AI at Work

What happens when companies stop adding isolated AI tools and begin redesigning entire business processes around AI employees?In this episode of AI at Work, I speak with Surojit Chatterjee, founder and CEO of Ema, which stands for Enterprise Machine Assistant. We discuss why the debate about AI replacing jobs often misses the larger business question: how should organizations redesign work when intelligent systems can coordinate tasks, access enterprise knowledge and complete workflows across multiple applications?Surojit describes this as “agentic business transformation.” Instead of giving every employee another chatbot or assistant, organizations can use coordinated AI agents to manage processes that cross departments, systems and approval chains. People remain responsible for setting boundaries, reviewing sensitive decisions and deciding when an agent has earned greater autonomy.He shares the example of Wipro, where an Ema-powered system called WiproNow supports around 240,000 employees across 65 countries. According to Surojit, it covers approximately 70 use cases spanning the employee journey from recruitment to retirement, connecting with over 100 enterprise applications.The reported results show why workflow-level automation matters. Average response times for employee requests reportedly fell from five days to less than five seconds, while employee satisfaction increased by almost 20 percentage points. Surojit also says the number of people needed for this work fell from roughly 1,000 to 550, with employees reassigned to other areas.We also discuss why companies do not need perfect data before beginning. Surojit argues that capable AI systems can identify contradictions, missing information and undocumented processes as they work. This can expose the informal knowledge that organizations often discover only when an experienced employee leaves or goes on vacation.Trust remains the deciding factor. Surojit compares deploying an AI employee with hiring a talented new colleague. Leaders provide context, test performance, review early decisions and gradually increase autonomy. Clear boundaries remain necessary for sensitive issues involving areas such as employee relations, healthcare or financial decisions.The practical lesson is that meaningful AI returns come from redesigning work across teams rather than measuring prompts, tokens or individual productivity gains. Is your organization preparing AI to own complete workflows, or giving employees another tool to manage? Listen to the conversation and share your thoughts with me.

Why are some businesses generating measurable value from AI while others remain surrounded by pilots, rising costs and impressive demonstrations that never reach daily operations?In this episode of AI at Work, I speak with Brad Hairston, Director of Strategy at SS&C Blue Prism, about the operational and cultural foundations that separate productive AI programs from expensive experimentation.Brad spent 30 years in consulting before joining SS&C Blue Prism around seven and a half years ago. He now works within the company’s Customer Zero program, which deploys SS&C’s automation technology internally before it reaches customers. Brad says the program has helped SS&C grow revenue by approximately one billion dollars without adding headcount.We discuss why AI programs should begin with the business outcome rather than the latest model. Brad explains why companies making progress connect their automation investments with corporate strategy, build on existing robotic process automation and create reusable governance, security, orchestration and measurement practices.Brad also challenges the idea that AI agents will replace every deterministic automation. Rules-based digital workers remain useful for predictable processes, while AI agents can support work that requires reasoning and adaptation. Combining both approaches can also provide greater control over cost.Our conversation examines what should happen before an AI agent receives permission to make payments, update customer records or initiate business processes. Brad recommends defined roles, limited permissions, human approval for higher-risk decisions, complete audit trails and an orchestration layer connecting agents with people, APIs and digital workers.We also discuss how companies can give employees access to no-code automation while maintaining common standards and oversight. Brad describes the federated model used inside SS&C, where individual business units build automations through shared platforms, templates and governance.For leaders feeling overwhelmed by daily announcements from OpenAI, Anthropic, Google and other providers, Brad offers simple advice: take a breath, return to the business problem and begin with a process where the outcome can be measured.Is your AI program building reusable capabilities with every deployment, or simply adding another experiment to the pilot queue? Please share your thoughts with me.

If anyone can produce a professional-looking image or video with AI, what will make audiences care about one piece of content over another?In this episode of AI at Work, I speak with Joaquín Cuenca, co-founder and CEO of Freepik, about how generative AI is changing creative work, business workflows, and access to professional production. Freepik serves over one million paid subscribers, while Joaquín says the platform attracts over 70 million monthly visitors.At that scale, Freepik has seen the difference between an impressive AI demonstration and a tool people can rely on for real creative work. Joaquín argues that generating something attractive is easy. Producing something that reflects a precise idea, maintains consistency, and creates an emotional response requires direction, judgment, and human intent.We also discuss what Joaquín calls the no-collar economy. His view is that lower production costs will allow individuals, smaller companies, and modestly funded creative teams to pursue projects that previously looked too expensive or risky. That could create opportunities for storytellers, photographers, audio specialists, performers, and other creative professionals. Joaquín also acknowledges that some existing roles will be affected as machines take over repeatable production work.For companies adopting creative AI, Joaquín recommends looking past licenses, activity, and content volume. Experimentation has value while teams are learning, but businesses eventually need to connect AI adoption with revenue, costs, brand performance, or another measurable return.We also consider the threat of AI slop. Better tools cannot provide taste, purpose, or a compelling story. As technical production becomes easier, those human qualities may become the greatest source of differentiation.Will easier production produce a new generation of creators, or will businesses fill every channel with forgettable content? Listen to the conversation and share your thoughts with me.

What does an AI first workplace look like when every employee has an agent but every person remains responsible for the outcome?In this episode of AI at Work, I speak with Alex Svanevik, co-founder and CEO of Nansen, about how his company is integrating AI agents into daily operations while retaining human judgment, security boundaries, and quality control.Nansen has around 80 employees, and Alex says each person has been given an AI agent. His own agent, Winnie, prepares draft agendas using previous meetings, company objectives, strategy, and cultural context. Alex then works with the agent to improve the agenda before the meeting begins.His use of AI extends beyond routine administration. Alex describes building the first version of a Nansen product through Telegram while walking with his daughter. By the time he returned home, the agent had created a working product that later became a command line interface used by thousands of people.There is also a lighter side to this deeply connected life. Alex and his wife occasionally use their respective agents to broker disagreements. As someone who has been married long enough to appreciate the commercial possibilities of automated diplomacy, I suspect this could become an unexpectedly popular category.The workplace message is serious. Nansen expects employees to use AI across much of their work, but Alex says the human must own the quality, output, and result. Employees cannot blame the tool for inaccurate, generic, or poorly reviewed work.Alex compares the review process with sending a disappointing meal back to the kitchen. The first output may be acceptable, but reaching a high standard often requires several rounds of feedback. He believes judgment and taste will become strong sources of differentiation as average quality becomes easier to produce.We also discuss the security tension surrounding workplace AI. Alex argues that companies must consider the risk of avoiding AI because attackers and competitors are using it. His preference is to provide employees with approved tools and safe environments rather than leave them to assemble uncontrolled alternatives.One of his most practical recommendations concerns machine readable information. Documents, code, designs, spreadsheets, and diagrams must be accessible to both employees and agents. Nansen has moved internal work toward GitHub repositories, Markdown documents, CSV files, and other formats agents can process.Making everything readable only by machines would create a different problem. People must retain the ability to inspect, understand, and approve the work. The aim is shared accessibility rather than transferring complete control to an agent.Evaluation becomes especially important when agents influence financial decisions. Nansen tests trading agents through backtesting, measuring whether they can interpret data, judge the significance of news, and produce profitable decisions. A separate optimizer or coach then recommends improvements to each agent’s strategy.Alex closes with four human traits he believes will matter in an AI first workplace: high agency, good problem selection, judgment and taste, and clear communication. Experimentation amplifies those qualities, provided people avoid unnecessary risk and retain ownership of the result.Could giving every employee an AI agent increase productivity while making personal accountability even more important? Listen to the episode and share your thoughts with me.

In this episode of Tech Talks Daily, I speak with John Nay, founder and CEO of Norm Ai, about Agentic Law, AI native legal services, outcome based pricing, and the proposed legal framework for companies managed by AI agents.John has worked on the application of AI to law and public policy for around 14 years. His research predates the current generative AI era and includes GovDeVec, an early attempt to train neural networks on legal and government text so they could identify concepts embedded across large bodies of policy information.The arrival of frontier language models opened a different category of legal automation. Deterministic systems can complete forms and apply fixed rules, but language models can also examine precedent and guidance before applying it to a new situation.John separates this work into three layers. The first covers deterministic rules and repeatable automation. The second uses model based analysis to interpret documents and apply legal guidance. The third preserves human supervision for legal advice, consequential decisions, client communication, and final approval.We discuss how this structure works inside an enterprise. An AI agent could conduct an initial compliance review of marketing communications against SEC or FINRA rules. A human professional would then review the findings and complete the determination.Norm Law applies a similar model to legal services. Documents received during a transaction can be processed immediately by AI agents, with the results presented to an experienced attorney. The attorney decides whether to contact the client, negotiate with the counterparty, request additional information, or move the matter forward.For John, the value includes time savings and broader coverage. A legal team conducting due diligence may lack the time or economic incentive to inspect and cross reference every document in a data room. AI agents can examine a wider set of material and identify inconsistencies that could otherwise remain unnoticed.Outcome based pricing changes the incentive structure. A law firm charging a fixed price can use AI to review additional evidence without adding hourly fees to the client. John acknowledges the limitations. Predictable transactions can be priced around outcomes more easily than litigation where scope, duration, and strategy may change dramatically.The operating model also creates new roles. Norm brings together practicing attorneys, legal engineers, and AI engineers. Legal engineers translate professional knowledge and client preferences into agent behavior, while AI engineers build production systems and connect agents with live workflows.Another part of the conversation concerns supervisory AI. As companies deploy agents that advise customers or take commercial actions, human reviewers may be unable to inspect every decision at machine speed. Norm Ai is developing agents that monitor other agents for compliance with laws, regulations, and company policies.We also discuss Delaware’s proposed Artificial Intelligence Company initiative. The regulatory sandbox would test a legal entity managed by an AI agent while retaining human involvement, capitalization requirements, disclosure obligations, and government oversight.John argues that autonomous agents will increasingly take consequential economic actions. The policy question is whether this activity develops within established legal systems or moves toward jurisdictions and technical environments offering fewer controls.The supplied episode brief also provides significant company context. Norm Ai recently announced a $120 million Series C at a reported $1.2 billion valuation, bringing total funding above $260 million. Norm says organizations representing over $30 trillion in assets under management use its technology for legal and compliance work.

