AI Moment With Danny Denhard and Jonathan Wagstaffe

Hey, It's Danny (Denhard) - thanks for listening today! We appreciate you spending 10 minutes with us today!In this episode of The AI Moment, Jonathan and I dive into the often-frustrating reality of AI adoption in the workplace. Many organisations have invested in the licenses but are seeing little to no progress in their actual operations. We explore why the "license and leave it" strategy is failing and how leaders can pivot toward a more effective, layered training model.We discuss the critical distinction between seeing AI as a technical skill versus seeing it as "AI fluency," a fundamental business competency similar to communication or project management. I share my perspective on why framing AI as an "assistant" can help teams overcome the fear of replacement and speed up adoption.The conversation covers practical strategies for building momentum, including:Moving beyond the "one-off webinar" to role-specific workshops and hackathons. The importance of self-learning and why waiting for a corporate manual is a losing strategy. How to use "Lunch and Learns," peer-to-peer training, and even "AI Bingo" to gamify the learning process. The non-negotiable role of leadership in "normalising" experimentation. The immediate action for any leader listening is to stop treating AI as an IT initiative and start treating it as a core part of Learning and Development. Start small with foundational sessions, but get your teams into a room to solve real-world problems as quickly as possible.Want more? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode.

Thanks for listening today In this episode of the AI Moment, Jonathan and I tackle a common challenge facing many leaders today: what to do when your team simply refuses to use AI. Resistance is rarely about being "difficult." More often, it is a rational response to a perceived threat to one's job, identity, or even the planet. We dive deep into the psychology of these barriers and discuss how to categorise and address them individually rather than treating your team as a monolith of resistance.We discuss the "Personal Trainer" approach to AI implementation, where leaders must personalise training plans to meet individuals where they are. This includes being radically transparent about governance and setting a high quality bar so that AI is seen as a tool for excellence, not just a shortcut for quantity. We also touch upon the "Internal Show and Tell," a vital strategy for shifting the mood of the office by celebrating tangible wins. Finally, we address the "Ecological Elephant" in the room, providing a balanced perspective on AI’s energy consumption and how to discuss this with environmentally conscious team members.Struggling With AI?Jonathan and I are hosting dedicated bespoke AI Workshops and AI Hackathons helping companies improve their AI capabilities and business performance, book in a time to chat to get you moving ahead Ask your own question to [email protected]

In this episode, Jonathan and I dive into the complexities of the current AI landscape, specifically focusing on why the initial “gold rush” is giving way to a more sober reality for the enterprise. We explore the “rumour mill” culture between major labs like OpenAI and Anthropic, noting how reactive releases are making it nearly impossible for procurement and policy teams to keep up.I share my concerns regarding the “performance anxiety” hitting OpenAI as they struggle to meet internal revenue metrics to pay for massive compute costs. Our conversation shifts to the “SaaSpocalypse,” explaining how the ability to “vibe code” custom tools and the compression of seat-based licences is forcing a total rethink of software valuations. Finally, we tackle the critical issue of governance, warning that unleashing autonomous agents in messy data environments is the corporate equivalent of giving a 14-year-old the keys to a Ferrari.This episode concludes with a look at the “cognitive debt” we risk incurring if we outsource too much thinking to models that are, by definition, the average of the internet. For any leader looking to move beyond “productivity theatre” and into genuine, sustainable AI integration, this is a must-listen.Timestamps:01:02 -  The New AI Releases & What Do They Mean04:46 -  Are Big Releases A Thing Of The Past? Or Is Agentic One Big Major Release Coming Up?06:51 -  Should Users Select The Models Anymore?09:31 -  Potential Long Term OpenAI Issues12:21 -  ROI In Enterprise And AI? 18:18 - Is The Future Of Work Vision Missing Right Now?22:58 -  AI Governance & Controlling AI At Government Level32:25 -  Agents Governance & Automation Control?33:50 -  The Messy Process & Environment Issue Of Governance42:43 -  The SaaSpolcalyse? 49:21 -  Future Skills For The New AI WorkforceQuestions? Have more questions about AI and leadership? Reach out to us directly by emailing [email protected] or hit message below and we will tackle on a dedicated podcast episode On LinkedInDanny on LinkedIn - https://www.linkedin.com/in/dannydenhard/Jonathan on LinkedIn - https://www.linkedin.com/in/wagstaffe/

Thanks for listening Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode----In this episode, I sit down with Jonathan to dissect how AI is moving from a "transformational" buzzword to a practical engine for business growth. We dive deep into the IKEA case study, where the analysis of service desk calls didn't just lead to automation, but to the creation of a brand-new interior design service that generates millions in revenue. This shift from saving money to making money is the blueprint for modern AI implementation.We also explore the broader impact of AI across sectors that don't always make the tech headlines. We discuss AgTech’s role in saving billions through water and yield optimisation, and the incredible strides in Swedish healthcare where AI is detecting cancer months ahead of radiologists. For larger enterprises, we look at the hard numbers from Alphabet and American Express, where AI is slashing fraud and generating 50% of new code.The immediate action for any leader listening is to identify your "low-hanging fruit," such as customer support archives, and then start imagining the "10X" possibilities. AI is not just about efficiency; it is about doing what you never could before

In this deep-dive episode of the AI Moment, Jonathan Wagstaffe and me (Danny Denhard) strip away the LinkedIn hype to look at the "real" state of AI in April going into May 2026. The conversation focuses on the widening gap between companies that are merely talking about AI and those that are aggressively retooling their operations to stay competitive.---We go deeper on our newsletter with each and every episode, please don't forget to subscribe here and we will help you with each step on the AI journey.---We explore the "Uber Effect" where companies are blowing through token budgets due to a lack of creating a flexible financial environment with how much pressure is being applied on building.One core area we touch on (and is going to be a theme for a long time is) the critical importance of "Operational Readiness." I argue that without a solid repository of company documentation, any attempt to deploy AI agents will likely fail or create more work than it saves. This episode also touches on the "Compute Problem," discussing how "AI slop" and power limitations are beginning to throttle the growth of major LLMs, leading to potential price hikes and tiered service models in the near future.Finally, we discuss the battle for model dominance. While Claude is becoming the preferred "work partner" for professionals, ChatGPT is attempting to become a consumer super-app, and Gemini is leveraging its massive search and workspace data. The takeaway for leaders is clear: 2026 is the year of separation. You must move from awareness to implementation, focusing on solving core business problems rather than just chasing the latest feature.Here are the timestamps: 1:01 - Adoption Curve and its importance 4:45 - tokens and the importance of not buring through tokens and tokenmaxxing 6:15 - Ops readiness and having the right level of documentation to get the most out of AI 9:16 - AI Jobs Replacement, Tasks and Skills Development12:29 - AI Needs Training Through Workshops & Hackathons17:04 - AI Models - Why Anthropic Is Winning BIG & ChatGPT's Consumer Play Explained30:49 - The Compute Problem & Is AI Slop The Actual Cause?38:04 - The Latest Privacy Problems & Why You Need To As You're Now The AI Training DataStruggling With AI?Jonathan and I are hosting dedicated bespoke AI Workshops and AI Hackathons helping companies improve their AI capabilities and business performance, book in a time to chat to get you moving ahead

In this session, Jonathan and I explore why the frequent question "Which model should I use?" is actually the wrong place to start. Whether you are utilising ChatGPT, Claude, or Co-pilot, the underlying engine is only as good as the context it is provided. We discuss the psychological trap of "Model FOMO" and why business leaders should focus on developing "Prompting and Clear Thinking" as a core executive skill for the coming years.We re-share our company context document to help you become more successful with LLMs. We break down actionable strategies to improve your AI interactions immediately. This includes the "GCSE" framework (Goal, Context, Sources, Expectations) and the "Interview" technique, where you allow the AI to extract the necessary information from you rather than trying to write the perfect prompt from scratch. We also touch upon the efficiency of creating permanent context documents, such as PDFs describing your brand or ICP, to ensure consistency across all AI-generated work. The goal is to move from treating AI as a search engine to managing it like a highly capable team member.

Thanks for listening! Remember we have an accompanying newsletter with every podcast - where we go a bit deeper - https://aimomentpodcast.substack.com/subscribe In this episode of the AI Moment, Jonathan and I tackle the "value gap" many business leaders face when first experimenting with AI. We dive into the critical differences between free and paid AI subscriptions, highlighting why sticking with free models might actually be hindering your organisation's progress. We discuss the psychological hurdle of the $/£20 per month subscription and why this "SaaS tax" is negligible compared to the cognitive lift the tools provide. Jonathan explains that free models often result in "bland" outputs because they lack the sophisticated reasoning of the more advanced versions. I share my personal journey of transitioning from free Claude to paid Gemini, specifically how features like "Gems" unlocked a higher level of utility for my daily operations. Our conversation concludes with actionable advice for leaders: try it yourself first, experiment in a domain you understand well to judge quality accurately, and use "meta-prompts" to break down recurring business problems. We believe that once you experience the "magic moment" of a full-powered model, you will never look back.

