Beyond The Prompt - How to use AI in your company

Leidy Klotz has spent years studying a simple but overlooked phenomenon: when we try to improve something, our first instinct is to add rather than remove. He shares the Lego bridge experiment that sparked his research and explains how this additive bias scales from small design decisions to entire organizations. Over time, companies accumulate reporting lines, meetings, software, and policies without questioning what no longer serves them. Henrik and Jeremy explore how AI tools intensify this pattern. When generating ideas, launching projects, writing code, or producing content becomes effortless, the temptation to add grows stronger. The cost of producing information drops, but the cost of consuming it rises. Without guardrails, organizations risk what Leidy calls “organizational indigestion.” The discussion moves from insight to implementation. Leidy outlines practical ways to counteract additive bias, including stop-doing lists, default kill dates on projects, and designing environments that make subtraction visible and acceptable. In a world of accelerating AI output, leaders must intentionally decide what to remove, what to protect, and what truly matters. Key Takeaways: We default to adding, not subtracting When faced with a problem, our instinct is to introduce something new. Subtraction rarely occurs to us, even when removing something would improve clarity and performance. Generative AI amplifies additive bias AI makes producing content, code, and ideas easier than ever. Without constraints, this frictionless creation can accelerate complexity instead of progress. More organizations die from indigestion than starvation Over time, companies accumulate tools, processes, and policies that quietly slow them down. The real risk is often not too few ideas, but too many unexamined additions. Architecture beats willpower Rather than relying on discipline alone, leaders can design systems that encourage subtraction. Stop-doing lists and default expiration dates make removal expected instead of exceptional. Protect what matters before adding more Before introducing new tools, workflows, or AI systems, leaders must define what is already working and worth protecting. Subtraction requires clarity about what should stay, not just what should go. Subtract: amazon/Subtract-Untapped-Science-Leidy-Klotz In a Good Place: amazon/Good-Place-Spaces-Where-Thrive/ Leidy's Speaking: https://leidyklotz.com/ Clip from Bear: Subtract - this is how you do better 00:00 Intro: Our Instinct to Add 00:28 Meet Leidy Klotz 01:15 The Subtract Idea 02:56 Organizations Get Bloated 03:49 Scandinavian Design Mindset 04:32 New Book: In a Good Place 05:59 AI Abundance and Indigestion 08:12 Curate Context, Not More 11:38 Cues and Stop-Doing Lists 15:00 Default Debt and Kill Dates 17:10 Odysseus Contracts and Biases 21:28 Reengage the Physical World 29:17 Bike Shedding and Priorities 36:10 Making Is Thinking 49:16 The Debrief 📜 Read the transcript for this episode: how-to-subtract-the-most-underrated-skill-of-the-ai-era-with-leidy-klotz/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Fathom was built on the assumption that transcription would become commoditized and generative models would steadily improve. Rather than training proprietary models, Richard focused on building the infrastructure around them and waiting for model capabilities to reach the right threshold. In this conversation, he explains why AI has made effort and impact harder to predict, and why that shifts product development from roadmap execution toward experimentation. He describes separating an exploratory AI team from core engineering, structuring that team to prototype and write specs, and expecting a meaningful portion of experiments not to work. Richard introduces his Jenga model for AI development, testing different models and use cases to find where resistance is lowest. He also discusses the operational realities of rapid model updates, hallucination rates, and what he calls the LLM treadmill. The discussion explores qualitative QA, organizational design, buy versus build decisions, and why leadership taste plays an increasingly important role as AI lowers the barrier to generating outputs. Key takeaways: Estimating effort and impact is becoming harder As model capabilities improve quickly, features that require months today may take far less time in the near future. This makes traditional planning assumptions less stable. Product development increasingly resembles R&D With shifting capabilities and uncertain outcomes, teams must experiment, prototype, and iterate rather than rely solely on long term roadmaps. Organizational structure must reflect experimentation Separating exploratory AI work from core engineering can allow faster iteration while maintaining stability elsewhere. Rapid model updates create operational pressure Frequent improvements and changing performance levels can require teams to revisit and adjust features more often than in traditional software cycles. Qualitative judgment