, the concept was coined in 1987. I also wonder what the people of the late 18th century or the first industrial revolution might have thought of that. These feelings are shared by many, as this recent anecdote shows. Some time ago I hosted a webinar on the subject of AI-assisted development. It&#8217;s a fairly technical subject and certainly not revolutionary. It reminds me of my Unisys days 40 years ago and the Xerox CASE systems. Right after that event, I received a number of phone calls and messages from people who were experts in certain areas of IT, but who were panicking when thinking of this Sci-Fi-like, &#8220;dehumanised&#8221; view of our future. But beyond these irrational fears, is what we are experiencing today really a &#8220;revolution&#8221;? In the sense that everything is changing radically. No one understands innovation (Berkun) I don&#8217;t think anyone in 2021 understands anything about innovation, an observation that was already made by Scott Berkun over 10 years ago, and which, in my opinion, remains entirely valid. So much the better, as it gives us work to do for many years to come, this is reassuring. After all, not everything is bound to disappear. Over the last few months, as I&#8217;ve been delving into the subject of AI and generative AI, I came across a programme on France Culture (Science Chrono, 21 October 2023 in French) in which Antoine Beauchamp described the first attempts to generate text using artificial intelligence. It wasn&#8217;t in 2023, nor 2010, but&#8230; 1956! Granted, the texts it produced were gibberish. Those written by the surrealists too. The buried figure exterminates the terrible dreams, the abysses and the solitary reapers are never a fierce anvil, crumpling with difficulty an ordinary sickle with the gleam, a blood mutilates the false twilight by a fertile sword. Computer-generated text based on the lexicon of Victor Hugo &#8211; 1956 The generation of text, and poetry in particular, was one of the first playgrounds for artificial intelligence and mathematicians. Not all that revolutionary. What&#8217;s more, the presenter pointed out that the human brain is designed in such a way that when confronted with an incomprehensible text, it adapts to try and make sense of it. We certainly do the same when looking at the results delivered by ChatGPT and its competitors. AI, Stochastic Approaches and The Surrealists Many artists of past centuries have experimented with methods similar to the stochastic approaches of generative AI. These include Stéphane Mallarmé, whose texts are often hermetic, the precursors of the surrealists, Georges Perec, the other members of the Oulipo (literally, the &#8220;opener of potential literature), and their master Raymond Queneau (also a mathematician, his books were sometimes the result of what might be described as algorithms, as in the case of the skin of dreams aka loin de Rueil &#8211; 1944) and, of course, the surrealists with their infamous &#8220;exquisite corpses&#8221; (a century ago). An Exquisite Corpse by André Breton. I wonder what Breton would have thought of this AI &#8216;revolution&#8217;?! So what can we derive from all this? Generative artificial intelligence has been so successful in the eyes of the general public since the end of 2022 (and longer as far as we are concerned), it would be silly not to admit that this is a technological breakthrough for computing. No one could buy into that. But is it a mere step forward or a &#8220;revolution&#8221;? I&#8217;m leaning towards the step forward, even if it&#8217;s hard to substantiate this conclusion with facts. Readers versed in French are kindly advised to get to grips with Philosopher, Mathematician and IT expert Daniel Andler&#8217;s book, &#8220;Intelligence artificielle, intelligence humaine, la double énigme (Nrf, 2023).&#8221; Here is an excerpt. But what kind of enigma is it? Here is what I think: the target of AI is an artificial intelligence that is on par with human intelligence. But this target never seems to get any closer, even though AI is constantly progressing. Here are the two explanations I propose to solve this enigma. […] The first is that the pursuit of an artificial intelligence endowed with human intelligence is pointless: according to the conception of intelligence that I defend, intelligence in the human sense can only be attached to a human being. Artificial intelligence, in whatever form and at whatever level of development, is designed to solve problems, which is only a secondary task for human intelligence. The second conclusion concerns the efforts that AI is devoting, with a tenfold increase in energy, to designing ever more intelligent systems, that is, in its view, ever closer to human intelligence. It also aims to give these systems as much autonomy as possible, and ultimately total autonomy. This dual objective is incoherent, dangerous and pointless. We will only truly understand the changes brought about by these technologies in a few years&#8217; time, and with the benefit of hindsight. In the meantime, untimely enthusiasm about technological &#8216;revolutions&#8217; should be taken with a pinch of salt. We are told that white-collar jobs, and mostly copywriters and even developers will disappear. Time will tell. I have my doubts. Jobs disappear and are replaced all the time anyway. There is nothing new in that. The Booker Prize Awarded to ChatGPT? Of course, ChatGPT and its clones know how to produce mathematically plausible texts. But will we be seeing the Booker Prize awarded to Bard, BingAI or ChatGPT in five or ten years&#8217; time? I doubt it very much. On the contrary, some authors will make fun of these machines, hijack them and use them as creative material. This will last for a while, and then we&#8217;ll move on to something else. Recently, I was looking back on more than a year and a half of using generative AI to create images for this website. I realised that my own perception of the pictures that were generated was evolving over time. Initially, rather like children, we played with these tools (Midjourney and others) in an irrational way. We started producing images all over the place. Many users are still doing that today. LinkedIn is awash with these plastic images made by AI. Half-scary, half-demonstrative, they come in garish, stereotypical colours and are instantly recognisable by anyone with even the slightest training. AI revolution : over time, perceptions change. What used to be a game has become boring. Repetition even triggers fierce reactions from readers. Over time, you learn to abstain from using AI. I&#8217;m at this stage now. You then use this tool as one of the sources for producing images, mixed with stock photos and also more personal images, and to avoid using them systematically. Of course, this won&#8217;t stop billions of users producing these gaudy, horrific images. But a more reasoned use of the machine can free us from these atrocities, and by rediscovering our technical skills and mixing several tools, we can find true creativity (combining, tearing apart, recombining, etc.). All this to say that the &#8216;revolution&#8217; will not take place, or rather that it will take place, but certainly not to the extent that we imagine today, and provided we wait (10, 15 or 20 years) and live long enough to witness the impact and true use of these platforms. Such impact will be undeniable for some uses — like picture generation — and much more debatable for others, in particular the generation of stochastic texts. I know that some people will be disgruntled and will dismiss my predictions. Most of them will. Year after year Internet and social media pundits predict new technical revolutions. Our History of technology books are full of the stories depicting these failed technological upheavals. I apologise for this, sincerely. But I&#8217;m not going to indulge in this exercise, which makes no sense whatsoever. I realise; however, that truth is less spectacular than fiction. People love their own dreams, this is precisely one of the things what will make humans better than machines. In the meantime, I&#8217;m willing to bet that the digital word of the year 2024 won&#8217;t be &#8220;generative AI.&#8221; &nbsp; The post The AI &#8216;revolution&#8217; will not take place appeared first on Marketing and Innovation.

How can a responsible marketer survive when the world around us is crumbling? Or at least when experts are telling you that it is. Three years to the day, the French association of marketers Adetem asked Visionary Marketing to join its CSR responsible marketing initiative, and naturally we welcomed the opportunity. That almost seemed natural to us. However, things aren&#8217;t that simple and many questions remain. Here are our thoughts on the subject even though there are more questions than answers. Our underlying goal is to encourage marketers (and salespeople) to question and reflect on their practices and how they should behave professionally at a time when society as a whole is torn on these issues, particularly environmental ones. It seems to us more suitable than developing an ideological pitch and demanding that facts match reality. Tips for the Depressed Responsible Marketer The responsible marketer is sure of his commitment, but is it effective? The key is not to get depressed and to stay the course &#8211; image made with Midjourney. This survival guide for depressed responsible marketers was written for some reason. Let me tell you an anecdote Anecdote: An Eco-Distressed Responsible Marketer One day, I met a colleague and friend who is a digital marketing expert. He was deeply concerned by recent developments in climate change (not to be confused with global warming). His wife had just given birth to a baby and he was devastated by &#8220;the world we would be leaving behind to our children&#8221;. I sensed that he was about to snap, so I reassured him and advised him to take the edge off. His eco-anxiety (the word hadn&#8217;t been invented back then) was probably not going to do much good, and even less so for his child, who would probably be in need of trust, encouragement and a positive outlook. And yet, six or seven years later, I&#8217;m reminded of this anecdote as a COP28 conference, led by an oil tycoon and carrying the hopes of many, has just drawn to a close. Same old song and dance, the following IPCC get-together will take place in Azerbaijan and will be led by an ex-marketeer from the oil industry! Worse still, I learned recently that one of the flagship organisations that gave birth to the IPCC was also founded and run by a Canadian oil investor (check Maurice Strong&#8217;s bio). Eco-responsibility in marketing is a bit like the rearrangement of deck chairs on the Titanic — image produced with Midjourney More depressing news for you To this one could add many other depressing events or books (from Vaclav Smil to James Hansen and Antoine Bueno [Fr] through to Jean-Baptiste Fressoz [Fr]). All that shows we might well hit the wall of climate change soon. French Energy expert Fressoz states, there is no such thing as energy transitions for instance. Smil had highlighted the energy transition issue as early as 2010. To top it all, there is no such thing as &#8220;peak oil&#8221;. Two mining experts from France, warned us about the surfeit of oil reserves on the planet [Fr]. There is no dearth of oil sorry, we&#8217;re in for trouble for very much longer I&#8217;m afraid. Worse still, there was this talk we gave in Tunisia. While around a hundred marketers flocked to the workshops dedicated to AI, digital marketing or agility in product marketing, the breakout session dedicated to responsible marketing was attended by two participants, one of which was a student. (Ir) responsible salespeople and marketers We could add to the list. There are many examples of irresponsible marketers and salespeople, such as door-to-door sellers who try to take advantage of the elderly by getting them to sign contracts when they don&#8217;t even realise it (real-life experience), or phone spammers, email or LinkedIn scammers. All those people are determined to make you like hellish. Laws and regulations are piling up but to no avail. Complaints and technical devices aimed at protecting you don&#8217;t seem to have that much of an effect either. Marketing and sales professionals have always been familiar with these fishy practices. Those who behave in this way are a disgrace to our profession and, sadly, they are often held up as an example of &#8216;success&#8217;. Think about growth hackers for instance. Nauseating. My Brompton P series above the Sea, a stone&#8217;s throw from Sword Beach where British forces first landed 80 years ago. I ride 3,000 miles per annum and I&#8217;ve covered at least 20,000 miles in the past six years inclusive of client visits et al. But is it sufficient? I doubt it. In the background, a Brittany Ferries ship on its way to Portsmouth. Brittany Ferries was founded by Alexis Gourvennec whose Father was an Onion Johnny Questions responsible marketers may have All this leads sincere responsible marketers to ask many questions at a time when societal and environmental issues can no longer be ignored. Am I setting an example? How can I contribute to improving things? Above all, how can I avoid working to make them worse than they already are? Is a responsible marketing framework sufficient? Should I sell or ask people not to buy any more or rather, not so much? Fight against Primark, Shein and Temu? And if so, how can I do so when the basics of sales and marketing are advocating the opposite? Will I, as a responsible marketer who sells less, be promoted or demoted? Answering these questions is probably not that simple. But we may still surmise, perhaps rightfully, that making efforts, however small, is better than doing nothing. Should a responsible marketer give up hope? But let&#8217;s return to the anecdote I quoted above. Should a responsible marketer &#8211; or one on the way to becoming one &#8211; despair? Should she be overwhelmed by this seemingly unattainable goal? Must she fight greenwashing and illusory transitions? Should she avoid working with unethical companies, those from the coal, oil, tobacco and alcohol and spirits sectors, those promoting addictive substances or contributing to the destruction of natural resources, that resort to modern slavery, that sell you nitrites or make you drink plastic waste, eat pesticides or contribute to the growth of green algae to the point of suffocating the beautiful shores of Brittany&#8230;? The list is almost endless, and it&#8217;s getting longer every day. The responsible marketer, however, has no right to be depressed. Firstly, because it&#8217;s pointless. Then, because while she may be &#8220;responsible&#8221;, she may not necessarily be guilty. She must constantly question her practices and make efforts not to take the easy way out. She must also try and convince those around her. Thirdly, because she must not waste time trying to convert zealots. Let them surrender or commit suicide when they will be surrounded (as at Masada). Finally, because it&#8217;s not possible to think that you can be held accountable for all the world&#8217;s misfortunes if you can&#8217;t solve them. That&#8217;s putting too much pressure on yourself. At the end of the day, a depressed marketer will never be able to improve the situation. A weak but necessary survival guide Such is my survival guide. It&#8217;s very weak, I readily admit to it. But at the same time it will allow you to live better and at peace with yourself. It will also enable us to try and improve things as we best can. No more, no less. It&#8217;s easier when you are a content marketer than a marketer for the Royal Dutch-Shell company, though, I&#8217;m aware of these limitations. There aren&#8217;t many chances of result either, I know that too. On the environmental front, the situation seems, if not hopeless, at least quite serious. So let&#8217;s hope, with Smil that there is no guarantee that this will be the end of the world and that humans are incapable of predicting the future. Both statements are probably both right. Assuming Smil is right, whatever good practices responsible marketers have put in place will always be a means to change the world for the better. We can live with that. The post CSR: a survival guide for the depressed responsible marketer appeared first on Marketing and Innovation.