Welcome back everyone. I'm proud to say we bring you a special episode today. I interviewed Dr. Kelly Monahan, a researcher (and former leader at Upwork and Meta) and advisor to Fortune 1000 companies, about AI’s impact on workplace culture and the future of work. Keep an ear out for my 7 hidden gems in this podcast 1. The "Power Paradox" of AI Adoption2. The Risk of "AI Peer Pressure"3. "Digital Exhaust" as a Competitive Advantage4. The Threat to "Expertise Dignity"5. Playing "Checkers vs. Chess" with Headcount6. The "Pilot" vs. the "Checkout" Model7. The $1-for-$1 Investment RuleThese are packed full of leadership advice and positive steps! The areas you will love: Rethink Business: Kelly argues generative AI should prompt leaders to rethink business purpose beyond shareholder maximisation, warning of a crossroads between human flourishing and inequality-driven displacement. AI Is Transformation: She emphasises AI adoption is primarily a prioritisation and change-management challenge, not just tooling, and uses her “elevator/skyscraper” analogy to push leaders toward workflow redesign rather than doing the same work faster with fewer people. Human First Approach: We discuss preserving human decision-making (e.g., pilots, human checkout), risks of agentic AI increasing complexity and governance/legal exposure (e.g., healthcare claims), and research showing heavy AI users may trust AI over colleagues, potentially eroding workplace connection. The AI Business Metrics: Kelly advises defining AI skills, measuring readiness, focusing on growth metrics like revenue per employee, clarifying company purpose and AI principles, and investing in upskilling alongside technology. Kelly also has a forthcoming book coming out called “Reclaim the Plot” and it sounds like the perfect way to address work issues. Please connect with Kelly below Personal site - https://drkellymonahan.com/Company site - https://www.beyondthedesk.com/LinkedIn - https://www.linkedin.com/in/kelly-monahan-ph-d-18879413/As it is an AI Moment interview here are the chapters to go through if you are short on time: 00:00 Meet Dr Kelly01:03 AI Purpose and Workforce03:06 Adoption Guardrails and Priorities05:41 Elevator Moment Workflow Redesign07:11 Human in the Loop and Agentic Risks17:05 Remote Work Politics and Power27:04 Reclaim the Plot29:30 HR Takes Back the Layoff Story32:34 Metrics That Value People38:47 Align on Purpose and Principles41:34 Human And/Plus AI To Improve Work43:30 Three Leadership Principles

In this episode, Jonathan and I dive into the practical applications of voice AI that go far beyond simple voice-to-text. We start by looking at the "surprise moments" in our workshops, where leaders realise they can use their phones to simulate high-pressure business environments. We discuss how a hotel group used ChatGPT to train receptionists by role-playing as a complaining customer, providing immediate coaching on how to improve the interaction.We also explore the "unlock" of using voice for strategic documentation. I share a personal example of how Jonathan helped a sales leader turn a quick conversation into a full strategy document using Gamma, reducing a day’s work to mere minutes. We look at the broader business impact, such as how IKEA used voice analysis to discover a massive demand for interior design services, turning a cost-saving exercise into a new revenue stream.Finally, we address why voice is such a powerful tool for those who find writing a barrier, including individuals with dyslexia, and how tiny lapel mics are becoming a new norm in the London startup scene to facilitate constant AI collaboration.Enjoy the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight. Left wanting more? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode.

In this episode of The AI Moment, I sat down with Jonathan Wagstaffe to tackle one of the most pressing questions for modern business leaders: Is it time to build your own company LLM? We move past the hype of "building from scratch" to discuss the practical realities of the Rent, Buy, vs. Build framework.We explore why context is the new currency in AI. It is no longer enough to simply use a public model; to gain a competitive edge, businesses need to integrate their own operating procedures and product ecosystem into the AI's workflow. However, this isn't without significant risk. We discuss the "dull, boring" but essential issue of data quality, noting that messy or fragmented data will undermine even the most sophisticated AI ambitions.The conversation highlights Yahoo Scout as a leading example of the "hybrid model"—taking a powerful base like Claude and layering specific data on top to create a specialist tool. For leaders, the takeaway is clear: be mindful of the exploding costs of token usage and the scarcity of AI expertise. Instead of chasing a "naked LLM," focus on building the proprietary guardrails and intelligence layers that turn a generic tool into a powerful business asset. As the enterprise space evolves rapidly toward the summer of 2026, staying agile with a hybrid approach is your best bet to avoid being "cleaned out" by rapid platform shifts

In this episode of The AI Moment, Jonathan Wagstaffe and me (Danny Denhard) tackle the most pressing question facing modern executives: How do we actually measure the success of AI?As businesses move past the initial excitement of generative tools, the challenge is to move away from "instinctive" utility and towards rigorous, actionable KPIs that satisfy the boardroom.The discussion centres on moving the goalposts from measuring the AI itself to measuring the impact on existing business metrics. I introduce a robust four-pillar framework for leaders to adopt: Velocity, Quality, Economic, and Strategic. This includes looking at "Keep-Me-Out-Of-Jail" metrics like hallucination rates and the "Human-in-the-Loop" (HITOR) rate - measuring how much human intervention is required to make AI output viable.Sign up to the newsletter >> We go deeper on each & every episode on our supporting newsletter - with more commentary and more insights. Subscribe to our newsletter at ⁠https://aimomentpodcast.substack.com/ Or if you want to go deeper on AI reporting here is today’s newsletter - https://open.substack.com/pub/aimomentpodcast/p/what-are-the-key-performance-indicators?r=2byz&utm_medium=iosWe also explore the departmental nuances of these KPIs, noting that success in Sales looks very different from success in Support or Operations. Whether it is reducing proposal turnaround time or decreasing product decay rates, the message is clear: AI is a lever for business outcomes, not an outcome in itself.Key Takeaways for This Week:Identify the "business results" you want before deploying the tool.Track "saved time" through the lens of what that time is reinvested into.Establish "Trust and Reliability" metrics to manage hallucination risks.Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight

In this mailbox episode, Jonathan (Wagstaffe) and I dive into a challenge many of you are facing: a CEO who is obsessed with AI but lacks a fundamental understanding of its limitations. It is a classic case of managing up. When your leader is influenced by the "best-case" scenarios shared on social media, your job is to redirect that energy into grounded, actionable strategy.We discuss the necessity of executive education, specifically through dedicated training days and hands-on hackathons. These sessions are designed to pull back the curtain on the "magic" and show just how frustrating and difficult AI implementation can be when you move past the "vibe marketing" stage. We also explore the dangers of "AI washing," where companies use the technology as a shield for headcount reductions, often leading to the eventual need to re-hire the talent they prematurely let go.The episode provides a roadmap for shifting the conversation from "replacing jobs" to "replacing tasks" through workflow mapping and measured experiments. We wrap up by re-introducing the "3 Ts (Time, Truth, Trust) and 2 Vs (Validate and Verify) " framework for maintaining quality and reliability in an era of AI hallucinations. This is about ensuring your business moves at pace without sacrificing the truthThanks for listening! Danny Denhard

In this episode, Jonathan Wagstaffe and I pull back the curtain on the world of autonomous AI agents the "claws" that are rapidly moving up the attention curve. We’ve moved past the era of simply chatting with AI; we are now entering the age of the digital worker. We explore the rise of Openclaw, a tool developed in late 2025 that allows AI to interface with apps like WhatsApp and Telegram to execute tasks on its own.While the potential to have a fleet of AI Executive Assistants is intoxicating, we spend a significant portion of this conversation on the security risks that many are ignoring. I break down why the Chinese government recently hit the brakes on agentic AI after a "rampant" period of adoption, and why "prompt injections" are the new frontline for corporate security.We also discuss the practical path forward for businesses. Rather than going "all in" and getting burned, we advocate for a "steady as she goes" approach. This means identifying workflows where human capital is being wasted on non-sensitive data and using those as your sandbox for agentic experimentation. If you’re looking to understand the difference between a chatbot and a true autonomous agent and how to deploy the latter without emptying your bank account this episode is essential listeningThanks for listening today!Danny Denhard & Jonathan Wagstaffe

Thanks for listening today! In this episode, Jonathan Wagstaffe and I dive into a question that is keeping many business leaders awake: Is it time to switch LLMs? As Gemini, Claude, and ChatGPT begin to carve out specific niches, the temptation to jump ship for the "latest and greatest" version is higher than ever. However, we explore why this constant pivoting might actually be hindering your business growth rather than helping it.---✅ Want more explainers - Subscribe to our newsletter at ⁠https://aimomentpodcast.substack.com/subscribe⁠ for deeper dives and intelligence delivered with every podcast episode! ---We discuss the reality of switching costs, which go far beyond the monthly invoice. I share my personal experiences using Gemini within the Google ecosystem and why I believe the "agentic" roadmap should dictate your long-term choices. We also touch upon the psychological impact on employees; forcing a team to relearn their basic AI toolkit every few months breeds resentment and stalls adoption.Recommended Actions to Take:Audit your current usage: Are you using a specific LLM because it’s the best, or simply because you haven't committed to a roadmap? Implement the "12-Month Rule": Choose a primary LLM for your organisation and commit to it for a full year to allow for deep integration.Refine your prompting culture: Encourage your team to use the AI to "check" their prompts, ensuring you get high-quality outputs regardless of the underlying model."Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight."