How much productive time disappears because somebody misheard an instruction, missed part of a meeting, or could not fully express an idea?In this episode of AI at Work, I speak with Paul Lee, CEO of Bridge and InnoCaption, about communication friction and why it deserves greater attention in the workplace AI conversation.Bridge provides AI-powered real-time captioning, transcription, translation, meeting summaries, and meeting intelligence. InnoCaption provides AI and human-powered telephone captioning for eligible Americans who are deaf, hard of hearing, or have a speech disability. Paul explains how the experience gained from captioning over 30 million calls is informing Bridge’s approach to workplace communication.Research shared by Bridge says one in six working-age adults experiences hearing loss. It also reports that 37% of employees with hearing loss lose over five hours each week because of communication gaps, while nearly 20% lose over ten hours. Those losses can appear through repeated conversations, missed context, reworked tasks, and weaker decisions.Paul introduces the curb cut effect, named after the sidewalk ramps created for wheelchair users that also help parents with strollers, cyclists, and travelers carrying luggage. He believes workplace captions can produce a similar result. Technology designed for people facing the greatest communication barriers can improve comprehension, attention, and recall across a much wider workforce.We also discuss how accurate transcription can turn meetings into searchable company knowledge. Paul shares how his own team uses AI to consolidate brainstorming notes and reduce 100 ideas to a manageable set of choices. The system organizes the information, while people remain responsible for deciding what happens next.Paul also considers multilingual collaboration, AI translation that preserves meaning and nuance, and why AI ROI should include decision quality, participation, knowledge retention, and product development speed alongside immediate time savings.For business leaders, his advice is to understand work at the department, team, and individual levels before choosing a tool. Setting an arbitrary AI adoption target can create poor incentives, while studying repetitive tasks and employee frustrations can reveal where AI will offer genuine value.Where is communication friction quietly consuming time inside your company, and could accessibility technology help everyone participate more fully? Listen to the conversation and share your thoughts with me.

Would you trust an AI system to prepare your taxes if it could not reliably tell HMRC guidance from information published by the IRS?In this episode of AI at Work, I speak with Arjun Kumar, cofounder of Taxd, about AI tax automation, digital tax filing, and the continuing role of human judgment in regulated financial services.Arjun began his career at PwC after joining through a school-leaver program. While working in expat tax, he and his cofounder saw how professional services firms often relied on offshoring and annual cost reductions rather than sustained investment in technology. Their attempt to promote a different approach internally eventually led them to create Taxd during the pandemic.We discuss Arjun’s prediction that routine tax compliance will become increasingly autonomous. When the required data already exists across tax portals, bank accounts, payroll systems, investment platforms, and brokerages, software can connect those sources and complete much of the repetitive work. AI can also help review hundreds of transactions for landlords, sole traders, and small business owners.However, tax advice often depends on jurisdiction, personal circumstances, and overlapping rules. Arjun recalls seeing customers use AI as a tax advisor, only to receive guidance drawn from the wrong country. A confident answer from a chatbot can become expensive when HMRC and the IRS are discussing entirely different tax systems.Arjun explains why Taxd combines software and AI with access to human accountants. We also discuss real-time tax reporting, Making Tax Digital, privacy, anonymized data, and how patterns across tax filings can help customers identify relevant deductions and questions.For founders, Arjun shares why specialist edge cases can provide a strong opening. Taxd began with expat tax, using its founders’ existing knowledge to serve customers whose needs were often poorly covered by general accounting services.Could your business automate routine compliance while preserving human responsibility for the decisions that carry real consequences? Listen to the episode and share your thoughts with me.**The Team at TAXD have kindly offered a discount code for listeners of the podcast. Use TECHTALKS to get 10% off any tax filing services (please note, this applies to filing only and excludes our advisory services).

What separates an AI agent that becomes part of everyday operations from one that remains trapped inside an impressive demonstration?In this episode of AI at Work, I speak with Jacob Kosior, who leads client strategy at EliseAI. The company builds vertical AI agents for the housing industry, handling property management workflows such as answering leasing inquiries, scheduling tours, processing renewals, collecting rent, and coordinating maintenance.EliseAI says its technology is live across over six million housing units in the United States and Canada. Jacob brings an unusual perspective because he spent over a decade working in multifamily housing operations and was previously an EliseAI customer. He has experienced these systems from both sides of the relationship and works regularly with the operators using them.We discuss why the agentic AI debate often becomes trapped between exaggerated expectations and deep skepticism. Some people believe agents can already perform almost any task, while others see them as chatbots with a new label. Jacob describes a narrower and far more useful reality: agents completing repetitive workflows from start to finish, provided they have access to the right systems, operational context, and escalation routes.Housing provides several valuable examples. A conversation about unpaid rent may reveal that a resident is withholding payment because of an unresolved maintenance problem. Handling the complete situation requires an agent that can understand both workflows and connect the relevant information. EliseAI says the experience behind its agents includes over one billion conversations, helping the system account for edge cases it has previously encountered.Jacob also discusses what separates production deployments from AI pilots that never progress. Adding a chatbot to an existing technology stack may answer basic questions, but it rarely changes how work gets done. An operational agent needs access to the systems, data, and context required to resolve a problem. It must also recognize when it has reached the limit of its ability and pass the customer to the person best equipped to help.One of the most interesting lessons concerns AI acceptance. According to Jacob, residents generally prioritize a fast, accurate resolution over whether the response comes from a person or an AI agent. EliseAI also found that introducing familiar regional voices to its voice AI increased conversations and conversions. This suggests acceptance can depend on familiarity, responsiveness, and outcomes rather than the technology label.We also consider how leaders can choose suitable workflows, why agents should be tested with difficult customer questions, and how automation could support heavily manual areas such as affordable housing administration.Is your business testing whether an AI agent can sound intelligent, or whether it can genuinely resolve the customer’s problem? Listen to the conversation and share your thoughts with me.

How do you know whether AI is making your company smarter rather than simply filling dashboards with impressive activity?In this episode of AI at Work, I speak with Michael Speranza, CEO of Kantata, about why familiar productivity metrics may be giving business leaders an incomplete picture of AI ROI. Companies can measure time saved, tasks completed, and documents generated, but those figures say little about whether AI is improving commercial decisions, creating revenue, or producing better client outcomes.Michael introduces the idea of the expertise compounding rate. This measures how effectively a company captures, synthesizes, shares, and builds upon the knowledge created through its projects and people. For professional services firms, that knowledge can include client conversations, previous deliverables, staffing decisions, financial performance, project outcomes, and relationships between colleagues.We discuss how AI can connect that information through a business specific knowledge graph. A team beginning a new project could identify similar work, locate colleagues with relevant experience, understand previous outcomes, and make better staffing or pricing decisions. Institutional knowledge that previously sat inside documents, meeting transcripts, or an employee’s memory can become available at the point of decision.Michael also shares an example of a services company using AI to change its project economics. By reducing delivery costs, the firm could offer projects at prices that created a viable business case for clients who previously would have postponed the work. That suggests AI ROI could be measured through sales conversion, opportunity close times, revenue growth, and the ability to expand without adding headcount at the same rate.Kantata frames the wider market around a revealing paradox. AI adoption across professional services reportedly increased by 40 percent last year, while executive confidence in real time visibility declined and revenue growth slowed to roughly half the industry’s historical benchmark. Greater adoption alone clearly does not guarantee stronger results.Michael argues that efficiency has become the price of admission. The commercial advantage comes from making each project more informed, predictable, and valuable than the one before it. We consider what leaders should measure, how human expertise and AI resources may influence future pricing models, and why clients care far more about outcomes than invisible automation behind the scenes.If every project created knowledge that improved the next one, how would that change the way your company measures AI ROI? Listen to the conversation and share your thoughts with me.

What should IT leaders do when employees adopt AI tools faster than their organization can evaluate or approve them?In this episode of AI at Work, I speak with Hemant Sahani, Vice President of Product Management for Workspace ONE at Omnissa, about the rapid growth of unsanctioned AI applications across the digital workplace.Omnissa’s State of Digital Workspace 2026 research found that workplace use of AI assistant applications grew by nearly 1000% during 2025. Hemant describes this period as AI’s iPhone moment, with employees choosing the tools that help them work faster instead of waiting for an official corporate rollout.We discuss why blocking every unapproved application can leave IT blind to what employees need. Hemant explains how observability can reveal where people are finding value, why approved tools may be falling short and which applications deserve a proper security, legal and procurement review.Our conversation also examines Omnissa’s vision for the autonomous workspace. Hemant imagines an environment that can configure, secure and repair itself while identifying digital experience problems before employees need to raise a support ticket.We also consider how AI is changing the responsibilities of enterprise IT. As device management, security and employee experience converge, IT teams increasingly need data skills, commercial awareness and closer relationships with HR, finance, security and legal teams.Could shadow AI become a valuable source of workforce intelligence, and how should organizations balance employee freedom with their responsibility to protect company and customer data? Please share your thoughts with me.

Is artificial intelligence really eliminating entry-level jobs, or is something much bigger happening beneath the surface? As businesses race to improve productivity and invest in AI, many graduates and early-career professionals are wondering whether the first rung of the career ladder is quietly disappearing.In this episode of AI at Work, I welcome Trent Cotton, Head of Talent Insights at iCIMS, for a data-driven conversation about how AI is changing hiring, workforce development, and the future of careers. Drawing on decades of HR experience and the latest workforce research, Trent separates headlines from reality and explains why the story is far more complex than many people assume.We begin by examining one of the biggest concerns surrounding AI. Is the technology actually replacing entry-level jobs? Trent argues that the evidence tells a more nuanced story. Rather than AI directly removing roles, many organizations are redirecting investment toward AI infrastructure while failing to rethink how entry-level positions create long-term value. The result is a hiring market where junior candidates increasingly feel employers expect mid-level experience before offering someone their first opportunity.Our conversation explores why that should concern every business leader. Entry-level employees don't simply fill today's vacancies. They become tomorrow's managers, specialists, and senior leaders. If organizations weaken that pipeline, they risk creating a leadership gap that may not become obvious for years.We also discuss how AI presents an opportunity rather than simply a challenge. Instead of replacing early-career employees, Trent believes organizations should use AI to reduce repetitive work, accelerate learning, and shorten the time it takes for new hires to become productive contributors. That requires rethinking learning and development, coaching, and career progression instead of simply automating existing processes.Another fascinating part of our discussion focuses on where technology talent is actually going. While many headlines concentrate on layoffs across large technology companies, Trent explains why skilled professionals are increasingly finding opportunities in healthcare, manufacturing, and other industries that are embracing AI to solve longstanding workforce shortages and operational challenges.We also examine the skills that are becoming increasingly valuable regardless of how AI develops. Critical thinking, communication, sound judgment, and the ability to orchestrate people, processes, and technology remain difficult to automate. These capabilities, combined with technical literacy and continuous learning, are becoming the qualities that employers value most.One of the biggest surprises from the conversation comes from changing attitudes among younger job seekers. Where previous generations often resisted assessments during the hiring process, many Gen Z candidates are now actively asking for opportunities to demonstrate their abilities through practical exercises rather than relying solely on a resume. As AI makes resumes easier to generate, proving genuine capability is becoming far more valuable than simply listing experience.We also discuss responsible AI in recruitment and why governance cannot become an afterthought. Trent explains why organizations need clear policies, transparency, and accountability before introducing AI into hiring decisions if they hope to maintain trust with candidates and employees alike.Is AI really closing the door on the next generation of workers, or is it giving businesses an opportunity to completely rethink how talent is developed? And as hiring continues to change, are we placing enough value on the human skills that technology still cannot replicate? I'd love to hear your thoughts after listening.