plays a larger role As AI lowers the cost of generating outputs, evaluating quality and deciding what to ship becomes increasingly important. Fathom: fathom.ai Fathom LinkedIn: linkedin/company/fathom-video/ Richard's LinkedIn: linkedin/in/rrwhite/ 00:00 Intro: Why AI Breaks Roadmaps 00:19 Meet Richard White (Fathom AI) 02:16 From Roadmaps to R&D 04:49 Designing AI Teams for Speed 07:11 The Jenga Model 09:56 Failing 50% & AI Team Psychology 13:40 LLMs as Interns & Anti-Planning 21:01 QA, Data Pain & Developing Taste 24:59 Executive Taste & Culture Rules 27:20 Reacting to AI Waves 28:50 Fathom’s 4-Step Product Plan 30:47 What New Models Unlock 32:13 From Scribe to Second Brain 40:32 Build vs Buy in AI 45:32 The Debrief 📜 Read the transcript for this episode: from-roadmaps-to-rd-how-ai-is-changing-product-development-with-richard-white-founder-of-fathom-ai/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Bryan McCann joins Henrik and Jeremy to explore how search is evolving from simple queries into more conversational and agent-driven systems, and why prompting is likely a temporary skill. Bryan shares how his definition of productivity changed as an AI researcher, moving away from doing the work himself and toward designing plans and experiments that machines could run continuously. The conversation expands to leadership and organizational design. Bryan explains why helping others learn how to work with AI became his highest-leverage activity, and offers a simple rule of thumb: try to get AI to do the task first, and treat anything it can’t do as an interesting research problem. Henrik and Jeremy connect this to Bryan’s view that organizations may increasingly resemble neural networks, with information flowing more freely and decisions less tied to rigid hierarchies. Key Takeaways: Productivity can be measured by machine output, not human effort Bryan explains how “keeping the GPUs full” became his primary measure of productivity. Prompting is useful, but likely temporary The episode discusses why future systems may rely less on explicit prompts and more on inferred context. Try AI first, then learn from what it can’t do Tasks AI struggles with can reveal meaningful research opportunities. Leadership is about scaling others Bryan shares how his focus shifted from scaling himself to helping his team increase impact. Organizations may benefit from neural-network-like design Better information flow and fewer bottlenecks can improve decision-making. YOU: You.com Bryan's website: bryanmccann.org LinkedIn: linkedin/company/youdotcom/ 00:00 Intro: Keeping the GPUs Full 00:22 Meet Bryan McCann: CTO & co-founder of You.com 00:43 Why Search Is Breaking - and Why It Becomes a Skill 01:41 From Search to Agents 03:18 The Case for Proactive, Context-Aware AI 04:30 We Don’t Need New Hardware - We Need Trust 05:43 The Trust Problem of Always-On Listening 07:57 Trust as the Real Bottleneck (Not AI Capability) 09:52 Delivering Immediate Value to Earn Trust 12:13 Business Models and Escaping the Attention Economy 17:27 What “Agents” Really Mean - and Why the Term Will Fade 20:37 Productivity, Parkinson’s Law, and Keeping the Machines Running 23:52 Scaling Yourself vs. Scaling Your Team 29:57 Building Culture: Automate, Throw Away, Rebuild 35:46 Designing Organizations Like Neural Networks 45:02 Recruiting for Initiative in an AI-Native Organization 49:18 The debrief 📜 Read the transcript for this episode: podcast.beyondtheprompt.ai/heres-how-to-know-if-youre-getting-the-most-out-of-ai-with-bryan-mccann-cto-of-youcom/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Humza Teherany breaks down how he bridges deep technical fluency with strategic leadership at MLSE, home to the Raptors, Maple Leafs, and more. He shares how a vacation turned into an AI reawakening and how that hands-on immersion led to a fundamental shift in how his organization builds and experiments. Humza walks through MLSE’s build in a day practice, their internal AI platform, and why speed to prototype now unlocks more than just efficiency. It changes who gets to shape the future. He, Jeremy, and Henrik explore the limits of traditional enterprise AI rollouts and how to build spaces for superusers that enable company-wide transformation. The conversation covers how technical literacy impacts credibility, why idea execution is the new differentiator, and how Humza’s five-year-old inspired a bedtime story app powered by AI. Whether you're a CTO, a founder, or just figuring out where to start, Humza makes a compelling case. The best leaders don’t delegate this moment. They build. Key Takeaways Leaders should not delegate the AI moment Humza, Henrik, and Jeremy agree that this is a moment for leaders to be hands-on. The ones who build and explore the tools themselves are the ones unlocking real impact. Technical fluency builds credibility and better decisions Humza’s return to his technical roots has changed how he leads. Understanding how AI works helps leaders earn trust and make smarter, faster choices. Speed enables inclusion MLSE’s build