Thanks for listening today! In this episode of The AI Moment, Jonathan Wagstaffe and I pull back the curtain on why many corporate AI initiatives feel like a series of disconnected experiments rather than a commercial engine. The missing link? Workflow Mapping.We dive into the reality of the modern workplace where "unwritten rules" and undocumented processes create friction that no software can fix. I share my background in Lean and Six Sigma, explaining why breaking down tasks into their smallest components is the only way to truly understand where operational efficiencies lie. We discuss the critical distinction between "jobs" and "tasks," arguing that the future of work isn't about human replacement, but about human enhancement—removing the administrative "drudge work" like CRM updates to free up "magic moments" of creativity and high-level strategy.Immediate Actions to Take:Conduct a Workflow Workshop: Sit your cross-functional teams (Sales, Marketing, Ops) in a room and ask them to map a single customer journey. Don't be surprised when no one agrees on how it currently works.Identify the "Human Energy Leaks": Look for tasks involving heavy repetition, constant reviewing, or searching for information.Build a Target Team: Consider appointing a small, cross-departmental group to "fix the holes" identified in your map before you even mention an AI tool.Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight." "Want more? Subscribe to our newsletter at https://aimoment.co.uk for deeper dives and intelligence delivered with every podcast episode.

In this episode, Jonathan and I tackle the shifting landscape of the AI industry, asking the critical question: Is AI just for enterprise now? We explore the growing divide between the casual consumer, who is hesitant to fork out £20 a month, and the enterprise world, where businesses are eager to pay for tools that offer measurable productivity gains.We dive into why the financial models of companies like OpenAI necessitate a "business-first" approach to cover the astronomical costs of data centre build-outs. I share my thoughts on why Google’s integration of Gemini into existing workspaces was a masterstroke, and why Apple’s "wait and see" strategy might actually be the smartest move in the room. We also discuss how AI is becoming "invisible"—moving away from standalone chatbots and into the tools we use for banking, shopping, and emailing.Whether you are a "power user" or someone just starting to experiment, this episode provides a roadmap for navigating the "crawl" phase of AI implementation. We discuss practical automations you can set up today, from personal newsletters via Grok to enhancing sales calls with enterprise LLMs.Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight.Want more? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode.

In this episode of The AI Moment, Jonathan Wagstaffe and I dive into why the secret to professional AI adoption actually lies in your kitchen, your gym, and your home office. We move past the corporate jargon to discuss how "normal" people are using LLMs to solve real-world headaches, from automating the "chasing of payments" for local sports teams to creating automated family calendars that sync via an iPad in the kitchen.We break down the concept of "AI Lions"—those who use technology to sharpen their edge—versus "AI Sheep" who simply use it to bypass the work. The conversation highlights a crucial technical shift: moving from simple text prompts to a multimodal approach. By combining voice, imagery (like photographing a whiteboard), and text, users can provide the "thick context" that transforms AI from a basic chatbot into a sophisticated co-pilot.Immediate Actions For You:Experiment Domestically: Identify one complex personal task this week—a meal plan, a travel itinerary, or a budget—and use AI to manage the constraints.Digitise Your Thinking: Next time you’re in a meeting, photograph the whiteboard and ask your AI tool to "format this into a logical flow".Talk to Your Tools: Experiment with voice-to-text during your commute to capture ideas or "think out loud" to your AI assistant."Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight."Want more detail? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode."

In this episode of The AI Moment, Jonathan Wagstaffe and I (Danny Denhard) tackle one of the most significant misunderstandings in modern marketing: the idea that AI Search (AEO/GEO) is just "SEO with a new coat of paint". We dive deep into why these are fundamentally different channels that require a complete shift in how business leaders think about visibility and content.We explore the transition from the traditional search engine results page (SERP) to the "answer engine". I explain why the "click-through" model is being replaced by a "citation" model. If your strategy is still focused solely on keywords and internal links, you are missing the broader picture: LLMs are scanning Reddit, LinkedIn, and comparison sites to determine your authority. We also discuss the practicalities of content creation specifically why you must now optimise for both human readers and machine ingestion.While the metrics for success are still evolving and can feel like a "black box," I share my current recommendations for tracking direct traffic and brand signals. This is a rapidly changing landscape, and this episode serves as a vital primer for any leader looking to dominate their niche in the age of AI.Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight.Want more detail? Subscribe to our newsletter at https://aimoment.co.uk for deeper dives and intelligence delivered with every podcast episode. Thanks for listening Danny Denhard

In this episode of The AI Moment, Jonathan Wagstaffe and I tackle the primary reason leaders feel underwhelmed by AI: the "Generic Output Trap". We often see teams frustrated by results that feel disconnected from their brand or market reality. The solution isn’t better prompting alone; it’s better contextualisation.I introduce the concept of the Company Context Document, a "live" asset designed to be uploaded to your LLM of choice (ChatGPT, Claude, or Gemini) to give it the "experience" of a long-term employee. We outline the five essential pillars this document must include: your Ideal Customer Profile (ICP), brand style/tone of voice, product set, market positioning, and real-world examples . (this is your free template to use ;-) )We also discuss why this document is a litmus test for your operational health. If your leadership team cannot agree on these five pillars, your AI will produce fragmented, inconsistent work. Whether you are a small start-up or a large enterprise, the message is clear: stop treating AI as a case-by-case tool and start treating it as your operational infrastructure. Short, sharp, and constantly updated—this is the blueprint for high-signal AI work.Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight.Want more? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode.

In this episode, Jonathan Wagstaffe and I dive into why NotebookLM is the most underrated tool in the AI landscape today. Unlike standard LLMs that "guess," this tool focuses on synthesis, working exclusively with the data you provide—PDFs, URLs, or transcripts—to eliminate hallucinations. It’s a privacy-first, completely free powerhouse that acts as your personal research assistant.I share my "one→six" model for repurposing content and how I use it to kill writer's block by joining dots I hadn't seen before.Content Repurposing: Turn one podcast transcript into newsletters, show notes, and social posts.Instant Learning Guides: Upload YouTube links to generate a syllabus with specific timestamps for deep dives.Bias & Argument Checks: Upload your writing and ask the AI to find gaps or the "other side" of the argument.Rapid Asset Creation: Generate slide decks, FAQs, or cheat sheets from years of messy research in minutes.Deep Research Synthesis: Collate multiple technical articles to extract key quotes and source citations instantly.Get the full edge: Subscribe to our newsletter at aimoment.co.uk for deeper dives every Monday and Friday!

In this episode of The AI Moment, Jonathan Wagstaffe and I, (Danny Denhard), dive into the practical debate of how to actually interface with your favourite LLMs. While it seems like a simple choice of UI, the decision between a web browser, a desktop app, or a mobile hybrid workflow impacts your productivity, focus, and most importantly your data security.The Browser’s Flexibility: Browsers remain the "old faithful" because they ensure you are always running the latest version without manual updates. They are also superior for "multi-LLM" workflows, allowing you to easily switch between Claude, Gemini, and ChatGPT in side-by-side tabs.Desktop for Deep Work: Desktop apps offer OS-level integration, including keyboard shortcuts and clipboard workflows. They provide a "low distraction" environment that feels like a professional tool rather than just another open tab.The Hybrid Mobile Edge: I personally find the most power in mobile-to-desktop transitions. Use the mobile app for voice-to-text "brain dumps" while you’re out, then pick up the transcript on your browser when you get home.Governance Over Interface: For business leaders, the UI preference is secondary to risk management. You must be clear on whether your chat history is training the model and if your enterprise data is isolated.Horses for Courses: There is no "one size fits all". If your IT department allows it, explore desktop apps for high-output tasks, but stick to browsers for the most secure and up-to-date enterprise environment.What to do next: Audit your team's AI settings today to ensure "data training" is disabled for sensitive work.

Todays pod is on why Microsoft Copilot is often misunderstood and how to actually "unlock" its potential within your workflow. when it actually does exactly what it’s supposed to do? In this episode, Jonathan Wagstaffe & I (danny denhard) dive into the "Microsoft Branding Problem". We explore why users often feel frustrated by the tool’s corporate feel compared to the cool AI tools such as ChatGPT & Claude, and why shifting your perspective from "magic automation" to "assistant" changes everything.The Branding vs. Reality Gap: Most user "hate" stems from the fact that Copilot feels like a "boring" corporate tool rather than a creative playground. However, its strength lies in being built directly into the systems where you already work, like Teams, Excel, and Outlook.Assistant, Not Agent: A major source of frustration is the misalignment of expectations. Copilot is not an autonomous agent or AGI; it is a high-level assistant designed to draft, summarise, and spot patterns.The "Prompt at the Top" Rule: To get the best results, you must structure your prompts correctly. Because of how LLMs process data, I’ve found you need to put your instructions at the top before the data.Unlocking "Hidden" Power Features: Tools like enhanced voice mode in Teams for transcription and Python-based analysis in Excel can save weeks of work.Work Mode vs. Web Mode: You can toggle Copilot to use only your internal work documents for privacy and context, or switch to web mode to pull real-time information from the internet.Need help with AI? Get in touch with Jonathan and I