Will marketers survive the content shock in the age of AI? The Omnes Education Group launched a cross-organisational programme in English called &#8220;Content creation in the age of AI&#8221; to help its students better understand GenAI. Close to 1,000 students will be certified in this program by early February 2024. As part of the program, Visionary Marketing interviewed Mark Schaefer, one of the world’s most renowned marketing bloggers, to gain insights on how marketers should approach the new content shock caused by AI. Schaefer&#8217;s answers were thought-provoking and valuable for both established and aspiring content creators. Surviving the Content Shock in the Age of AI How can marketers survive the content shock in the age of ChatGPT and GenAI? To find out Visionary Marketing asked Mark Schaefer — visual by Omnes Education Group. The Shift programme is a cross organisational education programme aimed at all the schools within the group. In 2024, nearly 1,000 students will be certified within the Content creation in the age of GenAI programme. Understanding Before Idealising Understanding GenAI in 2024 is a must, regardless of the fact that you like it or not. By that I not only mean how it works, but how it must be used, when it must and must not be used, what its limitations are, and the societal questions its implementation raise. My stance on this is very straightforward: What one gets to grips with (rationally), one never fears nor idealises Banning GenAI in schools will serve no purpose I am convinced that merely banning the usage of GenAI, as I see it done in many US universities now, isn’t a good idea. For one, it will not stop students from wielding those tools. There is always a workaround. Besides, it will not help them develop a critical eye towards technology and its — inevitable — limitations. For this reason, the Omnes Education Group, one of the largest in Europe, launched a very ambitious cross organisational programme in English for all its students. I was very lucky to work with them on that project. I wasn’t alone. Bénédicte, Julie and Fanny are part of an amazing team with whom I really enjoyed working. GenAI: approx. 1,000 students will be certified At the end of the process, in early February 2024, close to 1,000 students will be certified within this &#8220;Content creation in the age of AI&#8221; programme. Given the subject, it made perfect sense for me to interview one of the world&#8217;s best marketing bloggers, Mark Schaefer, whose work has inspired us at Visionary Marketing for the past ten years at least. I interviewed Mark as part of this lecture, so that he could tell us how marketers should tackle this new content shock. As always, his answers were thought-provoking. They are a lesson for all established and would-be content creators who want to know the way ahead. This exchange is one of many that we recorded for our students and cannot be disclosed. The school representatives were kind enough to let me publish Mark’s interview publicly, though. This is a condensed version of our exchange. The content shock, 10 years later: an arms race of content &nbsp; In 2013, Mark issued a warning. Too much content will kill content marketing and content marketing is not a sustainable strategy, he contended. “The thesis behind the content shock article is that, in an economic system, a natural system, or a human system, if there&#8217;s too much of something, there has to be an adjustment.” This is true of water, snow, pollution, heat… and there are no reasons why content creation wouldn’t be following that rule either, Mark explained. “You&#8217;re going to have a flood and you will need to adjust,” he went on. “This pattern repeats in every channel where there&#8217;s a need for content. When a new channel becomes popular, the amount of content in the channel goes up, up, up, up, up. And so, it becomes an arms race. And it&#8217;s a never-ending competition.” As always, Mark is hitting the nail on the head. All content creators have been through this before. Those who published monthly in the 1990s, started publishing weekly 10 years later, then daily and finally, several times a day. The “Publish or perish” adage has never been so true. And so it goes with social platforms too. Publishing once a month on LinkedIn isn’t going to make you very popular. After a while, one can wonder whether publishing ever more content still makes sense. Either you create better content or promote it better (or both). “And you only have two choices,” Mark went on. “You must create better and better content. And there&#8217;s a price to that. Or you must promote it better and better and there&#8217;s a cost to that too.” This is something that happens with every social media channel, old and new. “Now we have threads and everybody — in the States — says, ‘go on threads! It&#8217;s easy to find an audience.’” But it never lasts for long. By the time the platform has become popular and everyone has migrated to it, it has become a lot more difficult to find one’s audience. This is “a repeating pattern”, Mark explains. Mark Schaefer coined the content-shock phrase, a very useful concept in this age of GenAI — Photo by Mark Shaefer What of Generative AI? Thus, “how does generative AI impact content creation?” Things, as usual, aren’t black or white. On the one hand, “There&#8217;s a wonderful benefit of this. It unleashes creativity and productivity in wonderful new ways.” On the flip side, it floods, and will increasingly flood the market with content. Let’s start with the positive side, increased creativity, and productivity. Mark comes up with an anecdote about that: “I have a friend who is, by her own admission, a terrible writer. Enters ChatGPT. She says, ‘Now, I can blog every day. I might even be able to write a book.’ That&#8217;s wonderful! ChatGPT to writing is like a calculator to math. It makes everyone a competent writer, that’s wonderful.” On the other hand, flooding the market with a lot of new content may not be such a good idea. “It makes the whole content shock problem a lot more severe,” Mark added. “There are a lot of unethical (black hat) things going on. The system can&#8217;t survive in the long term.” Taken at face value, all this doesn’t bode well for content marketing. Yet, there is another way of looking at it and Mark remains, overall, rather optimistic. I think he is right. A large system like the Internet will almost always purge itself automatically. If the content is poor, users will leave eventually and that will force platforms and search engines to clean up their act. GenAI and the content shock: not so serious Indeed, Mark thinks that things will improve over time. “Those people will end up being penalised and they will go away and eventually, the system will work itself out. I&#8217;m not so much worried about the short term. I think we need to stay focused on doing good work, doing exceptional work.&#8221; Being known is what will save us Mark, as the author of the best-selling opus “Known,” believes that “only our personal brands will save us. Being known and beloved will enable us to earn our own audiences,” he added. This reminds me of my advice in this PushEngage webinar about GenAI in 2020. Since bad content will be plentiful, those in search of valuable information will have to focus on recognised, trustworthy sources. Not just mainstream media, but bloggers, renowned professionals, and influencers. People they can trust. For those who have worked on their reputation, Mark says, there is no real issue with the excess of content. AI-driven Content-Shock: A threat for tomorrow, not so much for today In fact, the sheer volume of content produced has no impact. “Let&#8217;s say we are a blogger trying to compete in a world where there are millions of other blog posts. Whether it&#8217;s 1 million or 1 billion, doesn&#8217;t really matter. Regardless, we will have to earn our way. And as you have suggested, a lot of this AI generated content isn’t very good. Yet, it&#8217;s going to get better. I think the most significant thing about generative content is not really the threat it provides now, but the threat it provides tomorrow because it&#8217;s getting so much better so fast. I think that in the next 18 months, we will be able to create a full-motion picture from our room, our kitchen table, with almost no money. And that&#8217;s why a lot of the writers and actors were on strike in Hollywood.” ChatGPT mimics Shelly Palmer in just five seconds To wrap up this interview, Mark told us an anecdote. “When ChatGPT was introduced, I immediately went to a friend of mine, Shelly Palmer, who is a very well-known technology analyst in America. I asked him what he thought of it. He said, ‘it&#8217;s terrifying. I&#8217;ve asked this thing to create a blog post in the voice of Shelly Palmer. And it did a magnificent job in five seconds. I&#8217;m 80% replaced.’” Once again, on the face of it, one could think that this is the end of content marketing. Mostly on technology topics that are so well covered on the Internet. “But let&#8217;s look at it more closely,” Mark concluded. “What is the 20% made of? What will not be replaced?” The answer to this question is simpler than you think. “Shelly is known, he is trusted,” Mark explains, “he is beloved. And that is something that ChatGPT can never take away from him.” It is clear, from what Mark and Shelly expressed, that the students who want to thrive tomorrow will have to up their game significantly. Let’s hope that this education programme and certificate by Omnes will help them achieve this very goal. The Omnes Education Group team developed this beautiful and inspiring trailer based on my introductory text for this programme. It was sent to all students within the group to inform them and encourage them to enrol. Click the button to discover the Omnes Education Group. The post Surviving the Content Shock In The Age of GenAI appeared first on Marketing and Innovation.