in a day model allows more people to contribute ideas and see them turned into real prototypes. Moving fast isn’t just efficient - it changes who gets to participate. Empower your superusers first Rather than starting with enterprise-wide training, Humza focuses on enabling the small group already eager to build. That early energy helps drive broader culture change. MLSE: mlse.com LinkedIn: Humza Teherany - LinkedIn 00:00 Intro: Humza Teherany and MLSE 00:27 The Role of C-Suite Leaders in AI 01:08 Reconnecting with Technical Skills 02:08 Diving Deep into AI Tools 03:03 The Importance of Hands-On Learning 04:25 Progression from Consumer to Technical AI Tools 07:28 Building a Business Case for AI 10:03 Creating a Culture of Innovation 14:00 Implementing AI in Business Operations 21:05 Challenges and Strategies in AI Adoption 26:17 Organizational Structure for AI Success 32:02 The Importance of Reviewing and Planning Code 33:01 The Future of Solo Developers and New Technologists 34:58 Reimagining Company Structures with AI 38:55 Key Skills for Future Technology Leaders 41:19 Personal AI Experiments and Innovations 46:52 Encouraging Creativity in Children with AI 49:11 The Debrief 📜 Read the transcript for this episode: building-an-enterprise-ai-innovation-lab-a-master-class-with-humza-teherany-chief-strategy-officer-of-maple-leaf-sports-and-entertainment/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Mikkel B. Rasmussen brings a rare lens to the AI conversation. As an applied anthropologist, he has spent decades helping companies like LEGO uncover what is really going on beneath the surface. In this episode, he shares how deep insight often begins with being wrong, why surprise is the clearest sign you have found something meaningful, and how the pain of not knowing is essential to breakthrough thinking. He also explains how AI is transforming his own research, from pattern recognition to video ethnography, and introduces a provocative idea: Anthropology Without Anthropologists. Jeremy and Henrik reflect on what it means to teach AI how to surprise us, how synthetic data might reshape experimentation, and why better insights begin with better questions. Key Takeaways Insight starts with being wrong Mikkel defines insight as the gap between how we think the world works and how it actually is. Anthropology helps uncover these mismatches, and that is where real breakthroughs begin. Pain is part of the process Mikkel and Jeremy both reflect on the emotional struggle that precedes insight. The doubt, sleepless nights, and questioning whether the work will ever come together is not failure. It is a necessary stage of discovery. Surprise is a signal The moment of surprise, when a new pattern emerges or an assumption is shattered, is at the core of applied anthropology. For Mikkel, it is the clearest sign that you have found something real. AI can accelerate experimentation Mikkel shares how AI is already helping his team analyze patterns, run faster experiments, and even conduct interviews that outperform humans in some cases. The goal is not to replace people but to push the limits of what is possible. HARL: humanactivitylab.com 00:00 Intro: Why This Conversation Matters 00:25 Meet Mikkel: Founder of Human Activity Laboratory 01:14 Understanding Anthropology and AI 03:32 Applied Anthropology: Tools and Techniques 04:56 The Role of Narratives in AI 07:06 The Importance of Sensory and Social Dimensions 13:06 Case Study: LEGO and the Anthropology of Play 21:07 The Role of Surprise in Anthropology 27:51 AI and Human Synergy 31:26 Exploring AI's Limitations and Potential 32:46 Anthropology Without Anthropologists 34:17 AI's Role in Generating Insights 37:23 Human Bias in AI-Generated Ideas 42:05 Synthetic Data and Its Applications 47:34 The Future of AI in Anthropology 49:25 The Debrief 📜 Read the transcript for this episode: why-ai-gets-people-wrong-the-real-source-of-insight-with-anthropologist-mikkel-b-rasmussen/transcript For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Diarra Bousso returns to Beyond the Prompt to share how she's reprogramming the fashion industry using AI, math, and a relentless spirit of experimentation. From selling AI-generated products before they exist to cutting out waste and wait times, she walks us through a radical new approach to design and operations. She explains how her team uses scientific rigor to test marketing ideas, create on-demand collections, and rethink the traditional fashion calendar. Diarra also opens up about the origin of her experimental mindset, which began during a year of recovery after a life-changing accident, and how that philosophy now shapes her leadership. The episode wraps with reflections on sustainability, mental health, and what it means to build a joyful, human-first company in the age of AI. Diarra shares how she’s using AI not just to scale her business, but to reclaim her time, and why her next venture might bring these tools to creators everywhere. Key Takeaways Experimentation is the foundation Diarra treats her entire business