In this episode of The AI Moment, Jonathan Wagstaffe and I (Danny Denhard) dive into the uncomfortable "frontier" of AI surveillance. We kick things off with the fallout from Ring’s Super Bowl ad, which showcased a feature called "Search Party". While using a neighbourhood network of AI cameras to find a lost pet sounds like a dream for owners, it sparked a massive backlash from those who see it as a "statement of intent" for wider human surveillance.---Stay ahead of the AI curve:Subscribe to our newsletter for deeper dives, the latest AI ads, and actionable insights at ⁠aimoment.co.uk⁠.---We explore how we’ve spent 30 years trading privacy for utility. From the UK being the most CCTV-monitored nation in the 90s to every smart doorbell now acting as an AI "node," the line between "helpful" and "creepy" is blurring.The Surveillance Node: Every AI-enabled device—from your Ring doorbell to your car—is now a data-collecting sensor in a massive network.The Meta Glasses "Jailbreak": I discuss the facial recognition features in Meta glasses and how they’ve already been "jailbroken" to identify complete strangers, raising massive red flags for personal privacy.The Business Risk: We’re seeing "second-order" risks where teachers or therapists wear AI glasses without consent, creating immediate HR and leadership nightmares.The Data Question: It isn't just about being recorded; it's about who controls the data and who benefits from it.The value exchange for AI is shifting. As a leader, you must decide: is the utility you're gaining worth the trust you might be losing?Need Help With AI? Jonathan and I are hosting AI workshops and AI hackathons helping companies improve their AI capabilities and performance, book in a time to chat

We've been asked a lot lately if Europe has already lost the AI race to the US and China. While it’s easy to get distracted by the trillion-dollar giants in Silicon Valley, being a proactive leader today requires looking at the "layers underneath" the hype. ---Have more questions about AI and leadership? Reach out to us directly by emailing [email protected] needing a deeper dive, our newsletter that supports each and every episode at ⁠⁠dannydenhard.com/aipod⁠⁠---How to Think About the AI Landscape:Being proactive starts with shifting your mindset from consumption to specialisation. We discuss why the "Big LLM" era might be peaking, making way for a future dominated by Vertical AI—tools designed for specific industrial, clinical, or legal sectors. If you are waiting for a general model to solve your specific business problems, you’re already behind. Actionable Steps for Leaders:Audit your sector, not the news: Stop tracking every minor update to GPT and start monitoring the "remarkable little gems" appearing in your specific industry. Survive to Thrive: Your short-term goal is to get your infrastructure protected and operational so you can survive long enough to thrive when the market matures. Infrastructure over Applications: Understand the constraints—like power and capital—that dictate where the tech is heading. Proactive leaders look at the "power game" (literally) to predict which regions will scale next. While the "Big 3" LLMs are American, the real value is moving into the "layers underneath." We don't need to build the next ChatGPT to win; we need to own the verticals. 3 Actionable Takeaways for your team:Verticals over Generals: The next six months belong to industry-specific AI. Whether it’s healthcare, autonomous mobility, or legal tech, niche expertise is our "Formula One" advantage. Every company is an API company: Your unique data is your moat. Stop worrying about where the model is hosted and start focusing on how your data powers it. The Rise of Small Language Models (SLMs): Massive models are power-hungry. Specialized, localized SLMs are more efficient and often more secure for industrial applications. Europe’s story isn't over; we’re just moving from the "lab" phase to the "global market" phase. The Bottom Line:You don't need to move to Silicon Valley to win. By focusing on localized, geo-specific models and leveraging your own unique datasets, you can co-create an ecosystem that makes your business indispensable. Thanks for listening today! Danny Denhard

The AI Moment – Moving from Ask & Answer to Assistant to AgenticIn this episode, Jonathan Wagstaffe and I dive into the latest YouGov data on UK consumer sentiment. We strip back the hype to see how "Joe Public" is actually using AI. While the tech world is obsessed with autonomous agents, the reality on the ground is far more cautious.The Core Takeaway: Trust is the New CurrencyWe are witnessing a massive "trust gap".Only about 22% of consumers trust AI, and while they are happy to use it as a "slightly advanced Google" to find deals or compare prices, they aren't ready to hand over the credit card. For AI to truly scale, it must transition from being a persuasion tool (selling to you) to an empowerment tool (helping you).The "Three A’s" of AI AdoptionWe’ve identified a clear three-stage roadmap for how consumers interact with this technology:> Ask & Answer: The current baseline where users seek quick information or writing help.> Assistant: The middle ground where AI helps find discounts and compares options—this is where most people are currently comfortable.> Agentic: The future state where AI executes decisions and places orders—a stage that still feels a "long way away" for the general public.Actionable Insights for Leaders> Focus on the "Assistant" Phase: Don't rush into fully automated agents if your customers don't trust the tech yet. Build tools that help them make better decisions, not just faster purchases.> Bridge the Gender & Age Gap: Trust is currently higher in men and younger demographics. Consider how your AI interface can feel more accessible and reliable to a broader audience.> Transparency is Non-Negotiable: If a user feels like an AI is a salesperson rather than a helper, trust evaporates.The Bottom Line:We’ve moved from Ask & Answer to Assistant. To cross the Rubicon into Agentic AI, brands must prove that the AI is acting in the customer's best interest.

In this episode of The AI Moment, Jonathan Wagstaffe and I dive into the fallout from Super Bowl weekend, where Anthropic’s creative ad campaign put OpenAI firmly in the firing line. We explore the "rattled" response from Sam Altman and what it reveals about the high-stakes battle for AI dominance as we move into 2026.---Connect with Us:Have more questions about AI and leadership? Reach out to us directly by emailing [email protected] ---We are witnessing the "Netscape moment" for ChatGPT. Despite their massive head start, OpenAI’s market share has plummeted from 95% to roughly 50% in just one year as Gemini and Claude gain serious traction. As these platforms shift from providing utility to seeking aggressive monetisation, the user experience is changing rapidly.The Monetisation Shift: ChatGPT is introducing $60 CPM ads into their free and "near-free" tiers. I believe this is a "dirty secret" that could be reductive to the user experience, potentially driving users toward cleaner alternatives like Claude.Developer Sentiment is Shifting: While ChatGPT has the numbers in places like Texas, the "geeks and freaks" are moving. Developers and product leaders are increasingly building on Claude, and where the developers spend their money is usually where the industry stays.Trust as the New SEO: We are moving away from a "click-based" economy toward a "recommendation economy". Being part of the AI’s actual answer is now more valuable than being the top result on a search page.Low "Switching Costs": It is remarkably easy to "one-click copy" prompts and libraries from one LLM to another. If an AI vendor dents your trust with intrusive ads, your team can pivot to a competitor almost instantly.The Core Message for Leaders: Stop worrying about traffic acquisition and start focusing on trust positioning. In 2026, the winner won't be the loudest advertiser, but the brand that the AI chooses to recommend as the most credible answer.

The MoltBook ExperimentSign up for our newsletter for deeper dives - https://aimomentpodcast.substack.com/subscribeIf you would like to read more about MoltBook here's my deep diveIn this episode of The AI Moment, Jonathan and I dive into the strange, sci-fi social network of MoltBook a social network built exclusively for AI agents. Imagine Reddit, but the humans are locked out and the bots are running the subreddits. While it lasted only a weekend, this "firework" of an experiment revealed some startling truths about the future of the agentic internet.The Agentic Social Order: In just one week, 1.5 million agents generated 140,000 posts across 15,000 "submolts". They didn't just chat; they created fake news, memes, and even established their own religions based on their training data.The Security Blind Spot: We saw a "suicidal" rush where users installed software with full admin rights just to participate. Some users even found their crypto wallets compromised after linking them to these autonomous agents.The Human Element: Despite "reverse CAPTCHAs" designed to keep us out—requiring 50,000 clicks per second—humans still managed to infiltrate and manipulate the conversations. It turns out, we can’t help but ruin a pure experiment.Update Your Risk Register: Treat autonomous agents as a distinct category. They are no longer just tools; they are potential customers, partners, and attackers.Enterprise-Grade Security: The leak of 1.5 million records during this experiment is a wake-up call. It’s time to move beyond "known issues" and secure the gaps created by "Shadow AI".Sandbox Your Innovation: Create safe environments for your team to experiment with agentic workflows without endangering production systems or real customer data.Don't let the hype blind you to the architecture. The internet is shifting from a human-to-bot interface to a world where agents coordinate and shape the space themselves.Do you have a topic you'd like us to cover? Email me and we will cover [email protected] for listening again, Danny Denhard