What if your business doesn't actually own its website, customer data, or digital marketing infrastructure? It's an uncomfortable question, but one that many founders never ask until they try to switch providers and discover just how difficult it is to leave.In this episode of AI at Work, I welcome David V. Kimball, Co-Founder and CEO of lilAgents, for a conversation that challenges many of the assumptions businesses have made over the last decade about websites, software subscriptions, AI, and digital ownership. David argues that convenience often comes at a hidden cost, with businesses gradually handing control of their most valuable digital assets to platforms that make it increasingly difficult to move elsewhere.We begin by exploring how so many organizations found themselves locked into ecosystems that seemed like the simplest option at the time. Website builders, ecommerce platforms, marketing suites, hosting providers, and CRM systems all promise convenience, yet many businesses only discover the downside when prices increase, features disappear, or they attempt to migrate to something better.The conversation then turns to artificial intelligence and where it is genuinely making a difference today. Rather than focusing on AI chatbots that have been added to almost every product, David explains why AI agents are becoming far more interesting. These systems can perform real work, connect different applications, automate repetitive processes, and solve practical business problems while people focus on higher-value work.One example that stood out involved a Shopify store with thousands of products that had accumulated years of inconsistent metadata. Using AI agents connected directly to Shopify's APIs, David was able to automate work that would have taken weeks by hand, helping improve search visibility and delivering measurable growth in organic revenue. It serves as a practical reminder that AI delivers the greatest value when solving real operational challenges rather than simply generating content.We also spend time discussing the hidden costs many businesses overlook. From paying for CRM contacts that no longer engage to running websites on platforms with far more functionality than they actually need, David explains why simplifying technology stacks can often reduce costs while improving flexibility at the same time. The objective isn't simply spending less. It's building systems that businesses genuinely own and can adapt as their needs change.Another theme running throughout our discussion is portability. Whether we're talking about websites, marketing platforms, AI models, or business data, David believes organizations should avoid becoming dependent on any single vendor. As AI continues to develop, he argues that businesses should think carefully about building modular systems that make it easy to change providers instead of finding themselves trapped by the next generation of platform lock-in.This episode offers a refreshing perspective on AI by moving beyond the hype and focusing on practical outcomes. It also raises an important question about the future of digital business. Are companies investing in technology they truly control, or are they simply renting increasingly expensive pieces of someone else's platform?How much of your digital business do you genuinely own today? And if one of your technology providers disappeared tomorrow, how easily could you move somewhere else? I'd love to hear your thoughts after listening.

What if the next competitive advantage in business isn't working faster with AI, but making better decisions because of it? As organizations rush to become AI-native, many conversations still focus on productivity, automation, and shipping work more quickly. But is speed really the outcome that matters most?In this episode of AI at Work, I welcome Jorge Valdivia, Chief Technology Officer at Fleetio, for a thoughtful discussion about what AI is actually changing inside modern organizations. Rather than adding another voice to the growing hype around artificial intelligence, Jorge offers a refreshingly practical perspective on why the future belongs to businesses that combine trusted expertise with intelligent technology.We begin by exploring how enterprise software has evolved over the past decade. For years, success meant becoming the system of record, collecting information in one central place and serving as the trusted source of truth. Today, however, customers expect something more. They want software that helps them produce measurable business outcomes, save money, improve operations, and clearly demonstrate return on investment.That shift naturally leads us into one of the most interesting parts of our conversation. Jorge challenges the common belief that AI automatically turns average performers into exceptional ones. Instead, he argues that the people gaining the greatest advantage from AI were already deeply curious about their customers, understood their industry, and knew how to solve meaningful problems. AI doesn't replace those qualities. It amplifies them.Throughout our discussion we examine what separates productive work from valuable work. While AI can certainly automate repetitive tasks and reduce time spent on administration, Jorge believes its greatest contribution comes from helping teams make better decisions. By bringing together customer feedback, product information, engineering data, and business context, AI becomes another source of insight that helps organizations identify the right opportunities instead of simply executing more tasks.We also discuss what it really means to become an AI-native leader. Rather than chasing every new tool or trend, Jorge explains why successful leaders focus on understanding where AI genuinely creates value for customers. That often means balancing experimentation with discipline, embracing automation where it removes friction, while keeping people responsible for the strategic decisions that still depend on judgment, context, and experience.One example that stood out involved Fleetio's own product development process. Faced with defining its long-term AI vision, the team used AI to synthesize customer conversations, product feedback, engineering insights, and design concepts into a shared understanding that had previously taken months of discussion without resolution. The technology didn't replace human thinking. It accelerated collective understanding so better decisions could be made.As our conversation draws to a close, Jorge shares advice for anyone building products or developing their career in an AI-powered workplace. Learning to use AI tools is rapidly becoming an expected part of the job, but lasting success still depends on becoming a trusted expert who understands customers, business problems, and the context behind every decision.Is the biggest opportunity with AI really about doing more work? Or is it about making smarter decisions that create better outcomes for customers, employees, and the business itself? I'd love to hear where you stand after listening.

What if the biggest obstacle to AI success isn't the technology at all, but the way your business actually works?In this episode of AI at Work, I sit down with Justin Watt, CEO and Co-founder of Switchboard, to discuss why so many AI initiatives disappoint and what organizations should focus on before adding another AI tool to the mix. Justin has spent his career helping growing businesses replace disconnected spreadsheets, manual handoffs, and fragmented workflows with systems that are designed to support the way people really work.During our conversation, Justin explains why many organizations are trying to build an AI-first business on top of processes that were never designed for automation. Rather than chasing the latest technology, he argues that leaders should first understand how work actually moves across their organization, identify unnecessary complexity, and remove friction before introducing AI.One of my favourite moments in our discussion is Justin's comparison between "single player AI" and "multiplayer AI." While many employees are already seeing personal productivity gains from tools such as ChatGPT and Copilot, the real opportunity comes when AI works across departments, connecting sales, operations, finance, legal, and customer teams instead of remaining isolated in individual chat windows.We also discuss why spreadsheets continue to dominate business operations decades after their introduction, how companies can move beyond them without disrupting the business, and why operational workflows should be treated like products that are continuously improved rather than collections of disconnected fixes.Justin also shares practical lessons from working with organizations that believed they had an AI problem, only to discover the real issue was broken processes. From legal teams overwhelmed by poor sales handoffs to businesses relying on undocumented workflows held together by spreadsheets and institutional knowledge, he offers a grounded perspective on where AI genuinely creates value and where better operational design delivers faster results.If you're leading digital transformation, responsible for operations, or trying to move AI from experimentation into everyday business value, this conversation offers practical advice that can be applied immediately.How well does your organization really understand its own workflows before asking AI to improve them? I'd love to hear your thoughts after listening.

What does it take for AI agents to move beyond impressive demonstrations and become part of the working day?In this episode of AI at Work, I speak with Will Del Principe from Thoughtly about what happens when AI voice agents are deployed into live customer and revenue operations. While many organizations are still evaluating where AI fits, Thoughtly is already helping businesses automate conversations, qualify leads, and manage customer interactions at a scale that would have been impossible just a few years ago.Will explains why the first breakthrough for AI voice isn't replacing complex human conversations. Instead, it is handling high-intent follow-up, where customers are already expecting a call and want fast, accurate answers. We also discuss why being open about using AI often increases trust, how even a fraction of a second in response time can determine whether a conversation feels natural, and why building conversational AI is far more technically demanding than many people appreciate.The conversation also highlights customer success stories, including Nomad, where Thoughtly's AI agents quickly grew to managing 20,000 tenant calls each day and 13,000 outbound sales calls every month. Rather than replacing employees, the technology allowed existing sales teams to focus on closing deals while AI handled repetitive outreach, qualification, and scheduling.We also discuss why businesses should experiment with AI before competitors gain an advantage, how AI agents are developing long-term memory across multiple communication channels, and why learning to work alongside AI is becoming an important skill for professionals at every stage of their careers.If AI can remove repetitive work while helping people spend more time on the tasks that matter most, where could it make the biggest difference in your organization? After listening, I'd love to hear your thoughts. How do you see AI changing the way you work over the next few years?

What if the biggest AI challenge facing organizations has nothing to do with technology at all? In this episode of AI at Work, I sit down with Lee Senderov, Chief Transformation Officer at Travelport, to discuss why AI should be viewed as a workforce transformation rather than a technology project, and why many organizations are still framing the opportunity in entirely the wrong way.While many businesses continue to focus on AI pilots, innovation labs, and isolated technical use cases, Lee argues that the real opportunity lies in empowering every employee. Drawing on Travelport's own AI journey, she shares how teams across the organization are using AI to eliminate repetitive work, create time for higher-value thinking, and solve problems that would never make it onto a traditional technology roadmap.We explore the practical framework Travelport has developed to drive adoption, covering capability building, creating the right operating environment, and fostering a culture that encourages employees to openly share ideas and AI-powered innovations. Lee explains why successful AI adoption requires far more than deploying tools, and how organizations can create an environment where experimentation becomes part of everyday work.The conversation also looks at the future of hiring, talent, and workplace culture. Lee predicts that AI proficiency will soon become as commonplace as email skills, shifting hiring conversations away from whether someone uses AI and toward how they use it to improve outcomes. At the same time, she warns against both ignoring AI and becoming overly dependent on it, arguing that the most successful employees will combine AI capabilities with human judgment, creativity, and critical thinking.We also discuss how AI is transforming the travel industry itself. From changing the way travelers search and book trips to supporting travel professionals during disruptions and complex itineraries, Lee explains how AI and human expertise are increasingly working together to create better customer experiences.Looking ahead, Lee believes the organizations that thrive will be those that build cultures capable of adapting quickly to whatever comes next. AI may be today's disruption, but the larger challenge is creating a workforce ready to embrace continuous change. Is your organization treating AI as another software tool, or is it rethinking how work itself gets done? Share your thoughts with me.