as a lab. Every idea is a test, and her team is trained to think in hypotheses, measure results, and adapt quickly. AI enhances human creativity She sees AI as a creative partner, not a replacement. It helps her move faster, make smarter decisions, and focus on the parts of design that require real taste and vision. Sell before you build By testing AI-generated designs with customers before making anything, Diarra unlocks cash flow, cuts waste, and sidesteps the long timelines of traditional fashion. Sustainability starts with the founder Diarra applies the same mindset to her own life. She’s using AI to reclaim time, reduce burnout, and build a business that supports health as well as growth. Website: diarrabousso.com DIARRABLU: diarrablu.com 00:00 Intro: AI-Driven Fashion 00:13 Meet Diarra Bousso: Founder of DIARRABLU 01:43 The Power of Experimentation 02:00 A Life-Changing Accident and Recovery 04:40 Embracing a Culture of Experimentation 06:13 Scientific Approach to Business 09:48 Empowering the Team 15:03 AI in Fashion Design 18:36 Revolutionizing the Fashion Industry 28:09 Traditional vs. Digital Fashion Models 32:18 Embracing AI in Fashion Design 32:49 Collaborating with Retailers Using AI 35:06 AI's Role in Prototyping and Design 36:58 The Future of AI in Creative Industries 39:14 Navigating Resistance to AI 48:10 Operationalizing AI for Efficiency 52:18 Balancing Innovation and Personal Well-being 57:19 Debrief 📜 Read the transcript for this episode: Transcript of How The Worlds Leading AI-first Fashion House Flips The Cash Flow Equation with Diarra Bousso For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Illia Polosukhin joins Henrik and Jeremy to trace the origins of transformers and how practical constraints inside Google led to a breakthrough that reshaped modern AI. He explains why recurrent models were hitting limits, how parallel attention opened the door to scale, and why he believed a major jump in capability was imminent long before the rest of the world saw it. The conversation then turns to the risks and responsibilities of today’s AI systems. Illia describes how models can be subtly guided to influence user opinions, why open weights are not the same as truly open models, and how hidden behaviors can be embedded during training. He explains why provenance and verifiable data pipelines matter, especially as AI begins mediating more of the information we rely on. Later in the episode, Illia outlines how blockchain can support trust, identity, and coordination in a future where AI agents act on our behalf. He shares why information is becoming more valuable than money, how ownership of personal AI models will shape user agency, and why domain expertise becomes significantly more powerful when paired with modern generative tools. Key Takeaways: Transformers emerged from practical constraints, not theory Illia explains that the shift from recurrent networks to attention was driven by speed and parallelization needs at Google, not a desire to invent a new paradigm. AI’s step change was foreseeable to early builders Illia expected a ChatGPT level breakthrough several years before it arrived, based on clear research signals and accelerating model performance. Provenance and trust will define the next phase of AI As AI systems can be subtly manipulated, Illia argues that verifiable data pipelines and transparent training processes are essential to prevent large scale misinformation. Ownership and identity matter in an agent driven world Illia believes individuals will soon rely on AI agents that act autonomously, making it critical that users own their models and that interactions between agents are secured and verified. https://near.ai – NEAR AI Cloud and Private Chat products are now live, try them here Illia's X: x.com/ilblackdragon Illia's Substack: ilblackdragon.substack.com NEAR X: x.com/nearprotocol 00:00 Intro: AI and Information Control 00:29 Meet Illia Polosukhin: Co-Author of 'Attention is All You Need' 01:03 The Evolution and Impact of AI 13:24 The Birth of Near AI and Blockchain Integration 15:16 Challenges and Innovations in Blockchain and AI 22:17 Privacy and Security in AI Applications 26:58 Exploring Sleeper Agents in AI 29:19 Practical AI Implementation in Teams 30:06 AI's Role in Product Development 31:41 Challenges and Future of AI in Development 36:35 AI and Economic Alignment 41:46 The Future of AI Agents 44:14 Debrief 📜 Read the transcript for this episode: Transcript of The Future Of AI With Illia Polosukhin: The Man Who Put The T In GPT | For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Christian Keller joins Henrik and Jeremy to explain how world models are shaping the next stage of generative AI. He talks through how AI learns using different types of inputs, and why video adds a sense of continuity, change, and cause and effect that text alone does not provide. Christian shares vivid analogies and clear examples to show what multimodal models make possible. The conversation moves into how AI is now used throughout the research process, from