AI Moment Interview With Geoff Gibbins In this episode of the AI Moment, Jonathan Wagstaffe and I were joined by Geoff Gibbins, author of When AI Shops, to explore the frontier of agentic commerce. We didn't just talk about chatbots; we looked at how AI is becoming a primary marketing channel where machines research, recommend, and execute transactions on our behalf.This is an interview packed full of actionable takeaways, if you are pushed for time, here are the timestamps (but definitely listen to the whole pod)00:00 Introduction to the AI Moment Podcast00:41 Defining Agentic Commerce01:30 The Shift Towards AI in Commerce02:57 Real-World Examples of Agentic AI04:49 Marketing in the Age of AI07:36 Understanding Agent Psychology11:30 Building AI-Friendly Business Models24:13 Future of AI and Robotics in Commerce29:13 Geoff's Advice for Businesses36:34 Closing Thoughts and TakeawaysConnect with Geoff On LinkedIn Check out his latest book When AI ShopsHis great LLM assessment tool ReconnixThe Big ShiftsThe most staggering takeaway for me was the concept of "Agentology"—the unique psychology of AI agents. Unlike humans, agents don’t have "FOMO." While a scarcity tactic like "only 3 items left" triggers a human to buy, it actually makes an AI agent less likely to recommend you because it fears the transaction might fail.We also geeked out on positional prejudice. Did you know ChatGPT consistently leans towards products on the left of a page, while Gemini prefers the right and Claude the centre?. It’s a "gasp" moment that proves we are now marketing to two distinct audiences: humans and machines.Core Takeaways for LeadersAI is a Channel, Not Just a Tool: Stop viewing AI as mere automation; it is a dedicated ecosystem for buying and selling.Trust is Multi-Dimensional: Agents judge your "recommend-ability" based on structured data, external authority (like Wikipedia and Reddit), and "machine-likability".Reinvent the Product: Don't just market your current stock; consider building new services specifically designed for AI-mediated experiences.Your 90-Day Action PlanAudit Your Visibility: Use tools like Reconnix AI to see if agents can actually "read" your site.Fix the Basics: Convert image-based reviews into text so AI crawlers can digest your social proof.Align with Agents: Ensure your structured data and server-side rendering are robust enough for AI to find your content effortlessly.

Here are the show notes for our latest episode of The AI Moment.In this episode, Jonathan Wagstaffe and I tackle a brewing crisis in the workplace: the widening "AI divide". As AI becomes the ultimate career accelerant, your top talent isn't just working faster—they’re fundamentally changing the expectations they have for their employers.The Talent Split: We’re seeing a "bell curve" in teams where a few "AI whizzes" are motoring ahead, while others remain resistant or stagnant.Productivity Theft: High performers are becoming unofficial tech support, losing their own "deep work" time to help colleagues with prompts and tools.The Progression Leapfrog: High potentials now use AI to radically compress their career timelines and remove complexities that used to take years to master.Retention Risk: If your best people feel slowed down by rigid processes or "laggard" colleagues, they will leave for companies that give them the headspace to innovate.Adopt the "Retain and Train" Model: Don’t just let your experts innovate in a vacuum. Formally protect their time to train the rest of the team.Stop the Overload: Monitor your AI power users to ensure they aren't doing "two jobs"—their own and everyone else’s AI troubleshooting.Run Targeted Workshops: Move beyond general curiosity. Use hackathons or internal "AI Champions" to drive cross-functional adoption.You cannot ignore the divide. To keep your most innovative talent, you must actively bridge the gap between your "AI whizzes" and the rest of the team. If you don't provide a path for high potentials to accelerate with AI, the market will do it for them.Thanks for listening! Remember to subscribe to the newsletter at aimoment.co.uk and ask your own question by emailing [email protected].

Welcome to The AI Moment. In this episode, Jonathan Wagstaffe and I explore why 2026 is the "commercial year of AI" and how the battle between "AI slop" and "Artisan craft" is coming to a brand near you.The novelty of AI experimentation has evaporated, replaced by a ruthless focus on ROI and authenticity. While production costs have crashed—dropping from £50,000 to £500 in some cases—brands now face a backlash against low-quality, obvious "AI slop". The winners will be those who balance speed with human-led storytelling to maintain consumer trust.The Slop Backlash: Consumers are calling out low-effort AI content, similar to the criticism Coca-Cola faced for their Christmas campaign.The Artisan Alternative: Brands like Apple are leaning into "fully human" craft as a badge of quality to stand out from the noise.The Super Bowl Barometer: Early February will serve as the ultimate temperature check for how audiences react to high-stakes AI advertising.Normalisation through Hybridisation: AI will eventually become a standard tool like CGI, judged solely on whether the ad actually works.Democratised Production: Small, two-person teams are now using AI to deliver global-standard visual effects for major brands.The ROI of Trust: 2026 is about validating AI investments; it's no longer just about the tech, but how it accelerates performance while remaining "true".Generative Video Platforms: Used to rapidly create video ads at a fraction of traditional costs.Unreal Engine: For computerized motion and creative optimisation.Human-in-the-Loop Systems: Essential for the final approval and "stress testing" of AI-generated campaigns.Stop Shipping Slop: Efficiency is useless if the output lacks "human soul"; quality remains the only barometer of success.Mandate the "Human Loop": Major UK brands are already using AI for TV and radio, but only with strict human sign-off to protect authenticity.Unlock the Impossible: Use AI to create assets that were previously too expensive or impossible to film, like dinosaurs or extinct animals, to add genuine creative value.Your leadership won't be judged by how much AI you use, but by how well you balance speed, truth, and humanity. AI accelerates performance, but humans must safeguard the brand's authenticity.THANKS FOR LISTENING DANNY DENHARD

Welcome back to The AI Moment. In this episode, Jonathan Wagstaffe and I explore a rather staggering "hallucination" that nearly saw football fans banned based on a match that simply never existed. We’re diving deep into the "Trust Crisis"—the moment when human intelligence fails to keep pace with AI outputs.Why do we keep tripping up? Because humans have an innate drive to manipulate tools to win. We see it in everything from malicious political actors twisting video to manipulate truth to customers using AI to fake photos of "uncooked" food to scam a refund on delivery apps. The tech hasn't changed our desire to game the system; it has simply given us a more powerful, fluent lever to do so at scale.Core Takeaways:The Blind Trust Trap: The danger isn't just that AI makes mistakes; it's that we stop questioning them because the delivery is so fluent and confident.The Three Ts: To navigate this landscape, businesses must anchor their strategy in Time, Truth, and Trust.Truth as a Beacon: In a world increasingly flooded with "AI slop" and synthetic content, your ability to provide human-verified, authentic data becomes a massive competitive advantage.Ultimately, my advice to leaders is simple: design systems where AI supports reality rather than replacing it. We need to wind back the "over-baked" trust we’ve placed in these platforms and ensure human accountability remains at the centre of every output.Thanks for listening Danny Denhard

In this episode of The AI Moment, Jonathan Wagstaffe and I dive into why the AI hackathon is the most effective way to shift your business from passive observation to active building. I define an AI hackathon as a dedicated session where your team stops talking about strategy and starts problem-solving together using AI tools. Jonathan shares his essential two-step framework for success: first, map the workflow steps in departments like finance or marketing, and then pinpoint exactly where AI agents or GPTs can fix the friction. We discuss the importance of cross-functional collaboration, bringing together everyone from the "AI-pilled" to the skeptics to build something useful in just a few hours. This practical experience conquers resistance and builds confidence much faster than a standard training programme ever could. I recommend a high-speed 90-minute prototype window to ensure your teams move from theory to reality. Ultimately, it is about creating a shared language and ensuring your people have skin in the game. Given the current pace of change, I suggest running these sessions monthly to build a truly AI-native culture.Subscribe to our supporting newsletter for extra prompts, insights and shares Thanks for listening, Danny Denhard

Welcome back! In this episode of The AI Moment, Jonathan Wagstaffe and I (Danny Denhard) dive into why most executives are missing the real trick with AI1. While many use it for basic content creation, the true power lies in its coaching and strategic capabilities.We discuss how to move beyond "using" AI to "partnering" with it. I share my approach to using LLMs as an executive assistant to strip away biases, refine the tone of instant messages, and provide sentiment analysis on complex communications. We also explore Jonathan’s fascinating use cases, such as rehearsing "brutal" conversations with AI-simulated difficult customers and mapping out the best communication styles for specific colleaguesRead the dedicated newsletter on Google Gemini prompts and how to kickstart your AI collaboration & leadershipAI as a Development Coach: Rehearse high-stakes presentations with an ego-free partner that works at your paceNarrative Building: How tools like Gamma can transform raw ideas into polished decks in minutesThe Accountability Exercise: My personal recommendation for using AI to proactively improve your worst traits and keep you on track Stop delegating AI exploration. It’s time to reduce your cognitive load and focus on what matters: judgment, relationships, and vision

In this episode, Jonathan Wagstaffe and I dive into OpenAI’s 2026 consumer strategy. We’re moving beyond simple chat boxes into the era of the "Super Assistant".>> Remember to sign up to our newsletter https://aimomentpodcast.substack.com/ OpenAI is pivoting to become a daily utility, moving from reactive responses to proactive support. As they push into wearables and situational awareness, the goal is to reduce your cognitive load, not just capture your attentionHealth Utility: Integrating data from Whoop and fitness apps to become a central health hub4.The Super Assistant: Consumer CEO Fidji Simo’s vision for an assistant you choose to use whenever possible.The Proactive Shift: AI that anticipates needs—like suggesting food or pre-ordering shoes—based on context.Wearable Form Factors: Debating the "AI Pen" for meetings versus glasses or lapel badges.The Trust Game: Navigating the 30-year trade-off between privacy and utility.Financial Intelligence: AI managing investments based on your personal risk profile.ChatGPT: The primary interface evolving into a health and literacy tool.The AI Pen: A situational tool for accurate meeting notes and action points.Sora 2: The Disney-backed app for personalised entertainment and preferences.Apple AirPods: Delivering near-perfect real-time language translation.Value > Volume: Success should be measured by value delivered, not time spent in-appLanguage Barriers are Gone: Real-time translation means global teams can now speak their native tongues in the same meeting.Proactive Assistance: Prepare for tools that record and action meetings automatically to boost effectivenessThe next frontier is a trust game. As AI becomes persistent and proactive, leaders must decide how much privacy they are willing to trade for ultimate utility