According to a recent Forrester report (How Generative AI Will Transform CRM), GenAI may have significant and beneficial impacts on Customer Relationship Management systems and practices. The real question is, however, how effective GenAI could be when it comes to handling various customer-facing tasks? And how easily could it adapt to specific CX applications? Forrester’s analysts surmise that using massive bodies of data and large language models (LLMs), one could achieve better interactions with customers, therefore elevating customer experiences, and contributing to growth. That is to say, as long as certain conditions are met. Besides, when mentioning GenAI and CRM, the analyst group warns that ChatGPT might not be the ideal solution.  GenAI Could Enhance CRM… Under Certain Conditions Forrester’s analysts think that GenAI may have significant impacts on CRM systems and practices. But there are a few provisos—image by Midjourney describing customer journeys and CRM processes automated with IT and GenAI. Providing Better and Quicker Responses to Customer Inquiries In this report entitled How Generative AI Will Transform CRM, a group of Forrester analysts discusses how CRM could use GenAI-powered chatbots and virtual assistants to provide quicker responses to customer inquiries. Therefore improving response time and overall CX. By analyzing data from customer interactions, including emails, meeting transcripts, and phone conversations, GenAI could apply its ability to surface, summarize, and interpret key consumer insights. This would enhance CRM’s strategic importance by providing more complete data sets and streamlining  processes. Implementing GenAI could possibly change the way CRM agents and sellers engage with customers through CRM systems, the analysts contend. In this manner, CRM agents would have more time on their hands for the more creative and strategic parts of their tasks. Thus, increasing CRM’s strategic footprint could make marketers more innovative. In addition, sellers would also be able to address buyers&#8217; requests for information more accurately. The bottom line is that all that would lead to more customer satisfaction and better Customer Relationship Management, Forrester&#8217;s analysts assert. In other words, GenAI would not threaten CX, it could well enhance it. GenAI Will Revolutionize CRM, Not Erode It Forrrester  Common GenAI features in Customer Relationship Management platforms according to TechTarget The Role of GenAI and its effect on CX and CRM According to the analyst group, Generative AI’s role is to reshape and elevate customer experiences. Extracting crucial information from Customer Relationship Management records. Providing real-time news feeds, assisting users to prepare for meetings and making effective decisions increasing customers&#8217; trust. By understanding customers’ preferences, companies could therefore create unique experiences across different touch points, from website interactions to targeted marketing campaigns, leading to a more positive customer journey.  AI algorithms can assess leads based on different criteria, making the sales process easier. They can analyze past customer actions to predict future movements. Helping businesses be ready for customer needs and desires. One key application is in chatbots and virtual assistants, the analysts describe, where generative AI can understand and respond to a wide range of user inputs, providing more humanlike interactions and creating a more engaging and satisfying experience for customers. That being said, Forrester provides a useful comparison table of various LLM models and deliver recommendations accordingly. Thus, while advocating GenAI inclusion within CRM systems and processes, the analyst group also draws our attention to the fact that GenAI isn&#8217;t limited to ChatGPT. Forrester described the differences amongst various generative AI models—image by Forrester Research, Inc. Evaluating the Risks of using GenAI within CRM processes and systems As a matter of fact, one should take notice of the risks associated with the use of GenAI and take Forrester&#8217;s recommendations into consideration. A significant concern is the potential for bias in the content, as it may unintentionally learn and replicate biases present in the training data. This could lead to a negative impact on customer perception and ruin a company’s reputation. As a result, one should address biases in training data and potential copyright violations. The best way to do that is for businesses to assess AI foundations, prepare data, and adopt a human-in-the-loop approach to ensure accuracy, completeness, and ethical considerations in the content produced by Generative AI, the analysts advise. Organizations looking to implement Generative AI need to make sure they have a data center of excellence. In essence this is no different from any other information system implementation. One could even add that AI, rather than obliterating data quality issues, is making data integrity even more pivotal.  Training LLMS on Proper CRM data LLMs need to be prepared on CRM data properly to improve the accuracy of analysis and responses to customers. The data needs to comply with privacy regulations. And its history has to be auditable so that it can be tracked in case of issues. The tracking and monitoring of the content automatically generated is a must-have, the analyst group suggests in their report. Ongoing monitoring and regular updates are therefore needed to evolve customer preferences and ensure the system’s accuracy.  We suspect that this will have a substantial impact on the amount of effort put in place in these processes and systems implementation. What will be the outcome on actual productivity gains remain to be seen. Such productivity gains may also depend on the level of knowledge of the CRM agents or sellers concerned. Whereas a beginner might find a copilot to be helpful when it comes to providing faster and more accurate responses, a seasoned CRM agent may be able to deliver better and faster information to her clients due to her field experience that makes cross and double-checking redundant. In conclusion, GenAI could indeed help organizations navigate the complexities of customer relations in a world where customers are ever more demanding and impatient. Yet, many doubts and risks are paving this way and businesses should tread this new ground carefully while taking into account all ethical, privacy, and communication challenges. Businesses that want to successfully integrate GenAI within CRM should mitigate such risks deftly and promptly. About the study  This report “How Generative AI Will Transform CRM” was written by Forrester&#8216;s analysts Kate Leggett and Rowan Curran with contributions from Linda Ivy-Rosser, Zeid Khater, Seth Marrs, Christina McAllister, Katie Linford, Hannah Murphy.  The post Can GenAI have an impact on CRM? appeared first on Marketing and Innovation.

What are the applications of geographic information systems? Following our discussion with Catherine Crook, we asked Floyd Bull to answer that question for us. Geographic information systems and their applications Visionary Marketing was interested to know more about the applications of Geographic information systems – image taken by Gaurav Jindal and touched up with Adobe Photoshop. How did you get started with GIS? During college, I saw geographic information systems as an opportunity that would lead to jobs after my studies. Throughout the first years, I began seeing all the applications that can be used and fell in love with GIS.  My favorite part as a GIS analyst is working with different teams and with our customers. Each time they come to us with a project, there are different aspects to it. Each market requires critical thinking. This leads to what I love the most about geographic informational systems, which is how they impact your everyday lives. It is amazing how they can be applied to everything even when it is not expected. There is a spatial component to the majority of our lives. How do you solve real-life problems with GIS? I worked for the city of Kirkland in Washington State. Their planning department wanted to know how the shadows on the interurban trail would be affected if they were to increase the heights of buildings. The trail is a walking and biking path through the city, which they hold very dear to them. I performed a shade analysis to see how much shade would go onto the trail during the year. In my report, I provided the city council with a full analysis and a YouTube video showing them hour-by-hour where the shade would be along the path. With this, they were able to see the impact of raising the buildings by ten feet and make a decision based on that analysis. geographic information systems and video games In video games, there is a spatial component too, since characters are moving along a given path. This is the case with games such as League of Legends, which is a MOBA – Multiplayer Online Battle Arena. With this application, you can see the optimal build while playing as well as see what others are using to counter your build. GIS and the issue of climate change Since climate change is a major problem impacting us all, one could use geographic information systems to help solve it. A lot of people getting into GIS work on the rise of seawater and how it can affect different areas. Sometimes people think of GIS as paper maps – we&#8217;ve moved past that. That was 90s GIS. Now we can do geographic information systems where we&#8217;re adding a Z-coordinate to it. We are looking at it in 3D, not just down on a map. Geographic information systems definition by gisgeographic.com Where do you see GIS in the next 5 to 10 years? Mobile applications use real-time data to feed applications and show end users what&#8217;s going on. This makes it possible for maps to update instantly. I see it going in a data science route &#8211; looking at the data more and analyzing it. We as GIS analysts can get very much into the weeds of data &#8211; end users don&#8217;t care as much as we do about the nitty-gritty. But we can use geographic information systems to tell them a story that they can ingest in a much easier way. It has an impact on how we see climate change, hiking, golf, video games, baseball, or any other application.  &nbsp; The post What are the applications of GIS systems appeared first on Marketing and Innovation.