generating synthetic data to evaluating model outputs. Christian shares how this loop is already in motion and how AI is helping scale and accelerate experimentation. He also reflects on the shift after ChatGPT launched, and how that changed the pace and structure of research work. Later in the episode, Christian describes how individual workflows are evolving, and how asking simple questions like “Could AI help with this?” often opens new possibilities. He shares examples from his own work and home life, including how his wife built and graded her own French exercises using generative tools. Key Takeaways: Text removes essential information Christian explains that text compresses reality and loses detail, context and temporality. Images and video help restore what text leaves out. World models give AI a sense of change Video introduces the before and after and how things move or enter a scene. This helps models learn cause and effect and builds more robust understanding. AI helps build AI Models can generate data, evaluate results and support researchers during development. Christian shows how this creates new ways of scaling experimentation and training. Workflows shift when AI handles early steps Christian shows how tasks like debugging and prototyping change with generative tools, which reshapes roles and opens new opportunities for innovation. LinkedIn: Christian Keller | LinkedIn 00:00 Intro: Information Compression 00:37 Meet Christian Keller: AI Expert 01:13 The Evolution of AI Products 02:11 Impact of ChatGPT on AI Development 02:38 Understanding PyTorch and Its Role 07:41 The Bitter Lesson in AI 09:12 Challenges and Future of AI Models 18:57 Using AI to Build AI 23:25 Innovative Chat Interfaces 23:41 Building the Autos Platform 24:35 Epiphanies in AI Integration 25:18 AI in Entrepreneurial Workflows 26:32 Challenges in AI Integration 31:15 Bias in AI Models 38:06 Debrief 📜 Read the transcript for this episode: Transcript of AIs Next Frontier: World Models Explained by Christian Keller | For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Generative AI is moving fast, but most organizations aren’t. Tim Creasey and Paul Gonzalez have spent their careers studying why. As leaders at Prosci, they’ve worked with thousands of teams navigating complex change, and in this episode they share what their research says about the human side of transformation. They discuss why traditional tactics like comms and training break down in the face of rapid AI adoption, and how successful organizations create the conditions for people to actually change. From hands-on leadership and peer-driven learning to the power of experimentation and the ADKAR model, this conversation is packed with practical tools and hard-earned insights. Tim and Paul also explore how AI is reshaping organizational structures, what “exposure hours” reveal about executive readiness, and why culture beats mandates every time. Whether you’re leading change or stuck inside it, this episode offers a grounded look at what actually works when everything is in motion. Key takeaways: Bold vision is not enough - it also needs to be balanced The most effective AI leaders communicate both where the organization is going and what teams are doing right now to get there. Prosci’s research shows that near-term clarity matters just as much as long-term ambition. Leaders need to use the tools themselves Tim and Paul introduce the idea of “exposure hours” as a leading indicator of readiness. The more time executives spend actively experimenting with AI, the better positioned they are to lead transformation. Experimentation requires structure and safety Organizations can’t just tell people to try new things. They need to carve out time, reduce the stakes, and make experimentation a shared and visible part of how work gets done. Real change still happens one person at a time Despite all the new tech, the fundamentals haven’t changed. Individuals need awareness, desire, knowledge, ability, and reinforcement to adopt new behaviors. Prosci’s ADKAR model remains essential for making change stick. LinkedIn: Prosci: LinkedIn Website: Prosci | The Global Leader in Change Management Solutions 00:00 Introduction to Change Management and AI Adoption 00:25 Meet the Experts: Tim Creasey and Paul Gonzalez 01:51 The Challenges of Change Management 04:07 Generative AI Transformation: Unique Challenges 07:44 Key Ingredients for Successful AI Adoption 15:18 Building a Culture of Experimentation 20:43 The Role of Leadership in AI Transformation 25:54 Future Organizational Designs with AI 27:02 Disruptive Organizational Changes 28:00 Examples of Innovative Enterprises 28:15 Military Analogies in Business 29:30 Challenges in Organizational Change 30:36 Timeless Principles of Change Management 31:36 The Role of Leadership in Change 33:13 ADKAR Model for Change 35:51 Addressing Resistance to Change 40:05 Effective Communication Strategies 47:48 Concluding Thoughts and Reflections 📜 Read the transcript for this episode: Transcript of How Science Suggests You Change Your