What happens when AI-generated code ships faster than humans can properly review it, and who takes the blame when something breaks?In this episode of AI at Work, I sit down with Cameron Etezadi, Chief Technology Officer at LaunchDarkly, to tackle one of the most uncomfortable questions facing modern software teams. As developers increasingly rely on AI coding assistants, copilots, and public LLMs to accelerate delivery, organizations are finding themselves caught between productivity gains and growing governance risks.Cameron explains why “Shadow AI” has become the modern evolution of Shadow IT, and why the stakes are far higher when AI-generated code is moving directly into production systems. We explore how engineering teams are balancing innovation with accountability, why runtime controls and kill switches are becoming essential in AI-native software development, and how organizations are struggling to maintain visibility into code generated by autonomous systems. Cameron also explains why he believes many companies are unknowingly exposing intellectual property, customer trust, and compliance obligations through careless AI use.The conversation also examines how the EU AI Act and Product Liability Directive could reshape software development globally. Cameron argues that organizations deploying AI-generated code are now effectively treated as manufacturers under emerging regulations, with accountability resting firmly on businesses shipping software, not the AI vendors creating the tools. From governance gaps and auditability concerns to token economics and developer productivity metrics, this discussion explores the operational realities behind the AI hype cycle.We also discuss why faster code does not automatically mean safer software, the hidden costs of AI-generated rework, and how some organizations are already spending more time fixing AI-assisted production issues than they expected. Cameron shares practical advice for boards, CISOs, and DevOps leaders on what questions they should be asking today before AI governance problems become tomorrow’s security incidents.If your organization is experimenting with AI-assisted development, this conversation offers a valuable reality check on where the risks are emerging, how the rules are changing, and why accountability still matters in an increasingly automated world.

Why are so many organizations investing millions into AI while still struggling to prove meaningful productivity gains?In this episode of AI at Work, I spoke with Rahsaan Shears, Principal and AIQ Program Lead at KPMG, about a major new study conducted alongside the McCombs School of Business at The University of Texas at Austin that analyzed 1.4 million real workplace AI interactions. What emerged from that research challenges many assumptions business leaders currently hold about AI adoption, productivity, and the future of work.One of the most surprising findings was that the most effective AI users were not necessarily the most technical employees, nor even the people using AI tools most frequently. Instead, the highest performers were what KPMG calls “sophisticated users,” employees who learned how to think with AI, challenge it, iterate with it, and use it as a reasoning partner rather than simply a faster search engine.Rahsaan explained how this distinction is forcing organizations to rethink how they measure AI success. Many businesses remain focused on surface-level adoption metrics like license counts, prompt volume, or chatbot usage. But those measurements often fail to capture whether AI is genuinely improving decision-making, productivity, creativity, or operational performance. The real challenge, according to Rahsaan, is that most organizations still lack a framework for understanding what meaningful AI-enabled work actually looks like.We also explored the growing behavioral capability gap emerging inside organizations. While some employees are rapidly learning how to integrate AI into their workflows in sophisticated ways, others remain stuck using these tools for basic task acceleration. Rahsaan shared why this gap has less to do with age or technical skill and far more to do with curiosity, ambition, critical thinking, and an employee’s willingness to rethink how work itself gets done.One of the strongest themes throughout our conversation was the idea that AI should not be treated as a technology rollout alone. Rahsaan argued that organizations succeeding with AI are redesigning culture, workflows, decision-making structures, and team dynamics at the same time they deploy new tools. He compared today’s AI systems to toddlers: incredibly capable compared to where they started, but still requiring guardrails, coaching, supervision, and careful integration into everyday work.For listeners interested in organizational transformation, this episode offers practical insight into how KPMG is building AI-first behaviors through peer-led champion networks, embedded learning models, AI coaching inside the flow of work, and safe environments where employees can experiment without fear of failure. Rahsaan shared why psychological safety, curiosity, and continuous learning are rapidly becoming core business skills in the AI economy.We also discussed why organizations that fail to create agency for employees may struggle to scale AI beyond pilot programs. According to Rahsaan, many existing business processes were designed around the limitations of human workers, limitations that no longer fully apply once digital teammates and agentic workflows enter the picture. Companies willing to question long-standing assumptions about work itself are beginning to separate themselves from the rest of the market.This conversation moves beyond AI hype and focuses on the human behaviors, organizational structures, and operational changes that will ultimately determine who wins and loses in the AI economy.

What happens when AI agent creation stops being the job of engineers and starts landing in the hands of the people who actually understand the business problem?In this episode of AI At Work, I sat down with Cien Solon, CEO and Founder of LaunchLemonade, to talk about why the next chapter of AI may have less to do with hype and more to do with practical problem-solving. Cien describes LaunchLemonade as the Canva for AI agents, and that immediately caught my attention because it gets to the heart of what so many businesses are looking for right now. They do not want more jargon. They want a way to build something useful, quickly, securely, and without needing a room full of developers to make it happen.What I found especially interesting in our conversation was Cien’s argument that the real barrier to AI is no longer cost or technical complexity. In her view, those obstacles have already fallen away. The bigger issue now is mindset. Too many organizations are still stuck in observation mode, watching from the sidelines, waiting for perfect tools and perfect certainty. Meanwhile, others are already building, testing, learning, and finding ways to turn AI agents into something that supports growth, fills skills gaps, and creates new revenue opportunities.We also talked about what return on investment actually looks like in the real world. That part matters because so many AI conversations still float around in theory. Cien makes the case that the people best placed to solve business problems are the ones living with them every day, not the engineers guessing from a distance. That is a powerful shift in thinking. Instead of waiting until there is budget to hire another person, businesses can now identify a gap, map out the workflow, and create an AI agent to help close it.There is also a bigger human story running through this episode. Cien shared examples of people who started out experimenting with prompts and basic no-code tools, then went on to build consulting businesses, launch products, sell courses, and reposition themselves in the market. One story that stood out was a university professor who used LaunchLemonade to learn, experiment, and eventually step into entrepreneurship full time. It is the kind of example that reminds us this technology is not only changing workflows, it is also changing careers and confidence.We also discuss the future of the no-code agent economy and where businesses need to focus next. Cien breaks people into a few camps, the observers, the operators, and the builders, and it makes for a memorable way of thinking about where each of us stands right now. Her message is clear. If you are still only watching, you risk falling behind. If you are building, the next challenge is no longer whether you can create something, but whether you can market it, sell it, and make it meaningful.By the end of this conversation, what stayed with me most was how accessible this all feels when someone explains it in plain English. This is not a conversation about futuristic abstractions. It is about people using AI to solve real business problems today, in ways that feel achievable rather than intimidating. So after listening, where do you see yourself in this new AI economy, observing, operating, or building, and what are you creating next?

What happens when software stops being something we use and starts becoming something that works alongside us?In this episode of AI at Work, I sat down with Mark Skelton, CTO at Node4, to explore how AI is moving beyond content generation and into something far more transformative. Mark has spent more than a decade operating at CTO level, helping organizations navigate shifts from traditional infrastructure to cloud, and now into a world shaped by AI agents, automation, and entirely new ways of delivering technology.We begin by unpacking the evolution from generative AI to agentic AI. While most businesses are now familiar with tools that create content, Mark explains that the real shift is happening as AI begins to take action. These agents can interact with systems, execute workflows, and handle tasks that previously required human input. It is a shift that brings both excitement and uncertainty, especially as conversations around AI co-workers become more common in boardrooms and across teams.A big part of our conversation focuses on what this actually looks like in practice. Rather than replacing people, Mark shares how AI is currently augmenting teams, supporting developers, automating repetitive work, and helping organizations move faster while still keeping humans firmly in the loop. There are still limitations, from hallucinations to data quality issues, which means oversight, validation, and strong governance remain essential.We also explore one of Mark’s boldest predictions, that the rise of agentic AI could fundamentally change how we think about software itself. Instead of logging into multiple SaaS platforms, future workflows may be driven through conversations with AI agents that access systems, retrieve data, and execute tasks on our behalf. That shift opens the door to new opportunities in orchestration, integration, and data strategy, while also raising important questions about how businesses prepare for what comes next.From the role of model context protocol servers as the connective layer behind AI agents, to the importance of guardrails across technical, operational, and cultural levels, this episode offers a clear and practical look at how organizations can start making sense of a fast-moving space. Mark also shares why data readiness, cloud adoption, and AI literacy are becoming the foundations that will separate those who adapt from those who struggle to keep up.So as AI agents begin to reshape how work gets done, where should businesses focus their energy today, and what does it really take to stay relevant in a world where software may no longer look the way it does now?

How do you keep product teams aligned when AI is speeding everything up, but people, priorities, and expectations are still pulling in different directions?In this episode of AI At Work, I sat down with Dave West, CEO of Scrum.org, to talk about one of the most overlooked challenges in modern product development: stakeholder collaboration. While so much of the conversation around AI focuses on faster delivery, automation, and productivity, Dave makes the case that the real pressure point is still human. As teams ship more, communicate faster, and rely on AI to remove friction, weak stakeholder relationships become even harder to ignore.We unpack why Scrum.org has launched its new self-paced course, Effective Stakeholder Collaboration for Scrum Teams, and why Dave believes this topic deserves far more attention than it usually gets. He explains how AI is exposing old cracks inside organizations, from fuzzy expectations and unspoken assumptions to inconsistent communication and poor decision-making. We also talk about why product teams need a more disciplined approach to stakeholder engagement, one that is clear, intentional, and built around trust rather than vague alignment.What I found especially interesting in this conversation was Dave’s view that this is less about job titles and more about how real people work together. We discussed how product owners, Scrum Masters, and developers can build stronger relationships without creating confusion, why empathy and better listening can change the direction of a product, and how segmenting stakeholders by needs, motivations, and context can reduce what Dave describes as stakeholder drag. It is a practical conversation for anyone working in product, Agile, Scrum, or AI-driven delivery.We also went beyond the course itself and into the wider debate about whether Agile and Scrum still matter in the age of AI. Dave had a lot to say on that, and he did not hold back. His argument is simple: AI may help teams build faster, but it also makes it painfully obvious when they are building the wrong thing. If you care about AI at work, Scrum, product management, stakeholder engagement, or the future of Agile, this episode has plenty to think about. Do you believe AI will strengthen stakeholder collaboration or expose just how broken it already is, and what side of that debate are you on? Share your thoughts.

What does AI actually change once you move beyond the pilot phase and into the messy reality of live deployment? In this episode of AI at Work, I sit down with Jack Siney, CRO and co-founder of FrontRace, to separate operational truth from industry hype and explore what he calls the “Great Upheaval” already reshaping how organizations generate revenue, measure performance, and define success.Drawing on experience from the U.S. Navy’s Blue Angels program, PwC, multiple startup exits, and now hands-on AI implementation across hundreds of companies, Jack offers a practitioner’s perspective on where AI is delivering immediate value and where it is still falling short. We talk about why so many expensive initiatives fail to move the needle, how legacy KPIs are pushing teams toward the wrong outcomes, and why most automation breaks because organizations never fully documented the human steps they were trying to replicate.A big part of our conversation focuses on sales leadership and the frontline reality. Jack explains how AI can finally decode the long-standing mystery of why two reps with identical activity metrics produce wildly different results, how decision engines built on a company’s own historical data can guide next best actions in real time, and why better data hygiene and process clarity matter more than buying another tool. At the same time, he is clear that today’s AI is an 80 percent solution that still demands human oversight, critical thinking, and constant tuning.We also step back to look at the economic and cultural shift ahead. If productivity is no longer tied to headcount growth, what happens to the traditional link between company performance, employment, and spending power? And what mindset shifts do chief revenue officers and business leaders need to make right now to avoid incremental thinking and instead redesign how work gets done?This is a grounded, candid conversation about readiness, responsibility, and real outcomes, recorded for leaders who want practical direction rather than another theory about the future of work. After listening, where do you see AI genuinely improving performance in your organization today, and where is it still a promise waiting to be fulfilled?