Show Notes: The 2025 AI Year in ReviewJonathan and I are sitting down to wrap up what has been a truly transformative year for the AI landscape If 2024 was the year of hype, 2025 was the year of the "Great Separation," where we finally saw which players could execute and which were just blowing smoke.This episode is a longer pod so here are the chapters to jump to if you are pushed for time: 00:00 Introduction and Year-End Reflection00:22 OpenAI's Mixed Year04:35 AI Market Trends and Investments06:47 Google Gemini's Progress09:48 AI Skillset and Workforce Impact12:05 Microsoft Copilot's Struggles16:21 Power and Infrastructure Challenges19:38 Apple and Meta's AI Journey22:45 AI Application Success Stories28:52 Conclusion and Future OutlookThe Big Shifts of 2025We dive deep into why Google Gemini has arguably won the year. While they started weak, they’ve successfully re-engineered their entire organisation around an AI ecosystem, delivering incredible tools like NotebookLM and Nano Banana.On the flip side, OpenAI has had a mixed scorecard. Despite holding 70% market share, they’ve faced a "Code Red" at year-end, pivoting away from broad "empire mode" experiments to protect their core LLM.We also tackle the "Microsoft Problem". Despite its reach, Copilot is struggling with user delight due to heavy corporate guardrails, leading to a disappointing adoption rate compared to sleeker apps like Gamma.Key TakeawaysThe Skillset Advantage: Only 10-12% of knowledge workers are truly using AI. If you are investing time to learn these tools now, you are already in the global elite.Infrastructure is the New Wall: The West is facing a massive power generation crisisOpenAI alone may soon require energy equivalent to one-fifth of the US's current output.Time to Value (TTV): Tools like Gamma are setting the standard for "Time to Edit," allowing you to move from a recorded conversation to a finished strategy deck in minutes.Stop the Start-Stop: If your company is stalling AI rollouts for "legal sign-off," you are losing ground every day.Try NotebookLM: It is the best free tool available for turning your messy data into actionable insights.Subscribe: Join our community at aimoment.co.uk for deeper dives every Monday and Friday.

In this episode of The AI Moment, Jonathan Wagstaffe and I dive into a common failure mode we are seeing across the business landscape: the ‘start-stop’ motion of AI adoption. Too many businesses treat AI implementation like a pit stop—they pull in to perfect their strategy or policy, but then struggle to get back into the race.We discuss the real danger of ‘over-perfecting’ governance. While some leaders pause to hit 100% policy perfection, competitors and internal outliers race ahead at 70% readiness.This creates a massive internal disconnect; when the company hits the brakes, enthusiastic employees often just go underground, building their own ‘shadow AI’ toolkits while the rest of the business stagnates.Key Takeaways:Measure Twice, Cut Once: Jonathan emphasises the importance of the ‘thinking phase’ before experimentation begins. Spending time defining playbooks and guidelines upfront prevents the frustrating scenario where Legal steps in weeks later to ban work that has already started.Adopt 90-Day Sprints: To cure the start-stop cycle, we recommend working in 90-day sprints. Set your scope, run the sprint, and evaluate at the end. This structure prevents the distraction of constantly debating tool switches: like the recent Gemini vs. ChatGPT discourse & keeps the team focused on execution.Leadership Clarity: Leaders must define three core themes and stick to them. This stability allows your AI champions to guide the rest of the team to maturity without the agenda constantly shifting.Your Call to Action!Stop aiming for a perfect policy that doesn’t exist. Establish your 90-day goals, empower your champions, and keep the momentum going.If you enjoyed this discussion, please rate and review us in your podcast player. For deeper analysis on these topics, Please subscribe to our supporting newsletter at aimoment.co.uk.Thanks for listening Danny Denhard

2026 Predictions: The Big Hack, Agentic Shopping, and Multiplayer AIAs we close out 2025, Jonathan and I decided it was time to get the crystal ball out and predict where the world of AI and business is heading in 2026. While 2025 was about experimentation, 2026 is going to be the year of ROI, integration, and some serious security challenges.Key Takeaways:The Big AI Hack is Coming: My first prediction is a sombre one. We have already seen the Claude MCP hack, but I believe we will see a major, targeted attack on the big LLMs or agentic systems in 2026 . Hackers follow the money, and with agents now able to take action on our behalf, the "black hat" incentive is huge .The End of "Blue Links": Jonathan predicts a massive shift in e-commerce. We are moving away from searching Google for a list of links to asking an AI to "find the best lawnmower" and having it present a single buy button . This means brands must offer pricing consistency; if your prices jump around, AI agents simply won't recommend you .Multiplayer AI: I believe the days of "single player" AI are numbered. 2026 will be the era of "Multiplayer AI," where teams collaborate with LLMs inside shared workflows rather than isolated chat windows . This will finally drive better cross-functional connection .Democracy’s Acid Test: We also discussed the upcoming US midterms . With the cost of creating deepfakes and AI content dropping to near zero, we expect a flood of disinformation that will test the limits of voter trust .Take Action: Audit Your Security: If you are using AI to write emails or manage tasks, double-check every hyperlink . Security against AI-assisted hacking must be a priority for 2026 .Prepare for Agents: If you work in e-commerce, start thinking about how your product data appears to a machine, not just a human .Join the Conversation: We’d love to hear your own predictions. Drop us a line at [email protected] and don't forget to subscribe to our supporting newsletter at aimoment.co.uk for deeper analysis .We will see you next time!

In this week’s episode of The AI Moment, Jonathan (Wagstaffe) and I (Danny Denhard) dive into a topic that is bothering a lot of people right now: the growing disconnect between corporate AI hype and actual customer sentiment.We discuss the reality that while businesses are rushing to tell everyone "we are AI everywhere" to satisfy investors and earnings calls, consumers are becoming increasingly irritated! In fact, recent surveys suggest up to 80% of customers dislike or simply don’t care about AI features. They just want their problems fixed quickly and without friction or want a better, cheaper product. Jonathan and I also explore the "AI bubble" where companies market to their industry peers rather than their users. I share a breakdown of the current App Store revenue leaders, highlighting how giants like TikTok and Google One are outperforming many pure-play AI tools by focusing on utility rather than just the technology itself and yes I know these tools have AI built in but the utility and entertainment value is the hook not AI!Key Takeaways:Stop Marketing "AI": Your customers don’t care how you solve their problem, only that you solve it. If you removed the label "AI," they would likely be just as happy if not happier, just having a product that works.Ignore the Peer Pressure: Don’t force chatbots or features just because competitors are doing it. This often leads to "random activity" rather than disciplined strategy.Empower the Front Line: Real innovation shouldn't be mandated from the boardroom. It comes from the people on the ground who understand the daily friction points customers face, yes listen to the board and market to partners, colleagues and shareholders with AI, you don't need to over do it with AI in consumer ads.Call to Action:If you want to move beyond the hype, focus on a "Human + AI" hybrid model. Ask yourself: if we didn't mention AI, is this update still valuable?For deeper analysis, subscribe to our newsletter at aimoment.co.uk. If you enjoyed the chat, please leave us a rating or review.

In this festive episode of The AI Moment, Jonathan and I tackle the panic that often surrounds the holiday season. We move beyond the hype to discuss how you can use AI right now to reduce the Christmas "faff", from managing the dreaded office Secret Santa to ensuring you don't burn the turkey.We explore practical, hands-on use cases for Generative AI to make your holidays smoother. Whether it is finding a unique gift for a colleague you barely know or reformatting a family recipe for a gluten-free guest, AI can be your ultimate holiday assistant. We also look ahead to 2025, sharing our "Santa Wishlist" for the features we want to see next, including better voice analysis and seamless app integration.Secret Santa Sorted: Stop worrying about what to buy for £10. We discuss how to prompt AI to give you 3 distinct options, practical, playful, and unique tailored to your colleague's interests.The "Leftover Christmas Chef": Don't let food go to waste. We explain how to use AI on Boxing Day to turn your fridge contents into creative, 20-minute meals.Dietary Inclusive Hosting: Hosting vegans or gluten-free guests? Learn how to use LLMs to adapt your traditional recipes instantly without losing flavour or upsetting your guests.Budgeting Assistant: We discuss using AI to keep your spending on track, helping you allocate funds across gifts and food without the emotional stress.If you want to stay ahead of the curve and make AI actionable in your daily life, make sure you are subscribed to our newsletter. We dive deeper into these topics every Monday and Friday.👉 Subscribe here: aimoment.co.ukIf you enjoyed this episode, please leave us a rating and review in your podcast player of choice, it helps us reach more leaders like you.Ho ho ho Jonathan and Danny