Visionary Marketing spoke with Catherine Crook ­– senior GIS program manager for Hexvarium – to discuss the use of geographic information systems or GIS and its impact on communication networks and the world. We touch on her day-to-day obligations as a senior gas program manager. The software is used for tracking systems, the issue of climate change, and more. Catherine provides a very insightful view on the emerging GIS that is becoming an autonomous type of tracking space. However, there is a need for students to understand and find an interest in what GIS really is. A day in the life of a GIS program manager Visionary Marketing wanted to know more about the job of GIS program manager. We asked Catherine Crook to talk about her experience – image produced with Midjourney and Photoshop Could you describe your day-to-day obligations as a senior gas program manager? Hexvarium is a startup that is pushing GIS into a broadband space. As a program manager, my job is to interface with clients. Manage a team of analysts who must get the deliverables to where they need to be. I also must make sure our development team is involved in all the nuances that are happening in what the customers are requesting. Because it’s a new way of looking at and navigating the broadband space, a lot of customers have never seen this type of application. Most of my time is spent training as well as making sure all components are where they need to be. The purpose of my job is to be the glue. To make sure that when we deploy something, everyone really understands the reason why we are deploying it the way we are. Which skills do you regularly use for your job? Things have changed a lot as I have navigated through my career. My job is now to implement systems that correlate directly and involve the GIS space. My background was mostly in the GIS analyst component, but my skill set would be more of a project manager. As a start-up, we were tracking hardly anything. I&#8217;ve implemented a lot of work-order tracking, systems tracking, and a JIRA ticketing system. There are all these different things in place now that we&#8217;re able to go back and grab. We can compare today to six months ago, and today to last week. That helps us navigate changes moving forward. I think understanding the GIS space is my regular skill. At the same time being able to project manage that GIS space seems to be the most dominant skill in these early stages of a startup. Which software do you use to implement those tracking systems? Previously, I&#8217;ve made my own tracking system. I&#8217;ve coded and made my own, and that&#8217;s been in SharePoint. That was a tracking system where if anybody wanted anything, it would come in as a ticket and then we would deploy it after it came in and there were criteria you had to follow. In the startup world, we don&#8217;t necessarily use SharePoint. I&#8217;ve then developed a work-order system so that a template environment must be adhered to for asks, and then that gets placed in a Slack channel. It&#8217;s like we&#8217;re building the plane while flying it. Most days if I have some time, I build a documentation or tracking system while I&#8217;m still in the air. It&#8217;s way better than it was six months ago. We can now grab things and understand landscapes better than we ever could before, but it&#8217;s going to take another year or so for us to be standardized in the ticketing and tracking space. Catherine Crook is a GIS program manager at Hexvarium, a broadband service provider using proprietary Data Science to accurately identify, deliver, and connect profitable networks across all geographies – image by Hexvarium How does the use of GIS influence fiber-optic communication networks? When we come to the broadband space, an Internet space that is not on the landscape. It&#8217;s considered a newer utility. If you don&#8217;t have electricity or water, those are life-changing spaces. If you don&#8217;t have broadband, they don&#8217;t really consider that life-changing. It&#8217;s changing more as we navigate through this because is the only way I can speak to you in France. We are heavily dependent on broadband, for me and you to have this conversation with no squiggly line saying that there&#8217;s a problem in the flow of information between us and that&#8217;s going through a cable that&#8217;s underneath the Atlantic Ocean. So, GIS is emerging in this space where we are tracking it, and we are looking at fiber builds and fiber locations. In the same matter, it is becoming an emerging GIS world as more states need to grab their grant funds and they don&#8217;t know where to go. GIS allows them to look at different areas and their populations. They might have low upload speeds and download speeds, where we can take over 200 datasets to look at where those people are. We can do a site suitability analysis and look at the location where those groups of people live that are not being connected. We know there is a large population. Especially in the United States and Canada. We spoke to the provincial Ontario Infrastructure Ministry at the conference. They came to us and said, &#8220;Wow, we just don&#8217;t know where these people are, and we need somebody to find them.&#8221; So, it&#8217;s becoming quite a coupled space with GIS and broadband. We asked Midjourney to imagine Paris through the eyes of GIS program manager. How has GIS been applied &#8211; or how could it be applied &#8211; to the issue of climate change? GIS is a database of information. Currently, we are barely scratching the surface of GIS and how it can help the climate issues. We have collected lots of different datasets, such as temperatures, and storms. We look at sea levels, and we have bathymetry data. Which is the topography on the ocean floor so that we can look at the various changes there. Many people could use the data in more predictive ways and look at historical events versus what&#8217;s happening currently. Sometimes we get overwhelmed by the amount of data, and we can&#8217;t possibly look at it all. I once had a student look at the effects of displacement of water and debris onto the Gulf of Mexico. It was amazing how he could use LIDAR. Which is sensory imaging on the Earth&#8217;s surface to do an analysis and tell us how much debris was removed from the ocean and placed on the land from the hurricane. Another example was at a meeting. I was listening to the recollection of how GIS was used for the Katrina environment after that hurricane occurred and FEMA uses GIS too. How did you get started with GIS? Nobody thinks of becoming a GIS person. I really wanted to do epidemiology, which was the study of viruses. Unfortunately, I couldn’t be away from the house for the long term that an epidemiology career would need. In 2001, one of my professors told me there was an emerging technology called GIS, and if I could learn it, he would take me to Costa Rica and map the rainforest. The GPS Trimble unit that he gave me was a unit that gives a satellite connection, it takes your position, and when you move, it drops points behind you, and those become lines. Once I took the data – I had to transform it and make it visible on the screen. Later on, I went to the University of Washington and took a certification and ended up being quite enthralled by it because it allowed me to take my data and visualize it myself. And because at the time my degree was a bachelor&#8217;s in environmental science, I could work in that space by collecting and deploying the data myself. Looking for a Job as a GIS program manager It was very difficult to find a job at that time because the population just didn&#8217;t understand GIS. I went on to write out my resume and a letter. I had seven letters that I sent out to jurisdictions. A response came from a tiny town in the mountains of Mount Rainier called Eatonville, Washington. With a population of 3,000 people, their goal was to expand their boundaries. At the same time, look at their internal infrastructure for grant funding. From there, it just kind of grew into the space where I was an &#8220;analyst one&#8221; and then &#8220;analyst two.&#8221; And I kept going until I ended up being a supervisor, then a manager, and so on. GIS program manager, a day in Paris. How do you anticipate GIS evolving over the next 5 to 10 years? I was in a meeting last year and Dr. Michael Goodchild, who was spearheading this workshop was there. He was the person who pushed GIS in academia. And I told him &#8211; it&#8217;s really intimidating to tell the person that almost created all this that things aren&#8217;t the way they were. If it were the case that GIS is just geographic information systems now, that would be a disservice to what we are looking at. It is combined with data science now. Spatial locations are all embedded in the same world. Everything we have done since the satellites opened. Now we have a point to always attach to our body as we&#8217;re carrying our phones. We were tracking people, as in we knew their information, but their movements, location and what were they doing. More of an autonomous type of tracking space. I mentioned that GIS needed a change in name so that we could sell it better. He was very interested and posed a lot of good questions about how can we get high schoolers interested in GIS. The problem we encounter is all the data can be overwhelming and we also need more people in this profession so we can manage better. A lot of my students at Seattle University will tell me, &#8220;I&#8217;m here because it&#8217;s required.&#8221; But then once they start learning, they start to realize that they are part of it. Unfortunately – younger generations – don&#8217;t know they&#8217;re in this space. So, I think in five years we need to change our views on it and make it more of a standard understanding. The post A day in the life of a GIS program manager appeared first on Marketing and Innovation.

Product Information Management (PIM) systems are a driving force behind a good customer experience. A 2023 survey entitled &#8220;Elevating customer experiences with product experiences&#8221;, sheds light on how product information can greatly enhance CX. Virginie Blot, Product Experience Management evangelist at Akeneo, gave us her point of view and analysis on that survey. Place a Product Information Management System at the heart of your Customer Experience Product Information Management (PIM) systems are one of the driving forces behind a good customer experience. A study by Akeneo portrays this with figures &#8211; images produced by Visionary Marketing with Adobe Illustrator and Firefly. Web-rooming/Showrooming: What are the trends? Let&#8217;s start with Webrooming and Showrooming. Akeneo&#8217;s 2023 B2C study (see methodological note at the end) shows that about 90% of consumers research products online before buying, especially, in-store. Webrooming has been a strong trend for many years and continues to grow. On the other hand, Showrooming is less common than Webrooming: only 77% of consumers have been to the store to complete their online purchase. Eighty-five percent carry on to do their research online before making their final decisions. Worthy of note: 71% of consumers order online first and then pick up their product from the store, this is what is called click and collect. Customers love this ability to switch from one channel to another. Download the 2023 Akeneo consumer study Is Showrooming a risk or an opportunity for brands? The strongest brands are distributed on many channels, both physical and digital. This reinforces their brand image. Consumers will see and try out products in one physical store, but then purchase them online. Either way, the brand wins. For retailers, it&#8217;s a different story. If they haven&#8217;t opted for an omnichannel strategy (online, in-store, and on social networks), it will become more complicated for them as time goes by. How is physical retail holding up in the ROBO/ROPO era? In recent times, we have heard a lot about store closures, the reason behind this is that humans are more attracted to bad news. Nonetheless, there is good news too. For example, DNVBs (Digital Native Virtual Brands), are brands that were directly created as digital, usually on Instagram. Once they have been successful, they will tend to open a pop-up store. Physical locations are best for building relationships with consumers and sharing more than just products, as well as offering an experience. On the Champs-Élysées in Paris, I noticed that L&#8217;Occitane en Provence had opened at the same time as Pierre Hermé (macaroons, chocolates, and pastry shop). Now consumers are able to come to the store and have a great time tasting macaroons while shopping. It&#8217;s a unique experience and a perfect example of how physical stores are reinventing themselves. Check out Sortir à Paris&#8217; report on the L&#8217;Occitane Pierre Hermé store &#8211; Photo L&#8217;Occitane P Hermé by Sortir à Paris L&#8217;Occitane has 3,500 points of sale worldwide, it only makes 16% of its sales through digital channels. Therefore, we must not forget that not everything is digital. It&#8217;s a great image booster as it attracts people to the store, but stores remain essential. Retail: will laggards disappear? The sooner retailers jump on the customer experience bandwagon, the better. It&#8217;s never too late to do it the right way. That&#8217;s why it&#8217;s crucial for distributors to manage their product information to better deploy their omnichannel strategy. By the way, working on one&#8217;s data quality is most time-consuming. This is what makes it possible to add new channels. It&#8217;s not about technicality, it&#8217;s about strategy. Product Information Management and CX expectations? We&#8217;ve been talking about CRM for over 30 years. Today, customer experience is the priority. PIMs have followed the same route. The PIM trend only began ten years ago. These days, the hot topic is product experience, because marketers have clearly understood that while technical product information is mandatory, it&#8217;s not sufficient. Beyond product features, there is a requirement to display information about the content and all other information enhancing the product experience depending on the customers&#8217; preferred channels. This requires data engineering. Even though around 80% of your product&#8217;s data is already available, you will still need to adapt the data to each channel so that it meets your customers&#8217; expectations. To that end, the teams in charge of the marketplace, social networks, or W eb channels will need a hand. What do consumers expect from the customer experience? CX begins with the customer&#8217;s first interaction with the brand and ends with customer support. Consumers expect more than just a good product. Our survey asked the question, &#8220;Are you sensitive to environmentally friendly values in brands?&#8221; The answer from consumers was loud and clear. Over 70% of them answered that they are sensitive to these values, while 40% are willing to pay up to 10% more for environmentally friendly products. 70% of consumers are sensitive to environmentally friendly brands and 40% are willing to pay up to 10% more for them Nowadays, an &#8220;about us&#8221; page on your website is no longer sufficient. Many of our corporate customers are already putting their money into &#8220;behind the scenes&#8221; product information. Real-time calculation of its carbon impact, researching the factories where it was made, and its traceability, to name a few. If you buy coffee from some of our customers, you&#8217;ll be informed about the history of the producer in the coffee&#8217;s country of origin and how direct-to-consumer distribution channels work. Transparency and traceability are on the agenda. They are what&#8217;s going to make a product experience successful and the customer engaged with the brand. What does product transparency entail? In Europe, an application like Yuka, has had a big impact on how and what consumers buy. Without extensive and up-to-date product information, one cannot meet new consumer demands. Transparency is a big issue in the cosmetics and food industries as they must follow standards and regulations, and must provide vital information. For such industries, transparency is a big part of the customer experience. PIMs are changing to offer our customers&#8217; customers better experiences as well as better product experiences, with a focus on transparency. What&#8217;s the current state of Product Information Management use in B2B? We work a lot with B2B businesses. They aren&#8217;t like B2C retailers and brands. B2B companies are highly innovative and are eager to provide their distributors with customised digital catalogues. They are very demanding when it comes to new products, and are keen to provide a unique product experience. Visionary Marketing has designed a dossier on Product Information Management on behalf of Capterra &#8211; in this panel, Akeneo&#8217;s solution is featured &#8211; find out more. Regarding the study The study by Akeneo (an international PIM publisher) named &#8220;Elevating customer experiences with products experiences&#8221; was performed by 3Gem Research. This is the third study of its kind since 2021. Akeneo surveyed 1,800 consumers in eight different countries in 2023. From Australia to Canada, through China, France, Germany, Italy, the UK and the USA. The sample was for people over the age of 18, representative of the populations of these countries. &nbsp; The post PIMs at the heart of Customer Experiences appeared first on Marketing and Innovation.