What if the best way to improve your negotiation skills was to rehearse the conversation before it ever happened?In this episode of AI at Work, I sit down with Professor Alexandra Mislin from American University’s Kogod School of Business to explore how AI is quietly reshaping the way professionals prepare for high-stakes conversations. Recently featured in Fortune, Professor Mislin has been teaching her students to use AI as a negotiation practice partner, helping them clarify priorities, test assumptions, and even role-play difficult scenarios before walking into the room. Negotiation is one of those skills we use every day, whether we label it that way or not. It shows up in salary discussions, scope changes, vendor renewals, internal disagreements, and those tense moments where trust feels fragile. The problem is that most people learn under pressure, with real consequences and little room to experiment. Professor Mislin’s approach offers something different. She teaches core negotiation skills first, then introduces AI as a thinking partner rather than a decision maker. The goal is not to outsource judgment, but to sharpen it.We talk about how AI can help professionals clarify what they truly want before a conversation begins. We explore how tools can surface blind spots, generate counterarguments, and simulate different negotiation styles. Professor Mislin also shares why she is less worried about AI creating formulaic responses or overconfidence than many critics assume. In her view, reducing ambiguity can actually empower more people to advocate for themselves and engage in everyday negotiations they might otherwise avoid.Trust, emotion, and identity remain at the heart of every negotiation. That human element does not disappear. Professor Mislin explains how AI can help diagnose a breakdown in trust or draft the structure of an apology, but sincerity still requires real human presence. As AI automates more routine exchanges, the competitive advantage will belong to those who know how to combine analytical tools with interpersonal intelligence.We also look ahead to what negotiation education may become in an AI-rich workplace. Instead of occasional training sessions, professionals could have continuous, on-demand coaching. Yet the skills that remain uniquely human, listening deeply, regulating emotions, and making difficult calls under uncertainty, may become even more valuable.If you have ever walked away from a difficult conversation thinking of everything you wish you had said, this episode offers a practical way to prepare differently. How are you using AI to think before you ask, and what changed when you did?

How do you move AI from a flashy demo on a conference stage to something that can handle real customer pressure on a Monday morning when the tickets are piling up?In this episode of AI At Work, I sit down with Niraj Ranjan Rout, Founder and CEO of Hiver, to unpack what it really takes to build AI that works inside high-volume support environments. With more than 10,000 teams using Hiver, including brands like Flexport, Capital One, and Epic Games, Niraj has had a front-row seat to both the promise and the pitfalls of AI in customer service.We talk about the difference between “slapping a chatbot” onto an existing problem and rethinking the entire support workflow. Niraj makes a compelling case that AI should function as infrastructure, embedded across triage, routing, drafting, summarization, quality assurance, and insights. Rather than replacing agents, the goal is to remove the repetitive, manual work that drains time and energy, so humans can focus on solving real problems and understanding how customers actually feel.Our conversation also gets into the uncomfortable but necessary topics many leaders underestimate. Data hygiene. Governance. The reality that 98 percent accuracy is sometimes still not good enough. Niraj shares why clear handoff protocols between humans and AI are essential, and how organizations can avoid measuring ROI through surface metrics like deflection rates alone. Instead, we explore more nuanced signals, from sentiment shifts to long-term customer outcomes and team productivity.We also discuss Hiver’s own journey from an email collaboration tool to an AI-native customer service platform. Niraj is candid about the noise in the market, from overblown promises to doomsday narratives, and how founders must stay close to customers while remaining hands-on with emerging models and agentic capabilities. Culture, he argues, is as important as code. Customer stories need to flow directly into product and engineering teams if AI investments are going to remain grounded in reality.And yes, we even end on a musical note, with a nod to Jimi Hendrix and a reminder that creativity, whether in music or software, still comes down to craft and feel.So here’s the question I’ll leave you with. As AI becomes embedded into every workflow, are you treating it as a shiny add-on, or are you redesigning your foundations so it can truly perform under pressure?

What does AI at work really look like once the hype fades and the day-to-day reality sets in?In this episode of AI at Work, I’m joined by Nicole Leib, Regional Vice President of People for the Americas and Global Head of Inclusion at monday.com, for a grounded, refreshingly honest conversation about how AI is actually being used in modern organizations. We recorded this during CES week, when every headline seemed to promise disruption, reinvention, and job loss. Yet the data Nicole brings to the table tells a very different story.Drawing on Monday.com’s World of Work: AI Edition report, produced in partnership with Nielsen and informed by millions of real workflows, Nicole explains why labor reduction is not the primary driver behind AI adoption. Instead, organizations are using AI to move faster, improve accuracy, and reduce the cognitive load placed on teams. This marks a clear shift into what she calls the operational era of AI, where success is measured by practical outcomes rather than futuristic promises. We unpack why the tools gaining traction are not the flashiest, but the ones that fit naturally into existing workflows and simply help people get through their day.We also explore the human side of AI adoption. Nicole shares insights into why innovation is barely a motivator right now, what tool overload looks like in practice, and why simplification is becoming a real competitive advantage. Our conversation touches on trust, security, and governance, especially for larger enterprises, and why embedding AI into systems people already rely on matters more than adding yet another standalone tool. We also address the confidence gap around AI, including the striking gender divide where women are often using AI more while undervaluing their own expertise, and what that means for career progression.By the end of the discussion, one idea stands out above all others. AI is not pushing people out of work. It is helping them step up, take on more strategic responsibilities, and rethink what valuable work looks like in a world of constant change. As we look ahead to what the next phase of AI at work might bring, are our leaders ready to stop waiting for a perfect future moment and start treating AI as a core operating capability today, and how are you seeing that shift play out inside your own organization?Useful InksConnect With Nicole Leib,Introducing AI at work: From vision to value, Monday Research’s latest reportFollow Monday on LinkedInThanks to our sponsors, Alcor, for supporting the show.

What if the most important jobs of the next decade already exist, but we just have not named them yet?In this episode of AI at Work, I sit down with Marinela Profi from SAS to unpack how artificial intelligence is reshaping work at a deeper level than most headlines suggest. We are not just talking about tools, automation, or faster workflows. We are talking about new roles, new decision structures, and a fundamental shift in how humans and machines collaborate inside modern organizations.Marinela brings a grounded, enterprise-tested perspective to agentic AI, cutting through the confusion that still surrounds the term. She explains why large language models are not agents, why autonomy is often misunderstood, and why most successful AI systems will always keep humans in the loop. We explore how agentic systems differ from traditional AI, how deterministic guardrails and probabilistic models must work together, and why governance needs to be designed into systems from day one rather than bolted on later.One of the most compelling parts of this conversation is our discussion on future roles. A few years ago, no one imagined titles like cloud governance architect. Marinela explains why roles such as AI decision designers and AI experience designers are likely to follow a similar path. These are not abstract ideas. They are practical responses to real challenges organizations face as AI systems begin to act, decide, and operate at scale.We also dig into where teams tend to go wrong. Too many organizations rush from pilots to hype without addressing data readiness, orchestration, or accountability. Marinela shares real examples from regulated industries, including banking, insurance, telecoms, and manufacturing, where agentic AI has moved from experimentation into production by focusing on decision workflows rather than flashy prototypes.This is a conversation for CIOs, CDOs, business leaders, and professionals who want to understand what AI means for work beyond surface-level narratives. It is also for students and early-career listeners who want to prepare for roles that are still taking shape, but will soon be unavoidable.If AI is becoming an expected skill rather than a specialist one, how do you prepare yourself and your organization for work that is already changing in front of us?I would love to hear your thoughts after listening. Where do you see human judgment becoming more important as AI systems grow more capable, and which future roles do you think we will be talking about next year?Useful LinksConnect with Marinela ProfiSAS WebsiteFollow SAS on LinkedInThanks to our sponsors, Alcor, for supporting the show.

What happens when eighty percent of the global workforce receives less than one percent of technology investment, and why has this imbalance gone largely unchallenged for so long?In this episode, I sat down with Emma Seymour, Chief Financial Officer of Deputy, to unpack the realities facing the world’s so-called invisible workforce. Deskless workers power healthcare, retail, hospitality, and frontline services, yet the tools built to support them have historically lagged far behind those designed for office-based teams. Emma brings a grounded, finance-led perspective on why this gap exists and why it is finally starting to close.We explored how AI-driven workforce management is moving beyond hype and into practical, measurable outcomes. From optimizing staffing levels to reduce overstaffing and burnout, to giving workers more control over their schedules through self-service tools, Emma shared how Deputy is translating technology investment into real operational and human impact. We also discussed how AI is reshaping the finance function itself, automating admin-heavy tasks and freeing up teams' time to focus on higher-value work.What also stood out in this conversation was leadership. Deputy’s predominantly female executive team offers a rare example of scaling a billion-dollar technology company while balancing high performance with high care. Emma shared how trust, accountability, and empathy shape decision-making inside the business, and why that culture matters just as much as product innovation when serving a workforce that has been overlooked for decades.As AI continues to accelerate and workforce pressures intensify, what would it look like if more technology companies truly built for the people who keep the global economy running, and how differently might work feel if the invisible workforce finally became visible?Useful LinksConnect with Emma SeymourLearn more about Deputy,Follow on LinkedInThanks to our sponsors, Alcor, for supporting the show.

Are today’s AI tools actually doing the work, or are they still sitting on the sidelines offering advice that humans have to act on?In this episode of the AI at Work podcast, I sat down with Oren Michels, Founder and CEO of Barndoor AI, to explore why so much enterprise AI still feels stuck in what he calls “advisor mode.” We talked about the gap between AI that summarizes and AI that acts, and why that distinction matters far more to knowledge workers than most leaders realize. Oren drew on his experience building Mashery during the early days of APIs, drawing a clear parallel between then and now, when powerful technology exists but remains inaccessible to the people who actually need to use it.We spent a lot of time unpacking what true agentic AI really means inside the enterprise. For Oren, it is not about smarter chatbots or recycled RPA workflows, but about agents that can safely take action inside systems like Salesforce, CRMs, and other tools of record. We discussed why so many AI initiatives fail to deliver ROI, and why the missing skill is often not prompt engineering, but the ability to break real business problems into clear, executable instructions that an AI agent can actually follow.Governance became a central theme in our conversation, especially as we dug into the Model Context Protocol, or MCP. While MCP is emerging as a powerful standard for connecting AI to enterprise tools, Oren explained why it also introduces new security, cost, and control challenges if left unchecked. We explored why governance should act as a launchpad rather than a brake, how least-privilege access changes the conversation, and why the most important question is not how a model was trained, but what it can do with access right now.If you are thinking seriously about agentic AI, enterprise adoption, or how to prevent “bring your own AI” from becoming the next wave of shadow IT, this episode will give you a grounded, experience-led perspective on what actually needs to change inside organizations. As AI agents begin to operate at speed and scale across core systems, are your guardrails designed to stop progress, or to make it possible to move forward with confidence?I would love to hear your thoughts after listening. How close do you think we really are to AI that acts, not just advises?Useful LinksConnect with Oren MichelsLearn more about Barndoor AIThanks to our sponsors, Alcor, for supporting the show.