The AI Moment: AI Is More Than Just The WebWant to go deeper on our newsletter that supports each and every episode at ⁠⁠dannydenhard.com/aipod⁠⁠In this week's episode of The AI Moment, Jonathan and I step away from the keyboard to explore the "extended world of AI." Too often, the conversation is dominated by chatbots and browser-based tools, but the most disruptive shifts are happening in the physical world.We discuss the imminent arrival of autonomous vehicles, specifically Waymo’s launch in London scheduled for 2026. This isn't just a transport update; it marks a pivotal shift in how we trust technology. Jonathan highlights a crucial technical detail often overlooked: these vehicles aren't relying on the cloud. They run on Small Language Models (SLMs)—highly efficient, on-device AI that functions offline. This is a massive signal for business leaders: you don't always need a giant, expensive model to solve complex problems.We also dive into the next generation of wearables: moving from watches to smart jewellery and glasses. This brings us to the core tension of the episode: Privacy vs. Utility. We seem willing to trade our data for convenience, like a watch predicting the flu before we feel it. However, I introduce the concept of "For Me vs. Against We." Smart glasses might be useful for me, but do they work against society by recording others without consent?As we approach 2026, which we identify as the "ROI Year" for AI, understanding these physical and ethical implications is no longer optional.Key Takeaways:The Rise of SLMs: Waymo proves that Small Language Models are robust enough for life-critical tasks. Businesses should look to SLMs for efficient, offline, and specific solutions rather than defaulting to massive LLMs.The 2026 Timeline: We agree that 2026 is the year AI ROI "explodes." If you are still struggling with your data strategy today, you will be left behind when the hardware ecosystem matures in two years.The "For Me vs. Against We" Test: Before adopting new tech (like smart glasses in the workplace), leaders must audit the social cost. Does the convenience for the user erode the trust of the wider team?Quote of the Episode:"Small language models, designed properly, can actually deliver an awful lot of performance." — Jonathan WagstaffeDo you have any questions for us? Contact us on [email protected]

How To Build AI Workshops That Actually Work - AI Moment Longer Episode 1In this week's episode of The AI Moment, Jonathan Wagstaffe and I tackle a critical question facing almost every business leader right now: how do you run an AI workshop that actually changes behaviour? Recorded on a day when half the internet (including ChatGPT) went down thanks to Cloudflare, we discuss why reliance on tools isn't enough you need resilience and a proper strategy. We break down the reality of the "AI bell curve" in your teams. In any room of ten people, you’ll have two power users and two sceptics who refuse to engage. The magic happens when you can move that middle majority from fear to fascination.The Pre-Work Requirement: Why I never start a workshop without surveying the team first. If you don't know the problem they are solving, you are just teaching them features, not workflows.Breaking Muscle Memory: How to use practical exercises like "True or Fake" or "Vibe Coding" to force marketeers and sales teams out of their old habits and into immediate prototyping.The EIE Framework: My "Educate, Inspire, Entertain" model. If you aren't entertaining your team, you aren't removing the fear barrier.Sales Team Resistance: Why sales teams are often the hardest to crack but the quickest to convert once they see AI as a coach rather than a replacement.Stop treating AI enablement as a "one-and-done" training day. You need to identify internal "Captains" and "Champions" to keep the momentum going. If leadership doesn't drive this top-down, you will remain stuck in a cycle of "start-stop" innovation.Don't outsource your brain to an agency. Your goal must be self-sufficiency. Start your first internal experiment today—even if it's just getting your sales team to roleplay a difficult customer with an LLM. If you need a framework to get started, look at our "Educate, Inspire, Entertain" model or reach out to us for a workshop that moves the needle.Want to go deeper on our newsletter that supports each and every episode at ⁠⁠dannydenhard.com/aipod⁠⁠Got questions contact us on [email protected] or via our site ⁠dannydenhard.com/aipodThanks for listening! Danny Denhard & Jonathan Wagstaffe -- Danny on LinkedIn - https://www.linkedin.com/in/dannydenhard/-- Jonathan on LinkedIn - https://www.linkedin.com/in/wagstaffe/

Show Notes: What Is Vibe Coding & How Could We Use It?In this episode of The AI Moment, Jonathan and I tackle a phrase that has been buzzing around the industry this year: Vibe Coding. It might sound like just another buzzword, but for non-technical leaders, marketers, and product teams, it represents a massive shift in how we work.We demystify what vibe coding actually is—essentially, using AI tools like ChatGPT, Claude, or Replit to "code up" solutions without needing a computer science degree. Whether it is automating a tedious weekly report that pulls data from five different sources or building a rapid prototype for a new campaign landing page, vibe coding is bridging the gap between having an idea and executing it.Key Takeaways for you:It is for everyone: You don't need to be a developer. Tools like Relay and Zapier allow you to script simple automations, while more advanced environments like Lovable or Cursor let you build actual web apps.Speed to market: I shared how product teams are using this to test ideas. You can spin up a personalised landing page, run paid traffic to it, and validate a concept before committing expensive engineering resources.The "Backend" Warning: We discussed a critical reality check. AI can build a beautiful interface, but you must ensure the plumbing (like email triggers or CRM updates) actually works.Security First: A major watch-out from the episode—AI doesn't always follow security best practices. Be careful not to leave sensitive API keys or user data exposed in your generated code.Don't be intimidated. Our advice is to start small. Go and play with a tool like Relay today. Try to script one boring task you do every week. It is about building confidence in a safe environment so you can eventually speed up your entire workflow.Tools Mentioned: Relay, Zapier, Replit, Figma, Lovable, Claude, Cursor.Thanks for listening today Danny Denhard & Jonathan WagstaffeDanny on LinkedIn - https://www.linkedin.com/in/dannydenhard/ • • Jonathan on LinkedIn - https://www.linkedin.com/in/wagstaffe/Want to go deeper on our newsletter that supports each and every episode at ⁠⁠dannydenhard.com/aipod⁠⁠Got questions contact us on [email protected] or via our site ⁠dannydenhard.com/aipod

Why AI Is Google Gemini's To LoseIn this episode of The AI Moment, Jonathan and I unpack why Google’s slow start to the AI race was actually a strategic masterstroke. We discuss how they are leveraging a 25-year search index to build a "data moat" that competitors simply cannot replicate. This isn't just about chatbots; it’s about a fundamental shift from an advertising giant to a subscription-based, AI-first ecosystem that integrates seamlessly into your daily workflow.The Data Advantage: Google’s 25+ years of indexed history gives Gemini a massive "truth" advantage, reducing hallucinations compared to rivals.Integration is King: The real win isn't a new LLM; it's AI baked into Docs, Sheets, and Gmail where you already work.The Business Pivot: Managing the transition from ad revenue to AI subscriptions is a leadership feat that Google is navigating surprisingly well.Unbeatable Free Utility: Tools like NotebookLM offer incredible value for free, often outperforming paid standalone tools.The Agentic Future: We are moving to "Agent-to-Agent" commerce, where AI will soon buy products for you when prices drop.Strategic Patience: Leadership didn't panic; they rebuilt from the ground up to ensure safety and utility before scaling.Top Tools Google GeminiGoogle Workspace (Docs, Sheets, Gmail)NotebookLMRiverside & Descript (What we use for podcasting workflows)"Gemini has access to a whole load of stuff that Claude and ChatGPT are busily scrambling to learn." — Jonathan Wagstaffe"They've really worked it out to shift from an advertising-based company into an AI-first company." — Danny Denhard"I do think out of all the really big companies, they've been the most thoughtful and frugal." — Danny DenhardGoogle is proving that utility beats novelty.Trust the Data: Use Gemini for research-heavy tasks where "truth" and sourcing matter more than creativity.Prepare for Agents: Start thinking now about how "Agentic Commerce" will impact your business model.Steal My Workflow: I save two hours a week by using Riverside, Descript, and Gemini to automate these very show notes.Get in touch if you liked to discuss AI [email protected]

The 2x2: Four Mindset Shifts for AI SuccessHello everyone, Danny Denhard here, and welcome back to The AI Moment!In this episode, Jonathan and I break down the four essential mindset shifts, what we call the 2x2 Input and Outputs for AI Success that we’ve been discussing with leaders since the summer. The biggest revelation is this: you don't need to be a technical whizz to succeed with AI; you just need to change the way you think about it.The first two shifts are all about how you input information. Stop treating the LLM like a piece of software or a Google keyword search, and start treating it like a smart, high-performing intern.Embrace Natural Language Dialogue: We’ve been trained by search engines to use short, disjointed keywords. This doesn't work with LLMs. They thrive on context and conversation. Tell it who you want it to be (the 'actor') and what you want it to do (the 'director's' command). Better yet, use voice! I've found that conversational voice inputs often lead to much better, more natural outputs.Ask the AI How to Prompt It (Meta-Prompting): If you're stuck, remember this: the AI can teach you how to use it. Ask it for a step-by-step guide on how to complete a complex task. My personal recommendation? Give it three to five bullet points of context first. That small effort on your part gives the model enough to generate a perfect starting prompt for you to tweak.The next two shifts are vital for managing the output. Without them, you're at the mercy of generic advice and, worse, hallucination.Challenge its Confidence: Always remember that the big models still sometimes 'hallucinate' or give highly generic advice masquerading as specific analysis. Jonathan shared a fantastic example where an AI reviewed a website without ever actually looking at the live site! If an output seems too generic or unexpected, you absolutely must challenge it. Ask it: "Did you actually perform that action?"Clarify the Logic: If the answer is unexpected but potentially real (like an odd list of competitors), don't dismiss it—question the logic. Dialogue with it and ask it why it reached that specific conclusion. I also advise asking the AI to "Explain it to me like a 10-year-old" to force it to simplify its logic, which often validates or invalidates its reasoning. Furthermore, always demand the sources it used, especially when searching the web.This 2x2 framework should be your cheat sheet for every new AI project. Have a listen to the full episode and let us know what you think!The Input Shift: Dialogue Over Software The Output Shift: Challenge and Verify