A recent study on behavioral targeting demonstrates the evolution of consumer behavior and its impact on segmentation in consumer marketing. While criticisms of profile-based segmentations are nothing new, the study in question provides a quantified demonstration of the extent of change in this area. It also provides figures and details on the various so-called &#8220;Behavioral situation-based&#8221; targeting in the UK. However, despite marketers&#8217; awareness of the weakness of profile-based targeting, the old segmentation methods are still the most widely used. Behavioral situation-based targeting, an alternative to conventional segmentation The fact that marketing is so fond of war metaphors is fascinating. Targets, campaigns&#8230; Beyond that, selecting the right target is always important &#8211; image produced with Midjourney and Adobe Illustrator. This study on behavioral targeting brings many insights, as it demonstrates how fluctuating consumer behaviors are, due to the evolving economic situation. Targeting by behavioral situation to better track changes What we find most interesting, is what the authors call &#8220;situation-based targeting&#8221;. As Murielle de Boisseson, Marketing Manager France at Treasure Data, explains: The results of this study confirm that hackneyed segmentation models that lock consumers into rigid, permanent groups simply no longer work This study provides a useful, in-depth complement to a previous survey on customer experience stereotypes conducted by Adobe and analyzed in our columns. Marketers are aware that traditional profiling-based targeting tools are no longer valid&#8230; but they continue to use them, nonetheless. But this study provides even more insights. It proposes a useful alternative to these traditional segmentation methods through what Treasure Data calls situation-based targeting. For the sake of clarity, we have formatted the study data into an infographic. We have included the characteristics of each of the main situations identified by the American software vendor&#8217;s study. Behavioral situation-based targeting in the UK (vs.US) Behavioral Situation based targeting by the numbers – Click picture to enlarge  This new, more behavioral segmentation reveals some particularly interesting points. The segmentation carried out by the study&#8217;s authors shows that different consumer groups are sensitive to different arguments. What&#8217;s more, the groups are fairly similar in size, demonstrating a real variety in behavior. This study also tells us that certain groups (practical consumers 26% and Generation C 18%) are more sensitive to customer experience (CX) than others. At the same time, the figures demonstrate the importance of experience. This underlines, that marketing generalizations are of little use. As in matter of fact, these groups are highly unstable, requiring the fine-tuning of the various segmentations over time. However, the authors of the study point out that in France, marketers are less inclined to review these targeting methods than in the UK and USA. Furthermore, even if marketers in France, the UK, the USA and elsewhere are aware of the weaknesses of old segmentation models, the techniques they use remain traditional. Changing one&#8217;s habits is not as easy as it seems. Many other interesting findings from this study can be found in the press release or eBook below. About the methodology of this study This behavioral targeting study is based on two surveys conducted by Opinium on behalf of Treasure Data. The first surveyed nationally representative samples in the UK and USA &#8211; 2,000 adults per country, for a total of 6,000 respondents &#8211; between July 3 and 6, 2023. The second surveyed 500 marketing decision-makers in B2C companies with more than 2 employees in France, the USA and the UK &#8211; 1,500 respondents in total &#8211; between July 3 and 13, 2023. Download the Treasure Data study Download the press release. The post Behavioral Targeting is the right way forward appeared first on Marketing and Innovation.

The green market revolution (impacts, how to adapt and change retail business models and organisations) was the title of Forrester&#8216;s Thomas Husson&#8217;s Keynote at the Paris Retail Week 2023 Trade Show. A topic of vital interest to our readers and ourselves. We interviewed Thomas to better understand how urgent the situation is, and how consumers and retailers are reacting. Thomas named a few best practices in this area. Not everything is perfect in that unfolding &#8220;green market revolution&#8221;, of course. Yet, a few businesses are making efforts, and they should be encouraged. The Green Market Revolution Is Underway We&#8217;ve had the Industrial Revolution, here comes the green market revolution. Change won&#8217;t be easy, though. Green market revolution, isn&#8217;t that an overstatement? This term is a reference to the industrial revolutions of past centuries. Environmental issues will force us to rethink a lot of things, including our business and organisational models and how we get around. As with the industrial revolutions, a great deal of innovation and investment in infrastructure are taking place. As with the railways during the Industrial Revolution, governments and regulatory changes are driving change. And this is just the beginning. We don&#8217;t quite realise it, but it&#8217;s happening. At Forrester we had rather talk about a green market revolution than CSR. Even though CSR is flavour of the month. Revolution is a more striking concept. It&#8217;s also closer to what&#8217;s really going to unfold before our eyes. Whether we like it or not, we&#8217;re heading straight for it. Yet you say businesses aren&#8217;t doing enough. Is it the economy, stupid? It&#8217;s a bit like what happened with digital. Initially, it&#8217;s an investment. And then, unlike digital, we have to measure the financials. And that&#8217;s not easy when the metrics aren&#8217;t in place. But things are changing. Costs are rising because of energy prices. And revenues can increase if you have the right positioning and consumers are prepared to pay a premium for greener products. As for those who can&#8217;t afford them, they may be frustrated and expect businesses to offer more environmental value for the same price. Thomas Husson at Paris Retail Week 2023 The basis of your thinking is the need to look at things positively Exactly! Opportunities exist, but they are not necessarily for everyone. But if we look at the triangle of inaction between consumers, governmental or local authority initiatives and businesses, everyone is waiting for everybody else. But at the end of the day, there will be companies that will turn this revolution into a competitive advantage. What&#8217;s more, when it comes to recruiting younger employees, getting one&#8217;s act together will be a must. What about the green market revolution in the United States? Traditionally, in the US one favours self-initiative and philanthropy. This green market revolution is less directly rooted in the heart of their business models. But things are changing. Above all, there is the IRA (Inflation Reduction Act), which has little to do with inflation, but allows companies to invest in innovation, in so-called battery gigafactories, solar panels and in renewable energies. Is this green market revolution reflected in consumer perception? If I take France as an example, around a quarter of French people are committed to changing their habits. They are very careful about what they buy, they research the topic a lot and they make a fair amount of effort. Yet, there&#8217;s also a good proportion of them who would like to do more but can&#8217;t because they can&#8217;t afford it or because changing one&#8217;s way of life is too difficult. Awareness is on the rise. That said, it&#8217;s difficult with double-digit inflation at the grocery store. A third of households are tightening their belts. Reducing the use of fossil fuels isn&#8217;t their main priority. Six of the nine planetary boundaries have been crossed. Will it speed up the green market revolution? I think that very little is known today about these nine planetary boundaries and that the focus is very much on carbon emissions and climate change. And that&#8217;s fine, because we&#8217;re now beginning to measure these indicators, but it&#8217;s difficult to keep track of the other issues. What&#8217;s important, I think, is to make people aware, particularly managers, that all the boundaries are linked with one another. These issues are extremely complex and technical and should not be taken lightly. Because resources are in short supply. Ultimately, there is a lack of education and knowledge on these subjects. On the management side how is this green market revolution perceived? Let&#8217;s face the music, things aren&#8217;t going so smoothly. We&#8217;re faced with short-termism and profitability issues, with soaring costs, inflation and a number of crises on top of each other. Getting to grips with the fact that all factors are linked with one another can help you grasp how strategic these issues are. But change will not happen overnight. I believe a minority of businesses manage to change their models and yet remain profitable. They will be the winners, ultimately and they will increase their market share. Who are the best practices? Albert Heijn is one of the traditional retailers in the Netherlands with the most ambitious scope 3 reduction targets. Albert Heijn has set his sights high as the Dutch company aims to become B Corp™ This is important because Scope 3 is the impact of carbon emissions linked to the company&#8217;s value chain, its consumers, the purchase of goods and services and haulage. So this is where it all happens. For major brands like Dannon, above all, it takes place in the agricultural and production value chain. As a result, their targets are among the most ambitious of all. Dannon has been granted the B Corp™ certification label. It means something about their commitment and that they are going to set up processes and audits to meet a certain number of fairly strict criteria. And the beauty of this label is that businesses must recertify every three years. Green market revolution: the B Corp label is more than a certification Danone says on its website. L&#8217;Occitane en Provence has also become a B Corp™ across several countries and brands. This takes time, and employees have to commit too. The upside is that it also has an impact on talent acquisition and retention. One must understand it&#8217;s not just consumers who suffer from cognitive dissonance. When you work for Total Energy, Shell, BP or other polluting companies, you may not feel very comfortable in your job at all times. Decathlon Is Also Among the Leaders of the Green Revolution You can feel that this group has a real wish and vision. It is a totally different ballgame. They also use this approach to differentiate themselves. It&#8217;s the third-largest sports company after Nike and Adidas. They also market their products under their own private labels and are streamlining their brand names at the moment. Of course, nobody is perfect. But we can see that they are making significant efforts. Second-hand Shopping Is Booming, Too The fashion industry is a complex one, and many have gone out of business. Most players have launched initiatives in the direction of second-hand shopping. There is also a huge demand for low prices. They have created shelf-edge programs for second-hand products. Yet second-hand shopping is challenging what with product returns and logistics. And it causes issues in terms of scalability. In France, Kiabi is one of the few retailers that are really scaling up and rolling out second-hand shopping throughout a large number of stores. A Framework for a Green Market Revolution It&#8217;s a framework for the marketing, strategic marketing, innovation and product departments. Our approach is holistic and cross-functional. Forrester&#8217;s framework around the green market revolution. The idea is to align the company&#8217;s value proposition with its purpose, so that it goes beyond lip service. There must be an impact on business models, on the offering and products. Then we add ecodesign, cocreation and consumer support. This makes up for a more positive story for consumers. Beyond storymaking it also enables them to become change agents. The post Forrester&#8217;s Green Market Revolution appeared first on Marketing and Innovation.