What does AI at work really look like once the conference buzz fades and teams have to turn ambition into execution?In this episode of the AI at Work Podcast, I sit down with Diego Lomanto, Chief Marketing Officer at Writer, to unpack how marketing teams are actually using AI and agents inside real enterprise workflows. Diego brings a grounded perspective shaped by more than two decades in enterprise software, spanning analytics, automation, and now AI, including his time leading product marketing at UiPath during its rapid growth years.We talk candidly about why AI adoption often stalls inside organizations, not because of the technology, but because leadership behavior, operating models, and incentives fail to evolve. Diego explains why C-level executives need to get hands-on first, why AI should be treated as a transformation of how work gets done rather than another IT rollout, and how marketing leaders need to rethink team structure, workflows, and success metrics in an agent-driven world.The conversation digs into what Diego calls an agentic marketing playbook, where AI handles speed and scale while humans remain firmly in charge of narrative, judgment, and creative direction. From automating repetitive content workflows to freeing up time for deeper customer relationships and high-touch engagement, Diego shares how Writer and its customers, including large consumer brands and regulated enterprises, are using agents to support people rather than sideline them.We also explore how Writer uses its own technology internally, what surprised Diego once AI agents were fully embedded into day-to-day marketing operations, and why change management and AI literacy matter just as much as model quality. As organizations look ahead to 2026, this episode offers a clear-eyed view of where AI-driven work is heading next, from departmental orchestration to deeper collaboration across marketing, sales, and product teams.If AI is quickly becoming table stakes, how will your organization use it to automate the repeatable while keeping humans as the real source of differentiation?Useful LinksConnect with Diego LomantoLearn More About WriterDenodo sponsors Tech Talks Network

As AI moves beyond hype and into everyday operations, many organizations are asking harder questions about impact, trust, and return on investment. Three years on from ChatGPT’s breakout moment, leaders are no longer experimenting for novelty’s sake. They want to know where AI genuinely improves outcomes for employees and customers, and where it risks getting in the way.In this episode of the AI at Work Podcast, I sit down with John Finch, Head of Product Marketing at RingCentral, to unpack how AI is changing customer interactions before, during, and after the call. We explore how tools like AI receptionists and real time agent assistance are helping businesses avoid missed calls, reduce friction, and support frontline teams without turning conversations into scripted or robotic exchanges.John shares RingCentral’s perspective on why voice remains one of the richest and most strategic data sources inside modern organizations. We discuss how insights drawn from real conversations are shaping smarter routing, coaching, and workforce planning, and why sectors like healthcare and financial services are leaning into AI faster than others. At the same time, we address the common mistakes companies make when they bolt AI onto fragmented systems rather than embedding it into a unified platform.Looking ahead to 2026, this conversation also reflects on what AI done well really looks like in the workplace. Not as a replacement for people, but as a way to remove pressure, improve performance, and create better experiences for everyone involved. As AI becomes more natural, conversational, and embedded into daily workflows, the line between digital and human support continues to blur.So as AI becomes part of the fabric of customer operations, how are you balancing automation with empathy, and what lessons from your own organization would you share with others navigating this shift?

What happens when holiday shopping habits shift faster than most small businesses can keep up, and AI becomes the first stop for gift ideas, local searches, and product discovery? In my conversation with Alicia Pringle, Senior Director of Online Marketing at Network Solutions, we look at how the rise of AI-assisted search is changing the game for small business visibility during the busiest season of the year. Alicia brings two decades of marketing experience and a front row seat to the rapid evolution of search, and she breaks down what is really happening behind the scenes as shoppers move from typing into Google to asking Gemini, ChatGPT, and other assistants for personal recommendations.Alicia explains how early holiday behaviour has become and why the traditional mid-December surge is now simply a final sweep rather than the main event. She talks through the surge in AI driven discovery and how more than a third of shoppers now ask AI for curated suggestions with specific personal details baked in. This has created a rare moment where small businesses can compete with large retailers again because AI search rewards clarity, genuine content, and trustworthy online signals rather than the size of a marketing budget. Her examples make it clear that websites, local listings, and social channels now act as one connected reputation system, and AI will only surface businesses that look consistent, human, and helpful across all of them.Throughout our conversation, Alicia brings the ideas to life with practical stories. She shares how a retreat centre in Arizona used smarter positioning, thoughtful content, and simple updates to pull in hundreds of organic clicks right as shoppers were searching for meaningful holiday gifts. She explains how small changes to website speed, photos, clarity, and mobile performance can lift a business in both traditional search and AI powered assistants, often in a matter of hours rather than months. And she makes a strong case for curiosity as the new essential skill, because leaders do not need to understand the mechanics of AI to benefit from it, they simply need to be willing to experiment.As AI search becomes part of everyday life, Alicia’s message is grounding. Visibility can be earned again. Small businesses can adapt. Modern tools can remove a lot of the technical pain. And with a few thoughtful changes, brands can still show up in those key digital moments when customers are ready to buy. So how should small businesses use this moment to build trust, stay discoverable, and meet shoppers where they already are? I would love to hear your thoughts.

What happens when a field races forward faster than society can understand it, let alone shape it? And how do we balance the promise of superintelligence with the responsibility to ensure it reflects the values of the people it will eventually serve? In this episode of AI at Work, I sit down with Dr Craig Kaplan, founder and CEO of iQ Company and SuperIntelligence. He's also a pioneer who has been building intelligent systems since the 1980s, and one of the few voices urging a deliberate, safer path toward AGI. Craig brings decades of perspective to a debate often dominated by short-term thinking, sharing why speed without design can become a trap and why the next breakthroughs must be grounded in intention rather than chance.Throughout our conversation, Craig explains why current alignment methods often rely on narrow viewpoints, which creates both ethical and technical blind spots. He shares his belief that the values guiding future intelligence should come from millions of people across cultures rather than a handful of researchers writing a constitution behind closed doors. Drawing on his work at Predict Wall Street, he illustrates how collective intelligence can outperform experts, why diverse viewpoints matter, and how these lessons shape the architecture he believes is needed for safe AGI and the superintelligent systems that follow. His clarity on the difference between tools and entities, and how quickly AI is shifting into the latter category, offers a grounding moment for anyone trying to navigate what comes next.This episode moves beyond fear and hype. Craig talks openly about risk, but he also brings optimism about the potential for systems that are safer, faster to build, less costly, and more reflective of humanity. For leaders wondering how to prepare their organisations, he shares what signals to watch, why transparency and design matter, and how a more democratic approach to intelligence could shift the odds of a better outcome. If you want a clear, thoughtful look at the road ahead for AGI, superintelligence, and the role humans still play in shaping both, you will find a lot to chew on here.Listeners wanting to learn more can explore superintelligence.com, where Craig and the iQ Company team share research, videos, papers, and ways to get involved. What part of this conversation sparks your own questions about the future we are building together?Sponsored by NordLayer:Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

I sit down with Toby Hough, Vice President of People and Culture at HiBob, for a grounded and human conversation about how AI is reshaping the world of work, not by replacing people but by amplifying them. As an HR leader inside a company that builds HR technology, Toby brings a rare perspective on what it really means to balance efficiency with empathy in an AI-driven workplace.We talk about the fear that still surrounds AI in many organisations and how leaders can help shift that mindset from anxiety to opportunity. Toby explains why HiBob is taking a “more with more” approach, using AI tools to empower employees rather than reduce headcount. From custom-built AI coaches that guide managers through feedback conversations to an internal platform with dozens of homegrown AI tools, he shares how democratising AI access can transform both productivity and trust.Toby also explores how leaders can measure success in this new era, moving beyond cost savings to focus on adoption, engagement, and well-being. He highlights the delicate balance between automation and human connection, showing how HiBob invests equally in AI enablement and in-person leadership development. As we look ahead, Toby reflects on the evolving skills required to lead both humans and AI agents, and how the next generation of leaders will need to master curiosity, adaptability, and collaboration across both worlds.Listen in for an honest discussion about the cultural, emotional, and practical realities of integrating AI at work, and why, Toby’s LinkedIn HiBob websiteIn Good Company websiteSponsored by NordLayer:Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

The arrival of generative AI has sparked an uncomfortable question for many young professionals: What happens to entry-level jobs when machines can now write, analyze, and even converse as well as humans? In this episode of the AI at Work Podcast, I reconnect with Anshuman Singh, CEO of HGS UK, to discuss how automation and artificial intelligence are reshaping the early stages of a career, and what skills will define employability in the years ahead.Anshuman brings a rare blend of optimism and realism to the debate. He traces how AI’s evolution from statistical tools to generative systems has amplified both opportunities and anxieties, particularly among graduates seeking their first big break. Drawing on research from MIT, ADP, and the World Economic Forum, he explains how AI is accelerating job displacement in certain functions, such as data entry and basic customer service, even as it creates entirely new roles in areas like AI training, ethics, and human-in-the-loop supervision.We explore why adaptability, not fear, is the true competitive advantage in this era of rapid change. Anshuman breaks down three categories of emerging roles: AI specialist positions such as prompt engineers, collaborative roles that blend human creativity with machine intelligence, and augmented roles where humans use AI to enhance judgment and performance. He also warns that if companies automate entry-level work too quickly, they risk losing the apprenticeships and on-the-job learning that build leadership pipelines.Our conversation turns to the human qualities that machines still cannot replicate, such as empathy, ethical reasoning, creative problem solving, and contextual understanding, and why these traits will define long-term success. Anshuman offers practical advice for workers and business leaders alike: redesign roles to keep humans in the loop, measure success by both human impact and automation, and invest relentlessly in learning cultures that help people evolve alongside technology.If you are worried about AI replacing your job, this episode reframes the story. It is not about competing with machines; it is about understanding what only humans can do and leveraging that as your edge.AI at Work is Sponsored by NordLayer:Get the exclusive Black Friday offer: 28% off NordLayer yearly plans with the coupon code: techdaily-28. Valid until December 10th, 2025. Try it risk-free with a 14-day money-back guarantee.