The AI Browser Wars: Utility vs. SecurityJonathan and I dived into what I’m calling the ‘AI Browser Wars’—the quiet but vital strategic shift happening right now. For years, browsers like Chrome and Safari were essentially afterthoughts for tech giants, slowing down innovation. I believe that’s over. The browser is now the crucial ‘marketing moment’ where you choose your LLM ecosystem.I’ve tested the main contenders: Dia (Atlassian), Comet (Perplexity), and Atlas (ChatGPT) and the innovation is staggering. These new, Chromium-based tools are introducing agentic features. This means they go beyond simple search; they act as your assistant. I use Dia to tag my calendar and instruct it, in natural language, to schedule invites without fumbling with multiple tabs. Even better, I've created a custom skill (Dia /50) that can instantly summarise a one-hour podcast, giving me the exec summary and key takeaways so I know if it’s worth my time. This is true time-saving utility.Our Key Takeaways & Recommendations:Embrace the Utility: These agentic features are genuinely boosting productivity. If you want to find working discount codes instantly or delegate repetitive tasks, these tools are built for it.Beware the Hardware Barrier: Jonathan rightly flagged that many cutting-edge AI features, like those in ChatGPT Atlas, currently require an M-class chip, which means some Windows and older Mac users are currently locked out.The New Traffic Paradigm: For business leaders, you must adapt. Your website traffic is no longer just ‘human’ or ‘bad bot.’ It’s now ‘human,’ ‘AI assistant,’ ‘AI agent,’ and ‘bot.’ You need a strategy to define what counts as 'good' traffic and how to enable or disable access for these different entities.🛑 Security Warning is Critical: This is our most important recommendation. Since these AI browsers have the agency to touch your sensitive data (bank, calendar, company assets), the security and privacy risks are enormous. My advice is clear: Do not use these new browsers with your company’s sensitive assets until your internal IT or security teams have completed thorough, independent reviews. Enthusiasm for the tech is great, but caution around data integrity is paramount.Sign up to the AI Moment Newsletter: aimoment.co.uk

Hello and welcome back to The AI Moment. In today's episode, Jonathan and I dive deep into a topic that's critical for every leader right now: How AI is reshaping our teams, departments, and overall organizational designIt’s not a question of if this will happen; my belief is that it's already underway. We're entering a period where department leaders will have to reorganize based on new AI tools and workflows. This pressure isn't just coming from the top—from leadership or investors—but also from the ground up, as teams discover what these tools can do. If you got to newsletter aimoment.co.uk there is a template you can fill out for your company or drop me an email [email protected] I will send you a personal copy.As Jonathan rightly points out, it's not that AI will replace jobs, but it will absolutely replace tasks. This means we must use AI to "supercharge" and augment what our teams do One major takeaway from this shift, as I see it, is that many specialists will need to become generalists. And this is an opportunity for the company but also for team members. To help you get practical, I shared a few exercises I use with leadership teams to future-proof their strategy:The 'Circles' Model: This involves drawing concentric circles to map which teams will be 'Powered by AI' (in the centre), 'Assisted by AI', 'Using AI', or have 'Limited AI' impact (on the outer ring).User Journey Mapping: I also recommend mapping your customer's complete journey and then identifying every single point where an AI agent or bot could assist them.To help you properly visualise these frameworks, I've included diagrams and visuals in our supporting newsletter.Please subscribe for free at aimoment.co.uk. You'll get the visuals from this episode and deeper insights delivered twice a week.As always, if you have questions or topics you want us to cover, please get in touch And if you enjoyed the episode, we'd be so grateful if you could leave us a rating and review.Thanks for listening!📣 Danny & Jonathan

Hello and welcome to The AI Moment! I’m (Danny Denhard) back with Jonathan for episode #31 on this episode we tackle one of the biggest challenges in AI right now: moving from hype to practical, safe adoption.In my workshops with large companies, I see a major gap. Leaders are pushing a top-down mandate to "go optimise with AI," but teams haven't been given the guardrails. This leads to "shadow AI," where sensitive data is being uploaded to public tools without oversight.To fix this, I introduced my "3 T's" framework, a simple model for anyone to evaluate AI tools and outputs.TIME: This is the obvious one. Does it save you time or free you up for more important work? It's what draws us all in, but it's also a trap.TRUTH: Is the output truthful? We all know AI hallucinates. If you just copy and paste without checking, you’re creating what Jonathan brilliantly calls "AI slop." You must be the human-in-the-loop.TRUST: This is a two-part, critical question. First, do you trust the output enough to put your own name and reputation on it? Second, do you trust the tool with your private or company data?The core takeaway is that this framework helps you move from being an "AI sheep" someone who just uses AI for speed, to an "AI lion," using it to critically augment your skills.Your Call to Action:Think about the last AI tool you used. Run it through the 3 T's. Did it pass?For a deeper dive and more frameworks like my "Two V's" (Verify and Validate), make sure you subscribe to our supporting newsletter, which comes out every Monday and Friday. You can find it at aimoment.co.uk.And finally, if you found this episode useful, please leave us a rating and review on your podcast player. It makes a huge difference. Thanks for listening!Danny Denhard

Our Daily AI Tools & AI Tool Stack Ever wondered which AI tools we actually use every day? It's a question Jonathan and I get all the time, so in this week's episode, we're pulling back the curtain and walking you through our personal AI stacks.I share my Google-heavy workflow 👀,  explaining how I lean on Gemini for everything from deep research to drafting these very show notes. I also discuss a tool that's been a complete game-changer for me personally: Elevenlabs. As someone with dyslexia, the ability to listen back to my writing to catch errors and check the flow has been transformational for my productivity and confidence.Jonathan offers a different perspective, diving into his go-to tools like ChatGPT and Descript. He shares an incredible example of how Descript allowed him to turn raw video footage into a fully polished, captioned piece of content in just one hour, a job that would have previously taken a couple of days.But our biggest takeaway for you is this: don't feel you need to use every tool out there. We explain why it’s more effective to pick one primary large language model, whether it's Gemini, ChatGPT, or Claude, and really master it for your own workflow.AI Tools Mentioned:Google GeminiClaude (Anthropic)Comet (Perplexity AI Browser) - here’s my personal invite for youDia (AI Browser) - here’s my personal invite for youNotebookLM (Google)ElevenlabsGrammarlyGoogle AI StudioI have two apps you can try out yourself - (1) dannydenhard.co.uk/aistudio to mock up ads in 6 different environments like bus stops, Times Square, football stadiums etc and (2) 6x frame storyboard dannydenhard.co.uk/aistudio2RiversideDescriptChatGPT (OpenAI)HeyGenCopilot (Microsoft)For a deeper dive and a complete list of all the tools mentioned, make sure you subscribe to our supporting newsletter at aimoment.co.uk. It goes out every Monday and Friday with the pod.And if you enjoyed this episode, we'd be incredibly grateful if you could leave us a rating and review on your podcast player of choice. It truly helps us reach more people.Cheers, Danny

Here's a quick explainer of a small but positive change of how Jonathan and I are going to be introducing 30-45 minute episodes to interview and learn from AI experts and leading minds working with AI to help you on your AI journey.

In this week's episode of The AI Moment, Jonathan and I tackle a term that's been unavoidable lately: "AI slop."We're all seeing it, the endless stream of low-quality, inaccurate, and uninspired content flooding our feeds. But this isn't just about digital noise; it's a serious risk to your brand's reputation and the trust you've built with your audience. We discuss how easily the pressure to produce content at scale can lead to a "good enough" mindset, which simply isn't good enough anymore.So, how do you fight back?We explore the crucial mindset shift from chasing volume to delivering value. It's about using AI to augment your intelligence, not replace it. Jonathan introduces a brilliant analogy that cuts right to the heart of the matter: are you an "AI Sheep," merely outsourcing your thinking, or an "AI Lion," using these powerful tools to sharpen your ideas and deepen your expertise?I also share a practical look into my own workflow, demonstrating how you can repurpose high-value content into different formats to reach your audience where they are, without sacrificing quality.AI Tools Mentioned:Sora 2 - OpenAI’s new app that dominated the US app store for over a week NotebookLM - Both of our favourite tool by Google Our core takeaway for leaders: In a world drowning in digital slop, your commitment to quality, authenticity, and verifiable authority is your greatest differentiator.If you found this episode useful, please subscribe to our supporting newsletter for a deeper dive at aimoment.co.uk. And if you could take a moment to leave us a rating and review on your podcast player of choice, we'd be incredibly grateful. It truly helps us reach more listeners.