The learning curve that governs qualitative marketing studies is pivotal if you want to avoid ending up with mountains of useless data. What&#8217;s more, the kind of data produced by marketing studies is unstructured and complex. Gathering too much of it will also inevitably lead to soaring costs. Our simple methodology based on the experience curve of qualitative studies is a good starting point for making the most of these studies. What&#8217;s more, it&#8217;s a good means of improving effectiveness. The Learning Curve of Qualitative Studies After 10 to 12 interviews, insights derived from your interviews will plateau. This is when the qualitative marketing studies experience curve has been reached. This method is straightforward. I owe it to my erstwhile marketing teacher, Pierre-Louis Dubois, a reference in the field of market research and the author of many marketing books. This methodology is all about effectuation. Qualitative Studies and the Effectuation Principle Be it for qualitative marketing studies, or in business in general, one must become familiar with the principle of effectuation. One could sum this notion up in just a few words: &#8220;The right effort for the right result, no more, no less.&#8221; Common Sense and Qualitative Marketing Research First of all, one must point out that the literature in this field is fairly confusing. Some authors recommend sampling strategies very similar to quantitative sampling strategies (Frisch, 1999), others aren&#8217;t quite sure and will tell you &#8220;it depends&#8221; (Quinn Patton, 2002, Qualitative research evaluation &amp; methods), and others avoid answering the question altogether (Giannelloni and Vernette 2017). It would seem that there is a space here for some down-to-earth commonsense for professionals wishing to do things right without giving in to hyperbolic methodological madness. Because of the learning curve, we always aim for 12 interviews (10 when that&#8217;s not possible), dividing up the audience for our qualitative study to maximise our results. No need to organise hundreds of interviews, despite what some marketing pundits are suggesting. Step one in qualitative studies The first thing that strikes me when I see the work of students (but also that of some professionals) is that most of the time, they are off to a very bad and uncertain start.  More often than not, they fail to understand the requirement for an initial level of investigation that could save them time and effort. Expert interviews, for instance. Be they internal or external SMEs, or both). No need to talk about sampling at this stage. The aim is different. You&#8217;re merely trying to make sense of a very fuzzy picture. A good starting point for qualitative studies is to start with expert interview. What about starting with those who have written books on whatever subject you are researching? Phase one of a qualitative study consists in a non-directive survey. You&#8217;ll start with a few assumptions and a list of questions that you&#8217;ll refine as you go along. This is not an in-depth questionnaire per se.  By definition, you&#8217;re not an expert in the field that you are researching and you need to understand it beforehand. Desk research and expert interviews  are a sound starting point. This will help you polish your recommendations to your clients or your business partners. The more diverse these experts, the more valuable their insights. Insights derived from such expert interviews will help you improve your understanding of the field of your research. Such insights will also help you hone your assumptions before you launch your qualitative study. Knowing what to search makes up for 50% of the success of your survey.  Too many professionals ignore that first stage. It&#8217;s a shame because it&#8217;s also one of the most rewarding. It will, eventually, ensure you save time and effort and will yield better results. By the way, with this initial phase, you will be in touch with various experts. These connections might prove useful in the future, you never know. In-depth Qualitative Studies  Time and time again, I was able to verify in the field what I&#8217;d learned from my time in business school. Studies always follow more or less the same pattern. This isn&#8217;t rocket science, it&#8217;s merely what I derived from experience.  After the initial desk research and expert interview stages, you can then go out and interview larger samples. It&#8217;s not necessary to interview more than 10 to 12 people. You may interview more if you wish, but be aware that this will not yield many more insights.  The Learning Curve in Qualitative Research This is known as the &#8220;learning curve&#8221;. I haven&#8217;t found any scientific evidence for it, apart from the lectures I attended when I was at school, but I&#8217;ve been able to confirm these figures almost every time I&#8217;ve had to carry out an in-depth interview guide in the field. It always worked for me. Whatever the subject. The learning curve always peaks after 10 interviews, sometimes 11 or 12. Most of the time after the 12th interview. Beyond that, there&#8217;s no point in contacting more interviewees. There is one proviso to that, though. Your interview guide must be well written and you must listen carefully to your interviewees. What this means is that your interview guide must be the same for all. That there is no bias in this guide. Lastly, it implies that your questions must be consistent throughout your interviews. Beyond this qualitative study process, you will have to resort to a quantitative phase to gain a deeper understanding of your subject. Sampling interviewees I recommend you divide the number of people surveyed (let&#8217;s assume there are 12 of them) into as many subsets as possible in order to draw conclusions for each small subset. You will be able to quantify each of these subsets with your subsequent quantitative survey. You don&#8217;t need to make these subsets representative. But you need to have enough of them to gather consistent points of view. Once again, no rocket science here, merely field experience. Should you interview 12 times the same kind of people, the insights you will derive from these interviews will be unbalanced. What you want to do in the in-depth interview phase is to bring as many points of view as possible from the 12 you&#8217;ve collected, to bring different perspectives into your research. For instance (in B2B) 3 employees for the internal view, 6 buyers from 2 different subgroups for the external view and 3 resellers. [Note that the same applies to B2C, replace &#8220;buyers&#8221; with &#8220;consumers&#8221; and resellers with retailers, for example]. Sample size for qualitative marketing research If you need more insights, bringing in other subsets and expanding your sample beyond the limit of 12 might be a good idea. That is if you think you&#8217;ll have enough time to conduct these interviews, make the transcriptions, analyse the results, and that there will be a benefit for you. In any case, if the insights you are getting are repetitive, put an end to that interviewing process to save time and money. Finally, remember that qualitative studies only provide insights. Such insights will have to be verified and quantified at a later stage. They should never be taken at face value. In fact, they are often used as the basis for the forthcoming quantitative study. Customer Interviews With regard to customer interviews, one will be essentially looking for very precise, in-depth customer insights on a subject that isn&#8217;t necessarily quantifiable. Besides, when dealing with hard-to-sell products or services (aka complex selling), customers are by definition in short supply. Samples are therefore too small to conduct quantitative interviews. In this case, statistics won&#8217;t help. Qualitative studies are, in that case, a no-brainer.  Qualitative studies can also be used for customer feedback in order to improve customer experience Beyond that, the methodology is pretty much the same. The content of your interview guide, however, will be very different. It will be all about the relationship between your customer and the business and/or its representatives. Each case is unique, there are no one-size-fits all interview guide for customer interviews. The Why and the How First of all, you need to define why you are interviewing your customers. Is it about measuring product or service quality or product-market fit, assessing relationship quality, understanding customer issues, or anticipating a change in market needs? Unless it is far more specific. In the case of the launch of a new offer, product, or service, for instance. You will also need to know how to decrypt when customers are paying lip service to your questions. Namely when a sales rep is facing her customer during the interview. Buyers may not always feel comfortable when criticising vendors openly. In this case, customers will most probably refrain from opening their kimonos, in order to retain their bargaining power.  Thus, you&#8217;ll also need to decode your customers&#8217; words to understand false and true arguments. Reading between the lines is a must in that case. Cross-checking information will also be necessary. In other words, it&#8217;s quite difficult to carry out this type of survey if you&#8217;re on the vendor&#8217;s side, to maintain enough neutrality when you are being criticised. Frequently however, customers&#8217; strong feelings are the most valuable and useful form of feedback if you want to improve your offer and service quality. The post The learning curve of qualitative studies appeared first on Marketing and Innovation.

AI may be fun to play with but its implementation within businesses is a tad more complex. Visionary Marketing attended a round table discussion on the impact of generative AI on businesses and the future of work at Big Data AI Paris in September 2023. AI and IT experts from different backgrounds were able to shed some light on the dos and don&#8217;ts of the integration of artificial intelligence within enterprises. In a nutshell, there are four main areas one should never overlook: 1/cybersecurity 2/experimentation 3/problem-solution match 4/learning from failures.  Dos and Don&#8217;ts for AI implementation within businesses (Pictured Above) A Big Data AI Paris 2023 panel on the implementation of AI and especially generative AI in businesses. From left to right: Thomas Pagbé, Stéphane Roder, David Sebaoun, Valentine Ferreol and Nicolas Levillain. &#8220;AI is old hat,&#8221; Thomas Pagbé, editorial manager of leading French IT for Business publication, announced quite rightfully in his introduction to the round table on &#8220;The future of work transformed by AI&#8221;. He raised the question as to what had really changed in how businesses viewed artificial intelligence. To answer this question and document the theme of the round table, he had brought together a panel of IT experts from diverse and complementary backgrounds: Stéphane Roder &#8211; CEO of AI Builders David Sebaoun &#8211; Executive Partner at IBM Consulting France Valentine Ferreol &#8211; Digital Factory Director &#8211; interim CIO at Citeo Nicolas Levillain &#8211; Managing Director at BCG Platinion France What has really changed in companies&#8217; vision of AI implementation What better way to describe the state of artificial intelligence in business in 2023 than with a generative AI like Midjourney? Here&#8217;s a vision that&#8217;s both futuristic and at the same time rooted in a 1980s-style Blade Runner view of the past. What if the future were a lot more like the past after all? Sure, there were ChatGPT&#8217;s 100 million near-instant users, but is that sufficient to tell you how to implement AI within your business? Even if the numbers are huge, the share of visits to AI sites, compared to others, remains modest. What&#8217;s more, ChatGPT&#8217;s visitors are said to have been fewer since July. Andreesen Horowitz have published a study that set the record straight. Let&#8217;s return to our round table with this comment from Stéphane Roder. &#8220;Starting in 2016 we saw AI applications finding their way into our business processes and bringing value. This gives good results if you put them in the right places.