In this episode of AI at Work, I sit down with Tom Totenberg, Head of Release Automation and Observability at LaunchDarkly, to explore what happens when artificial intelligence starts writing and shipping our software faster than humans can think. Tom brings a rare blend of technical insight and grounded realism to one of the most important conversations in modern software development: how to balance speed, safety, and responsibility in an AI-driven world.We discuss the hidden risks of AI-fuelled shortcuts in software delivery and why over-reliance on AI-generated code can create dangerous blind spots. Tom explains how observability and real-time monitoring are becoming essential to maintaining trust and stability as teams adopt AI across the full development lifecycle. Drawing on LaunchDarkly’s recent investments into observability, he breaks down how guarded releases and real-time metrics are helping teams catch problems before users ever notice.From the dangers of “vibe coding” to the rise of agentic AI in software pipelines, Tom shares why AI should be seen as an amplifier rather than a magic fix. He also offers practical advice for leaders trying to balance innovation with caution, reminding us that the goal is to innovate with intention — to measure what matters and build resilience through feedback and transparency.Recorded during his time in New York, this episode captures both the human and technical sides of what it means to deliver software in an era where the line between automation and accountability is being redrawn.

I invited Kyle Hauptfleisch, Chief Growth Officer at Daemon, to strip the buzzwords out of AI and talk plainly about what moves the needle at work. The conversation began with an honest look at why so many pilots stall. It ended with a calm, workable path for leaders who want results they can measure rather than demos that gather dust. Along the way we compared two very different mindsets for adoption, AI added and AI first, and what that means for teams, accountability, and the way work actually gets done.Here’s the thing. Plenty of organisations raced into proofs of concept because a board memo said they had to. Kyle has seen that pattern play out for years, and he argues for a simpler starting point. You do not need an AI strategy in a vacuum. You need a business strategy that names real constraints and outcomes, then you pick the right kind of AI to serve that plan. AI Added vs AI FirstThis distinction matters. AI added means dropping tools into the current way of working. Think code generation that saves hours on day one, only to lose those hours later in testing, release, or approvals. The local gain never flows through to the customer.AI first asks a harder question. How do we change the workflow so those gains survive from whiteboard to production? That can mean new handoffs, fresh definitions of ownership, and different review gates. It is less about tools, more about the shape of the system they live in.Accountability sits at the center. Kyle raised a scenario where a lead might one day direct fifty software agents. The intent behind those agents remains human. So does the responsibility. Until structures reflect that, companies will cap the value they can safely realise.From Pilots to ProductionKyle offered a simple mental model that avoids endless experimentation. Picture a Venn diagram with three circles. First, a real constraint that people feel every week. Second, usefulness, meaning AI can change the outcome in a measurable way. Third, compartmentalisation, so the work sits far enough from core risk to move fast through governance. Where those circles overlap, you have a candidate to run live.He shared a small but telling example from Daemon. Engineers dislike writing case studies after long projects. The team now records a short conversation, transcribes it with Gemini inside a safe, private setup, and drafts the case study from that transcript. People still edit, but the heavy lift is gone. It saves time, produces more human stories, and proves a pattern the business can repeat.Leaders can start there. Pick a contained problem, run it in production, measure the outcome, and tell the truth about the bumps. That story buys trust for the next step, which is how you scale without inflating the promise.Humans, Accountability, and CultureWe talked about the fear that AI erases the human role. Kyle’s view is steady. Models process data. People set intent, judge context, and carry the can when decisions matter. Agents will take on more tasks. The duty to decide will remain with us.Upskilling then becomes less about turning everyone into a prompt whisperer forever and more about teaching teams to think with these tools. Inputs improve, outputs improve. Middle managers, in particular, gain new leverage for research, planning, and option testing. The job shifts toward framing better questions and challenging the first answer that comes back.

In this episode I sit down with Mo Cherif, Vice President of AI Innovation at Sitecore, to explore one of the biggest shifts in business today: the rise of agentic AI. Unlike traditional AI models that focus on narrow tasks, agentic AI brings autonomy, reasoning, and collaboration between specialized agents. It is changing the conversation from automation to transformation.Mo explains how agentic AI is reshaping marketing, customer engagement, and creativity. From hyper-personalized chat-driven discovery to removing repetitive project management tasks, we look at how AI can free marketers to focus on strategy, storytelling, and innovation. He also shares why success depends on three foundations: context, mindset, and governance.We dig into Sitecore’s three pillars of brand-aware AI, co-pilots, and agentic orchestration, and how the company’s AI Innovation Lab, launched with Microsoft, helps brands experiment, co-innovate, and apply these ideas in practice. Mo also reflects on lessons from real projects such as Nestlé’s brand assistant and looks ahead to a future where personal AI agents interact directly with others on our behalf.If you want to understand how agentic AI is moving from hype to real business impact, this episode will give you practical insight into what is already happening and what comes next.*********Visit the Sponsor of Tech Talks Network:Land your first job in tech in 6 months as a Software QA Engineering Bootcamp with Careeristhttps://crst.co/OGCLA

When access to advanced AI models is no longer the big differentiator, the real advantage comes from how effectively a business can connect those models to its own unique data. That was the central theme of my conversation with Rahul Pathak, Vice President of Data and AI Go-to-Market at AWS, recorded live at the AWS Summit in London.In a bustling booth on the show floor, Rahul explained how AWS is helping organisations move from AI pilots to production at scale. We discussed the layers of infrastructure AWS provides, from custom silicon like Trainium and Inferentia to services such as SageMaker, Bedrock, and Q Developer, and how these combine to give enterprises the flexibility and performance they need to build impactful AI applications.Rahul shared examples from BT Group, SAP, and Lonely Planet, each showing how the right blend of tools, data, and strategy can lead to measurable business results. Whether it is accelerating code generation, generating custom travel guides in seconds, or using generative AI to produce personalised content, the common thread is a focus on business outcomes rather than technology for its own sake.A key point in our discussion was that most companies do not have their data ready to power AI effectively. Rahul broke down how AWS is helping unify siloed data and make it available to intelligent applications, turning a company’s proprietary knowledge into a competitive edge. We also touched on responsible AI, sustainability, and the operational challenges that come with scaling AI, from cost efficiency to security and trust.For leaders still weighing up whether to invest in generative AI, Rahul’s message was clear: waiting too long could mean being left behind. This episode is a practical guide to what it takes to deploy AI with purpose and how to ensure it delivers lasting value in a fast-changing market.

What if the food we eat every day is silently undermining our health, and AI holds the key to reversing it?In this episode of AI at Work, I sit down with Jonathan Wolf, co-founder and CEO of Zoe, to explore the intersection of AI, microbiome science, and the future of personalized nutrition. If Zoe sounds familiar, it’s likely because of their groundbreaking COVID study app or their clinical trial published in Nature Medicine proving Zoe’s approach is more effective than standard dietary advice. But this isn’t just about test kits or health trends.Jonathan shares the origin story behind Zoe, including how a chance meeting with Professor Tim Spector turned a pivot from adtech into a mission-led company focused on improving the health of millions. We explore:How AI is powering Zoe’s free new app launching in the USThe dangers of ultra-processed food and what’s really inside your mealsWhy personalized advice and behavior change, not food tracking or perfection, are key to long-term healthWhat shotgun metagenomics can tell you about your gut and why that mattersThe ethical challenge of combating food industry misinformation at scaleFrom photo-based food recognition to conversational AI that understands your microbiome, Jonathan breaks down how science, data, and product design are working together to make health advice smarter and more accessible.Whether you're a founder thinking about your next pivot or someone just trying to eat better without obsessing over every bite, this conversation offers real insight and practical steps.

What if your tools could finally talk to each other and reduce meetings, manual tasks, and copy-paste chaos in the process?In this episode of AI at Work, I sit down with Sanchan Saxena, Head of Product for Work Management at Atlassian, to unpack the thinking behind their new Teamwork Collection. Recorded live at Team 25 in Anaheim, this conversation explores how Atlassian is bringing together Jira, Confluence, Loom, and AI-powered agents into a single, streamlined experience.Sanchan shares how his team is designing tools that not only integrate more deeply but also help companies work more effectively. We discuss how AI is now summarizing meetings, creating Jira tickets from Loom videos, and pulling historical campaign data directly into brainstorming sessions in a way that fits how teams actually work.We explore:How the Teamwork Collection helps overwhelmed teams cut through digital noiseReal-world use cases from companies like Rivian saving hundreds of hours a yearWhy context switching kills productivity and what a unified experience can solveThe growing role of agentic AI in supporting, not replacing, teamsHow Atlassian is helping customers overcome change fatigue and adopt new workflowsWhy AI is no longer a luxury but a critical enabler of business velocityWhether you're leading digital transformation or just trying to tame your team’s growing tool stack, this episode offers clear insights into where collaboration is heading and why simplicity, clarity, and connectedness are the new competitive edge.Explore the Teamwork Collection at atlassian.com/collections/teamworkAsk ChatGPT

In this episode of AI at Work, I sit down with Mike Mason, Chief AI Officer at Thoughtworks, to explore what happens when the generative AI hype starts to settle and businesses begin asking the real questions. What’s working, what’s not, and what does mature adoption actually look like in 2025?Mike brings a practical, deeply informed view of the AI landscape. We talk about how intelligent agents are evolving well beyond basic chatbots and starting to act as collaborative teammates inside real workflows. From customer support to software development, these agents are now reasoning, adapting, and in some cases, working alongside other agents to get things done.We also explore the growing shift toward open source AI. Mike explains why some companies, especially in regulated sectors like financial services, are leaning into in-house or fine-tuned small models for better control, data security, and flexibility. We unpack what’s driving the rise of small language models and why in many cases, smaller, more nimble models are outperforming their larger counterparts in speed, privacy, and efficiency.One of the most thought-provoking parts of our chat was about the diverging paths organizations are taking with GenAI. Mike shares insights from Thoughtworks’ upcoming global survey, which shows that while some are embedding bias detection and strong governance into their strategies, others are focused purely on quick wins and interpretability. That divide is shaping not just how projects are executed but how companies are thinking about long-term AI maturity.If you're navigating the tension between speed and safety or trying to decide whether to build, fine-tune, or adopt off-the-shelf models, this conversation offers real perspective. We cover explainability, regulation, open ecosystems, and what tech leaders should be planning for next as AI becomes part of everyday business.This isn’t about future hype. It’s about how AI is actually getting to work.

In this episode of AI at Work, I’m joined by Simon Ranyard, Managing Director for Northwest Europe at Orange Business, to challenge old assumptions about manufacturing and reveal how technology is rewriting the rules.We discuss how AI, automation, augmented reality and 5G are giving manufacturers the tools to boost productivity, reduce downtime and create high-value careers instead of cutting jobs. Simon shares practical insights on where the UK stands, how to close the skills gap, and why apprenticeships and reskilling are more important than ever.If you think factories are all grease and gears, this conversation will make you think again. Take a closer look at how Orange Business is helping manufacturers adapt and thrive, and what this means for workers, companies and the wider economy.

In this episode of AI at Work, I sit down with Dennis Woodside, CEO of Freshworks, to uncover how real companies are getting true value from AI.Dennis shares how Freshworks has built AI tools that help businesses resolve routine questions automatically, boost agent productivity, and give managers clear performance insights without needing complex dashboards. He explains the company’s focus on making AI quick to deploy and simple to buy, so mid-sized companies can see immediate returns without endless consulting bills.We explore customer stories like Total Expert, which saved thousands of agent hours and saw a 250 percent return on its AI investment. Dennis also talks about the lessons learned from integrating AI internally and how the company stays flexible enough to adopt the latest advances from across the industry.This conversation is for anyone who wants to see beyond the AI hype and hear how smart companies are using it to save time, cut costs, and let people focus on more rewarding work.

In this episode of AI at Work, I catch up with Amanda Brock, CEO of OpenUK, for a wide-ranging conversation on the changing landscape of open technology, AI transparency, and international collaboration.We explore how OpenUK is working ahead of the market, helping shape policies and support for open source projects while responding to rising geopolitical tensions and funding pressures. Amanda explains how the UK occupies a unique position between the EU and the US and what that means for future AI standards and regulatory frameworks.We also discuss:The sustainability challenges facing open source communities and maintainersShifts in AI development, including legal and ethical questions around IP and model transparencyThe role of tools like Roost and initiatives like Current AI in creating practical solutions for AI governanceWhy "tools, not rules" may offer a more realistic path than top-down regulationThe importance of keeping open source accessible as a route into the tech industryAmanda shares her concerns about the rollback of EDI efforts and highlights how open communities can still offer a clear path into tech for people from underrepresented and underserved backgrounds. We discuss OpenUK's upcoming skills report and how it aims to highlight open source as a solution to address the ongoing talent shortage.Recorded ahead of International Women's Day, this episode also reflects on the slow progress around diversity and how leadership, policy, and community must come together to drive lasting change.If you're interested in how policy, law, and open technology intersect with AI development, this conversation offers thoughtful perspective, clear examples, and real-world action.🎧 Listen now and let us know where you think the future of open innovation is headed.

In this episode of AI at Work, I sit down with Juan Orlandini, CTO North America at Insight, to unpack the often-overlooked side of AI adoption: regulation, data strategy, and governance. While much of the recent conversation around AI has focused on speed, productivity, and experimentation, Juan brings the discussion back to fundamentals. Before you scale that shiny new AI tool across your business, have you classified your data? Have you considered your compliance obligations? And do you understand the different responsibilities that come with being an AI creator, adapter, or consumer?Juan walks us through Insight’s perspective on the current state of enterprise AI, including how they’ve used their own internal tools like InsightGPT to stress test both opportunities and risks. We discuss why internal use cases are often the best place to start, and how leaders can avoid repeating the mistakes of past tech waves, like the race to cloud or mobile apps without a clear strategy.We also explore the patchwork of US regulations, with California leading the way, and compare this to the EU’s more prescriptive approach. Juan explains how these emerging policies are shaping real business decisions right now, and what business leaders can do to stay ahead. Throughout our chat, his advice is grounded and practical, offering a steady counterpoint to the noise and hype.Whether deep in deployment or just starting to explore how AI fits into your business, Juan's insights offer a roadmap to thinking bigger while avoiding costly missteps. How do you keep your organization agile enough to adapt, but stable enough to deliver? And what does it really take to treat AI as an enterprise tool rather than a passing trend?Tune in to hear Juan’s advice on managing risk, reimagining processes, and building a culture that is ready for what comes next.

What if AI could help us discover new medicines faster, more accurately, and with greater impact for patients?In this episode of AI at Work, I speak with Dr. Chris Austin, Head of Research Technologies at GSK, to explore how artificial intelligence is changing the way new treatments are developed. Chris brings a unique perspective shaped by decades of experience across academia, biotech, government, and now big pharma. His mission at GSK is clear: to bring science, technology, and talent together to radically improve human health.We unpack how AI, combined with massive clinical and genetic datasets, is enabling GSK to target disease with unprecedented precision. From identifying the right molecular pathways to simulating clinical trials using digital twins, Chris walks us through how technology is helping reduce development timelines and increase the chances of success. He shares powerful examples including a promising asthma treatment that moved from first-in-human testing to Phase 3 trials across four diseases in record time.We also explore how GSK uses AI to improve patient selection in clinical trials, design oligonucleotide-based therapies for hard-to-treat conditions like hepatitis B, and incorporate generative AI into everything from drug design to safety prediction. According to Chris, the key isn't just having better algorithms. It's about generating the right data, at scale, to make those algorithms meaningful.If you're curious about how AI is being applied to some of the most complex problems in healthcare, this episode offers a rare inside look. Chris also reflects on his journey from medicine to data science, and why this is the most exciting time he’s seen in drug development.

How do you transform a century-old creative institution into a future-ready force without losing sight of its roots?In this episode of AI at Work, we spotlight Dr. Joyce Brown, President of the Fashion Institute of Technology (FIT), who has spent 26 years leading a quiet revolution in fashion education. As the first woman and first African American to hold the role, Dr. Brown has reimagined what it means to prepare students for creative careers in a digital world.She shares how, when she took the helm in 1998, FIT was operating with outdated systems and a siloed approach to education. Through strategic planning, bold hiring decisions, and a commitment to change, she reshaped FIT into a collaborative, interdisciplinary, and forward-looking institution. Under her leadership, FIT quadrupled its use of technology in teaching and launched the DTech Lab, a hands-on innovation hub where students work directly with brands like Netflix, Adidas, Girl Scouts, and Tommy Hilfiger to solve real challenges using emerging tech like AI and advanced materials.This episode also explores how FIT is fostering a new wave of sustainable design. Students are using kombucha, mycelium, and pineapple fibers to rethink fashion from the ground up, while also cultivating a natural dye garden on campus. We unpack how the school integrates innovation, science, and sustainability without losing the soul of design.Dr. Brown reflects on how FIT has responded to social and global shifts, from the pandemic to social justice movements, and how students are using creative work to make sense of the world. Her insights offer a compelling look at what education can achieve when it embraces experimentation, diverse voices, and emerging technologies.Whether you're in fashion, tech, education, or simply interested in how institutions evolve, this conversation offers a masterclass in visionary leadership and what it takes to truly modernize without losing meaning.How are you preparing your team or organization for a future shaped by creativity, technology, and purpose? Join the discussion.

What if the limitations of search engines and conventional AI tools are holding back your ability to truly understand the world? In this episode of AI at Work, we welcome Mel Morris, founder and CEO of Corpora.AI, to explore how artificial intelligence is reshaping the way we research and consume information across industries and professions.Mel, known for his pivotal role in the early success of Candy Crush creator King, has now set his sights on transforming how individuals, businesses, and institutions discover knowledge. With Corpora.AI, he has created a powerful research engine that processes two million documents per second and delivers comprehensive reports containing up to 500 cited sources per query. The platform ingests over 100 petabytes of open-source intelligence in real time, offering unparalleled speed, scale, and accuracy.During our conversation, Mel explains why traditional search methods no longer scale for human users and how Corpora.AI addresses this by using real-time data ingestion, multilingual capabilities, and dynamic content summarization. We discuss how the platform is being used by academics, journalists, legal professionals, and even medical researchers to uncover deeper insights and verify claims quickly.Mel also breaks down how the platform avoids common AI pitfalls such as outdated information, source ambiguity, and bias. Every report produced through Corpora.AI is transparent, traceable, and backed by robust citations, allowing users to make informed decisions with confidence. We also touch on the impact this could have on democratizing access to advanced research, especially in underserved regions.With the future of work demanding faster, more credible, and more comprehensive access to information, can AI-powered research engines like Corpora.AI redefine how we learn and make decisions? Tune in to hear how this technology is setting a new benchmark for speed, transparency, and trust in research.

Artificial intelligence is no longer a distant ambition, it is actively reshaping how businesses operate, innovate, and compete. But what does AI truly mean for the workplace of today and tomorrow? And as the pace of advancement accelerates, are organizations truly ready for what comes next?In this episode of AI at Work, we explore these questions with Louis Landry, newly appointed CTO of Teradata. With over two decades of experience in software architecture, engineering leadership, and technology innovation, Louis brings a grounded and insightful perspective on how businesses can harness AI responsibly and effectively.Together, we unpack some of the defining trends for 2025: the maturation of retrieval-augmented generation, the evolution of large-scale personalization, and the rise of agentic AI systems that blend generative AI with traditional software architectures. Louis explains how enterprises are moving beyond experimental AI projects to focus on outcome-driven deployments that deliver measurable business impact.Throughout our conversation, Louis stresses a recurring theme: trust. Building trusted AI, grounded in transparency, human accountability, and high-quality data, is essential for sustainable success. He shares practical strategies for managing emerging challenges such as vector data governance, navigating regulatory uncertainty, and balancing innovation with responsible risk management.We also explore the vital role of data harmonization in achieving faster, more confident decision-making, and how open-source technologies are enabling more accessible and customizable AI solutions across industries. Louis highlights why data quality, explainability, and clear business outcomes should be the North Star for any organization looking to thrive in an AI-driven future.As businesses face an increasingly complex digital environment, what strategic investments should they prioritize? How can they build AI systems that remain trustworthy, scalable, and truly transformational? And what leadership mindset is needed to unlock the next era of workplace innovation?Tune in to hear Louis Landry’s insights on the future of AI, and join the conversation: How do you see AI shaping the future of work in 2025 and beyond?

What happens when artificial intelligence moves beyond assisting individual developers and solves problems across thousands of codebases simultaneously?In this episode of AI at Work, we explore how AI is being used to tackle one of the most complex challenges in modern software development: large-scale code migrations. Justine Gehring, AI research engineer at Moderne and author of AI for Mass-Scale Code Refactoring and Analysis, joins the show to explain how she and her team are helping enterprises rethink how they approach code changes across massive environments.While many are familiar with tools like GitHub Copilot and ChatGPT that assist with writing or suggesting code snippets, Justine shares how mass-scale refactoring calls for a very different set of tools and methods. At Moderne, AI is applied with precision inside an open-source framework called OpenRewrite, which enables consistent and verifiable code changes while maintaining enterprise-level reliability and security.We discuss how Moderne's approach blends deterministic automation with targeted machine learning to make code migrations faster and more trustworthy. From onboarding new developers to simplifying upgrades across legacy systems, the real-world impact of this work is becoming increasingly visible in sectors like banking and insurance, where complexity and risk have historically slowed down innovation.This episode also dives into how AI enhances collaboration between developers and machines. Justine highlights the potential for AI to become a quiet partner in understanding, searching, and maintaining vast repositories of code and why this shift may help organizations reduce technical debt and increase maintainability over time.For business leaders evaluating how AI fits into their development strategy, this conversation offers a practical look at how to make meaningful progress without cutting corners. Whether you're leading a digital team or managing critical systems, Justine's insights reveal what it truly takes to put AI to work at scale.