&#8221; The Democratisation of AI Is the True Revolution So AI has ceased to be a technical subject, it’s become strategic. Increasing operational efficiency, designing new offers&#8230; So many subjects once reserved for humans alone have now found a co-pilot as the name goes. The role of generative AI is undeniable, and should not be underestimated, in the &#8220;democratisation of artificial intelligence. Because it has made models available to users,” explained BCG&#8217;s Levillain. Therefore, there is an undeniable generalisation of access to AI, but when it comes to disruption, we&#8217;re still not there, as IBM&#8217;s David Sebaoun points out. &#8220;Everything has changed and at the same time nothing has changed in AI,” he told us. &#8220;For me, generative AI is incremental.” For the IBM representative, it&#8217;s quantum computing that will &#8220;cause disruptions”. No doubt, it&#8217;s all a matter of appreciation. Could this be the sign of a new era for the leading American IT company? Watson had often been presented as a revolution that has recently come to a rather unhappy end. Be that as it may, many AI experts agree on its &#8220;incremental&#8221; diagnosis. In fact, whether or not we believe that ChatGPT is &#8220;revolutionary&#8221; &#8211; the history of technology will take care of that question without our comment &#8211; what&#8217;s important is that &#8220;we offer useful and effective solutions to decision makers”, added Valentine Ferreol. The ultimate goal is to implement &#8220;both operational and decision-making issues, in a collective manner”. This is a task in which Valentine sees “CIOs playing the role of technologists vis-à-vis other departments, such as marketing or finance.” For my part, I see it as a reminder not to give in to technological “revolutions&#8221; too fast. Neither today nor tomorrow. Do CEOs need to be convinced of the importance of AI? Is there still such a need for evangelisation in AI? According to Stéphane Roder, this has already been done by OpenAI and Microsoft, and we can only be impressed by how fast they did the job. Even if Joe Bloggs&#8217;s mastery of these tools remains uncertain. Where does artificial intelligence stand in terms of insecurity? A Gartner study shows that for US companies, Generative AI is cited among the greatest risks. The Ultimate Goal of Artificial Intelligence Technologies Like Valentine, Stéphane Roder insists on the ultimate purpose of these technologies. &#8220;The question is whether it really adds value, or whether it&#8217;s a toy. CEOs want to quantify the contribution of technologies and answer questions about confidentiality.” It&#8217;s a fundamental problem, but one that is &#8220;In the process of being resolved,” he told us. I am under the impression that such issues are still only seldom addressed. That being said, Stéphane foresees &#8220;massive adoption, because ML allows you to do exceptional things”. Nicolas Levillain confirms, &#8220;We&#8217;ve moved on from the need to convince CEOs to an effort to educate them to ask what they can get out of these technologies. Rethink how they work and how they interact with their customers, and find out if they can create new business.” And he adds that this is work that BCG X, the techno arm of the famous consultancy, is carrying out with banks. It remains to be seen whether this movement with banks, sometimes carried out in a more than brutal manner from a human point of view, is due to the ongoing transformation of the business or to a miraculous and timely technological invention. AI implementation by Industry David Sebaoun has another explanation. For him, it&#8217;s the fact that banks and insurance companies are entirely based on IT and technology. Admittedly, but it&#8217;s probably not the most cutting-edge technology &#8211; otherwise what need would most established continental financial institutions have to buy budding pure players? Banks were certainly the first to face new entrants, as he points out, but the digital transformation of banks, which we were calling for ten years ago, is still largely incomplete, to put it mildly. [Above] The most up-to-date statistics on the use of generative AI by generation (not much impact there) and by industry (No! Healthcare isn&#8217;t the most likely candidate for AI implementation)&#8230; Our panellists argued that the evangelisation for AI is over, yet these figures are telling a very different story.    If banks are so interested in Generative AI, it&#8217;s seemingly more to catch up with the movement indicated by Chris Skinner, which began in the UK and spread worldwide around the time of the 2008 crisis. ChatGPT is a good excuse for cutting cost quickly and effectively. Where does artificial intelligence stand in terms of productivity gains? So what impact will generative AI have on the workplace, and on productivity gains in particular? According to JP Morgan, it will be enormous. Working hours will shrink, and decision-making processes will be turned upside down. In short, it will be total disruption. It&#8217;s rather difficult to be that adamant when talking about such subjects, though. Changes won&#8217;t happen overnight, contrary to what we hear. Wait and see before it happens. The Big data and AI Paris 2023 panellists, however, have observed productivity gains. &#8220;BCG X analysed customers using generative AI in their software factories,” says Levillain, &#8220;and we witnessed 40% productivity gains, improved quality, and fewer bugs.” Here again, not everyone agrees, starting with Thomas Gerbaud, Data Scientist, developer, and IT blogger. Generative AI does produce code, but is it useful? Sometimes, according to a few studies (Chen et al 2021, Cassano et al 2022, Buscemi et al 2023). I don&#8217;t find it all that interesting! The data scientists I work with prefer to think on their own and when they hit a snag, browse Stack Overflow for an answer. Alt-Gr.tech &#8211; the end of code Valentine Ferreol agrees with him: &#8220;It&#8217;s true that AI can generate code, but only simple one,&#8221; she said. This is not to say that ChatGPT and its competitors can&#8217;t help us generate code. But it does mean that they may be of more interest to novices and tinkerers and that pros have other ways of getting things done. Above all, this means that it&#8217;s too early to panic, and that time will tell whether the productivity gains will be that enormous, for what it means, for white-collar workers. By the way, the latter were already facing problems before the recent AI boom. What&#8217;s more, some jobs disappear, others are invented, it&#8217;s always been the case. Let&#8217;s get straight to the point that interested me most in this round table. Learning from AI implementation failures. Learning from the post-mortems of AI projects Here are the lessons I&#8217;ve learned from the panellists&#8217; analysis of these AI implementation post-mortems. Securing AI is more difficult than you might think (Nicolas Levillain): new AI technological frameworks seem easily accessible, and may give rise to the temptation to move very quickly in order to gain a competitive advantage. However, Making this new breed of applications secure requires a great deal of testing. Holding one&#8217;s horses is Levillain&#8217;s advice. Experimentation vs ratiocination (Valentine Ferreol): Valentine reminds us of the basics of innovation, and particularly digital innovation: success is best guided by trial and error rather than theory. Familiarising oneself with a new technology is the right starting point, whereas possible applications come next. This rule of thumb &#8220;allows us to innovate faster, as long as we dynamically exert our critical eye&#8221;, she says. We couldn&#8217;t agree more. ‘An LLM isn&#8217;t a hammer to crack a nut’ (Stéphane Roder): Stéphane reminds us that we witnessed a lot of epic failures in AI until 2019. It seems to have died down for a while, and now it&#8217;s picking up again. &#8220;Everyone wants their XXXGPT,&#8221; he said, &#8220;French Railway completely screwed up with their implementation, he said.” We&#8217;re just discovering the underlying technology, and while &#8220;these models are fun to &#8216;play&#8217; with, that&#8217;s not how it works within businesses”. He insists on the learning curve for professional IT teams &#8211; &#8220;We know of big e-commerce players who are now working hard on shrinking their data models. Ninety percent of use cases are indeed queries on simple document databases,” he warns us. A reminder that the size of data models is no guarantee of success and that there are many setbacks when databases become oversized. Even ChatGPT is taking a step backward with ChatGPT4, which is reportedly less powerful than ChatGPT3.5 in some areas and sometimes declining over time for certain tasks (download the Stanford report about the evolution of ChatGPT&#8217;s behaviour overtime). There are three AI implementation failure factors which must be stressed (David Sebaoun): a) Lack of adoption, b) Implementing POCs (Proofs of Concept) rather than pilots hence failing to scale (a main reason for failure in Big Data projects we already spotted 10 years ago or so), c) Lack of governance (for example, those US states that used AI-based unemployment fraud detection systems and ended up favouring fraudsters). As Levillain rightly concludes, &#8220;The key success factors for these types of projects are 75% human. The remaining 25% depends on technology.” So far, I feel as if I were attending a project management course back in the &#8217;80s. Yet Nicolas added: &#8220;What has changed is that these models bring together people who didn&#8217;t talk to each other before: data scientists, business people and developers.&#8221; Granted! In that case, I agree that this is a revolution, one that we&#8217;ve been awaiting for decades. Let&#8217;s hope he&#8217;s right. The post Implementing AI within businesses appeared first on Marketing and Innovation.

Visionary Marketing spoke with Katja Graisse – co-founder of Balistikart, an independent digital creative agency – to discuss the Incognito AI Influencer Project. We touch on the benefits and limitations of AI-generated influencers, the intertwining of art and business, and more. Katja provides an optimistic assessment of the current digital landscape, proclaiming that the usage of AI allows for media involvement that is more flexible, sustainable, and ironically, honest. However, not everyone is transparent about AI personalities &#8211; some have used deepfakes in intentionally deceptive ways, causing many to question the technology&#8217;s true impact on our lives. What the future has in store for virtual influencers is unknown at this point.   The Incognito AI Influencer Project Picture taken from the Incognito AI influencer project Do you want to give us an introduction and describe what the Incognito influencer project is? We created the Incognito influencer project at the start of New York Fashion Week this year with the idea of creating an AI-generated influencer who would be able to engage with the Parisian fashion world from behind our screens and live all the different experiences that Fashion Week can offer through this influencer. From there on, we tried to decipher some of the trends that the Fashion Week was having and try to put those into the storytelling that we give to this influencer. The whole notion that he is incognito is kind of paradoxical because the aim of most influencers is not to be incognito. Quite the contrary. And in this case, we wanted to have somewhat of the fashion underdog who is trying to infiltrate the fashion world as best he could. Where did you first get the inspiration for a fashion underdog? Every year we collect information and create a report on the fashion weeks, and so the idea was to be able to look at them from the front row and then be able to report them in a more fun way than we usually do through our trend reports. It was also a desire to be able to test AI technology, as we know that this is up-and-coming and that we&#8217;re now seeing more and more AI-generated influencers that have been appearing and we wanted to basically test and explore and therefore the term project behind Incognito influencer. Picture taken from the Incognito AI influencer project What advantages do you think virtual influencers have over real-life influencers today? I think virtual influencers are perhaps a little more docile than real influencers. They don&#8217;t have whims or demands when it comes to flying first class or what have you.  It also is interesting when it comes to the luxury industry, which likes to have control. When you have an influencer, you can actually pretty much control all of the parameters. The other idea was to have a project that was proof of concept. And so we wanted to start an influencer with zero followers and not do any media spending or such. All that we&#8217;ve done so far has been organic, and we&#8217;ve seen that the response has been tremendous from all of the followers. You&#8217;ve described the project as art. Would you say that this starting-from-scratch philosophy is related to this in some way? I think the whole idea was to create a character that&#8217;s all about autofiction. You have the author, the character himself, the prompter &#8211; all of these are one single person. It goes back to Stefan&#8217;s early work where he had created a film that was presented at the Centre Pompidou in Paris &#8211; which was for the Mobile Film Festival &#8211; a 45-minute film called Autofiction. All of this relates to the kind of work that we like to do. We didn&#8217;t want to create an influencer that was trying to gain a tremendous number of followers. We&#8217;re more interested in the storytelling that goes with his character as he moves from all of these different fashion shows. It&#8217;s not the idea of coming up with someone who&#8217;s perfect &#8211; quite on the contrary &#8211; and it enables us to create lots of different fictional characters and situations that might not be truly there in reality. Have any brands shown interest in this project? Indeed, we have had some people come and reach out to us. I can&#8217;t necessarily disclose who they are at this point, but people who are interested in the concept and who have noted maybe the irony that was involved. We have the hope that we can also team up with younger brands that don&#8217;t necessarily have the traction that these major luxury brands may have for the moment. It will be interesting to see how we can perhaps do partnerships with younger brands and put forward their work. Picture taken from the Incognito AI influencer project Who would you describe your target audience as? We have seen that we have a large scope of different demographics. We have Gen Alpha, who is following. We have Gen X who is following, and then we have a whole bracket in between. Mostly male by 60%, but all sorts of age groups and areas around the world. Virtual influencers tend to have a higher rate of engagement than real-life influencers. Have you noticed this trend yourself? Certainly, I think the engagement so far has been really, really high. We&#8217;ve had about 100,000 impressions and about 350 comments. Quite a good number of likes as well. We see what we are calling &#8220;the club of 100&#8221; &#8211; we have 100 followers who are extremely engaged, both on Instagram and also on Stéphane&#8217;s LinkedIn. Stepping back a little, how would you describe an influencer in today&#8217;s world? If we look at traditional influencers today, they have become extremely subject to monetization. A lot of them today are basically following this very specific mold &#8211; if you go from one to another, it&#8217;s very hard to differentiate them. You do have some exceptions, but on the whole, it&#8217;s become pretty much stale and I think perhaps a little shallow. We believe that with AI-generated images, you can actually create a character that has a lot more say to it, that has a lot more opinion. Influencers today are often less outspoken than they were in the past because they don&#8217;t want to bypass their VIP invitation to a dinner or something. So, I think that it is perhaps time to rethink what “influence” is. How would you describe the distinction between a virtual influencer and a fake profile? I think the idea of a fake profile is someone who is trying to mislead you. The whole notion of a virtual influencer is that they should be very outspoken about the fact that they are not real and that they&#8217;re not trying to mislead you in any kind of way. It’s very important to be transparent and to immediately say “I am a virtual influencer.” We&#8217;re dealing with a fictional character, rather than a fake personality who&#8217;s trying to impersonate someone or use someone else&#8217;s face to gain popularity. In the case of the influencer incognito influencer project, the whole idea was to actually take Stéphane Galienni’s own face from an ID photo and paste that into all of the AI-generated images that he was doing. He is the real Stéphane, but he&#8217;s just interested in a different environment with a different type of clothing. His looks are constant, which is something that other AI influencers sometimes lack. Is the usage of bot accounts different between virtual influencers and real-life influencers? Bot accounts have been around for a very long time. However, they do not bring any kind of true engagement to an account because these are fake people, so they&#8217;re not engaging with your content in a real way. Having a large number of followers can fool some, but most people are pretty savvy with the fact that if you have a very large following but you have no engagement, no comments, and no likes, then there&#8217;s something probably a little fishy about your account. In our case, I don&#8217;t think that’s something that we were interested in. The whole idea was not to gain a huge amount of coverage or followers in order to get free tickets to something. The whole idea was to experiment and to be authentic. In the coming months or years, where do you see this project leading? I think time will tell. First of all, it takes a lot of energy to be creative on a regular basis. Not only are you generating 1 to 3 images a day, but you have to keep in mind that behind one image, maybe there are 50 trials before we come up with the right one. But people are really eager to see where it will go, and I think everything is open at this point. We sincerely believe that we can do something very creative and very DNA-specific to each brand because the whole idea of creating something from scratch is that you can really try and follow the brand codes, the color schemes, and the whole idea behind the brand. The ideas are so open, and we are certainly on the side of people who are saying, “Wow, technology is really extraordinary. Let&#8217;s play with this and see what happens.” In conclusion, a few thoughts about this AI Influencer project The advent of AI influencers certainly raises questions about how authenticity is defined in today’s digital landscape. The idea that an AI influencer can represent authenticity and transparency challenges our notions of the concepts &#8211; perhaps we must look beyond physical properties to assess creditability. Skepticism of the technology is natural, however – as Deepfakes permeate the digital world, critical evaluation of informational sources is paramount. With an eye to the future, we hope that AI influencers will continue to represent a refreshing spin on a medium, rather than a catalyst for a disinformation campaign. The post An AI Incognito Influencer in the digital world appeared first on Marketing and Innovation.

‘Breaking down silos’ is certainly digital transformation’s most common phrase and myth. If you haven’t heard this phrase repeated over and over again, chances are your government hasn’t let you out of your house after the Covid-19 pandemic. It’s probably the biggest fable about transformation projects, be they digital or not. Some time ago, I delivered a presentation about the necessary cooperation between marketing and CIOs, which triggered an interesting piece in France’s leading economic newspaper, Les Echos. In this piece, the journalist reported that I was advocating the implementation of organisational silos to achieve better results. Obviously, he had completely misunderstood what I had said, unless I hadn’t made my point clearly enough. What I meant was that organisational silos couldn’t be broken down, that most of the time it served no purpose to even try and I further explained that intrapreneurship provides an interesting answer to this digital transformation dilemma. To top it all, it does produce far better results.  Why Breaking down silos is digital transformation’s greatest myth and how to overcome it Breaking down organisational silos. An old pipe dream. At the same time, silos are a ready-made excuse for those failing in their digital transformation endeavours – image produced with Midjourney. My presentation was highlighting that there is no point in breaking down silos to facilitate digital transformation. It can even be counterproductive. What I meant above all is that organisational silos are neither good nor bad. There are both good and bad things in them.  Silo-breaking is a great subject for after office hours pub discussions, that’s all. One had rather embrace intrapreneurship. Here is why. Breaking Down Organisational Silos: The Biggest Myth in Digital Transformation Intrapreneurs are like scrum halves in Rugby Union. There are short players but fast and agile. Intrapreneurs do not even attempt to break down silos, because they are too busy leading the pack and have no time to waste. Image by antimuseum.com Organisational silos are neither good nor bad There’s no point raging against organisational silos, it’s a waste of time. Let’s wonder why they exist. Organisational silos are created, or even recreate themselves, naturally, to protect business units’ ivory towers. That much is understood. Yet, if they are ever reappearing this is because they are the easiest way to do and manage business. They also make measuring and reporting on results far simpler. Now, I did witness business owners explain that matrix management was more effective and I spent months on end trying to map such complex organisations inside information systems. The sad truth is, though that it’s not true at all. More complexity in an organisation leads to more tensions, limited measurability and poorer results. Incidentally, it also leads to more expensive and malfunctioning information systems. Breaking down organisational silos is easier said and depicted with Midjourney than done in real life Besides, you cannot force people to collaborate just by throwing a matrix organisation chart at them. Working across departments happens naturally and effortlessly … provided that all parties reap a clear benefit from a cooperative endeavour. Matrix organisations and work efficiency Anyone who has worked within matrix organisations knows how inefficient and inflexible they are. As a young project manager, I spent months on end mapping sales and marketing processes for the design and implementation of CRM systems. When everyone reports to everyone else and everyone works with everyone else, achieving satisfying results and measuring them aren’t a piece of cake. Besides, efficiency gains aren’t necessarily as good as you might hope. There are some exceptions, but they’re few and far between. That’s why, whether you like it or not, organisational silos are here to stay. You’d better get used to them, they will not go any time soon. No organisation is perfect In fact, anyone who has studied organisations in the context of innovation knows that no organisation is perfect, and that no type of organisation solves the difficult question of innovation within a company. The key to achieving the difficult balance between creativity and process efficiency lies not in organisational charts, but in people, their skills and the leadership that fosters – or fails to foster – innovation. Cross-functional innovation work can and must gain a foothold across organisational silos That said, cross-functional work, is a prerequisite for innovation. To thrive, it must gain a foothold across these different silos, circumventing all the issues related to those organisational silos. While I highlighted the above-mentioned benefits of silos (to some), I do not intend to glorify silo-based organisations either. What this really means is that it is not necessary to ‘break down’ silos, nor business units, let alone get rid of the people who work within them in order to innovate. This is what I describe as the ‘scrum-half’ approach. For those who are not familiar with the game of Rugby Union, a scrum-half is a comparatively shorter player but faster and more agile than the rest. More often than not he (or she) is the one distributing the ball here and there and organising the attack. He or she isn’t the biggest or the heaviest, by far, but is certainly the smartest and fastest runner. I should know, I once was one of them. Intrapreneurs really know how to ‘break down’ silos This is where intrapreneurs come in. It’s not a typo, an intrapreneur is a person, or group of people, who manages to innovate in an entrepreneurial way within a (usually large) organisation. To succeed, intrapreneurs do not give a damn about organisation models. Organisational charts may change from day to day, or more often than not disappear altogether. I’ve seen it happen ever so often. Organisations change so quickly that no one can keep up with the changes and by the time the chart has been updated a new change in management occurs. Intrapreneur Overlook Organisation Structures Do not misunderstand me, intrapreneurs are no anarchists nor seek to overthrow the powers that be even though some people may think so. They do not care about organisation structures because it doesn’t matter. I’ve almost always found it laughable. While many of my colleagues were waiting for, as they said, ‘someone to clear out the organisation’, I’ve always found that these moments of uncertainty are most favourable to change management (read this piece about my change management tips). Four main organisational models for innovation but none of them is good Even when you’ve broken down silos, your projects do not always cut the mustard. Above are the four most common types of organisation for innovation. In 2010, Paris-based consulting firm CCA demonstrated that none of these organisations is good or bad. All that matters is leadership and implementation. Results are not impacted by these models in any way, they found. The only thing that matters for innovation – and digital transformation for that matter – is the quality of the people working for an organisation. Scrum-half way, agile intrapreneurs who are bound to work across silos, move quickly and circumvent issues others had found unsolvable. Intrapreneurs fight against no one, they just get things done True intrapreneurs aren’t fighters. They have no enemies. Their only foe is status quo. They even grow a stiff upper lip in front of resilience to change. To do this, he or she will solely rely on people’s skills, let self-organisation take place. Based on the results, he or she might end up structuring his or her project organisation. It’s not even necessary. Most of the time, structuring innovation takes place later at management’s request, who often lean on the above-mentioned innovator/intrapreneur to do so. Breaking down silos? The innovator doesn’t ask please, he asks forgiveness (David Armano) In conclusion, intrapreneurs never beg for help, they never ask where they fit in the organisation nor whether they have been given the right ‘job description&#8221;. On the contrary, intrapreneurs get things done and later seek what change agents might be able to give them a hand. A word of caution: I do not mean that innovation cannot come from above. It is entirely possible, and it often does. Yet, even in that case, innovation requires intrepreneurship in the exact same way. Even if you have support from the top, be humble and work like intrapreneurs, because that’s the way to make innovation succeed. By definition, innovation is about getting things done differently. Hence, this is no surprise that you are bound to upset a few people down that route. We also recommend that you read our nine top tips for intrapreneurship and change management. The post Breaking down silos: digital transformation&#8217;s greatest myth appeared first on Marketing and Innovation.