Organization - with Prosci’s Tim Creasey and Paul Gonzalez | For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Applied Intuition builds the kind of AI you don’t see, but can’t live without. Co-founders Qasar Younis and Peter Ludwig share how their $15 billion company powers vehicle intelligence across cars, trucks, tanks, mining equipment, and defense systems operating in some of the most demanding conditions on earth. They explain why combining AI with safety-critical systems raises the stakes, how a single mistake can destroy an entire company, and why so many autonomy startups ended up in the “graveyard.” The conversation explores the slow, methodical path to real autonomy, the hidden complexity of machines that run nonstop, and why consumer AI metaphors break down once software meets the physical world. Qasar and Peter also reflect on how Applied uses AI internally, how their principle of “radical pragmatism” keeps innovation grounded, and what it takes to move fast without breaking things when lives and livelihoods are on the line. From six-figure labor shortages in remote mines to the future of defense and logistics, this episode reveals how AI is quietly transforming the physical world — one carefully coded system at a time. Key Takeaways: Safety changes everything about AI When AI moves from the screen to the real world, the rules change. Qasar and Peter explain why building for trucks, tanks, and jets demands a different kind of discipline — one where precision and safety replace speed and iteration. The graveyard of autonomy is real There’s a long list of companies that underestimated what it takes to build safe, reliable autonomy. Applied Intuition’s founders share what went wrong — and why moving slower has been their biggest advantage. Radical pragmatism is the hidden differentiator Inside Applied Intuition, “radical pragmatism” isn’t a slogan — it’s a practice. Qasar and Peter describe how it guides product decisions, culture, and leadership, helping them innovate in places where failure isn’t an option. The next frontier of AI is off the screen From mines to military systems, the future of AI won’t be chatbots — it will be machines that think, move, and decide in the physical world. Jeremy and Henrik reflect on how that shift raises the bar for builders, leaders, and the technology itself. Applied Intuition: http://applied.co/ LinkedIn: linkedin.com/Applied X: https://x.com/Applied 00:00 Intro: Safety Critical Systems 00:33 Meet the Founders of Applied Intuition 01:09 Understanding Applied Intuition's Unique Approach 03:02 The Human-Machine Teaming Concept 07:26 Challenges in Autonomous Driving 16:39 AI in Industrial Applications 28:27 Future of Fighter Jets and AI 29:50 AI in Applied: Coding Tools and Beyond 33:16 Radical Pragmatism and AI Integration 36:03 Challenges of AI Adoption in Large Organizations 39:56 Human and Technical Challenges in AI 42:02 Innovation and Organizational Structure 48:38 Reflections on AI and Future Prospects 📜 Read the transcript for this episode: Transcript of You Can’t Vibe Code a 100-Ton Truck: Inside Applied Intuition’s Approach to Safety-Critical AI For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

As Head of IBM Consulting, Mohamad Ali led one of the most ambitious enterprise AI transformations to date. By making IBM its own “Client Zero,” his team tested every AI solution internally before bringing it to market. The effort began with massive hackathons involving 150,000 employees, turning curiosity into capability and belief at scale. Mohamad shares how leadership alignment, process redesign, and broad employee engagement drove $3.5 billion in cost savings and renewed growth. Jeremy and Henrik reflect on why IBM’s model may signal the next evolution of consulting — where organizations act as their own laboratories for change Key Takeaways: Start with Yourself: “Client Zero” Works IBM transformed internally before advising clients, using its own systems as a testing ground. This allowed the team to validate AI tools, workflows, and cultural shifts in real conditions, creating credibility and clarity before going to market. Transformation Needs More Than Tech Success came from a mix of technical leadership, process redesign, and cultural momentum. AI wasn’t just layered on; it was embedded into workflows, backed by leadership buy-in, and powered by 150,000 employees who participated in company-wide hackathons. Digital Labor Is Reshaping Business Models IBM automated most transactional HR tasks with AI tools like AskHR, driving a 40% reduction in HR operating costs — a look at how hybrid human–AI teams transform services. Measure and Share the Impact Transformation became real when IBM tied outcomes to business metrics. By reporting $3.5 billion dollars in savings and tracking results with the CFO, IBM showed how to make AI adoption tangible, accountable, and visible to both employees and investors. LinkedIn: Mohamad Ali - IBM | LinkedIn IBM: IBM 00:00 Intro: HR Automation 00:41 Introduction of Mohamed Ali and IBM's Transformation 01:14 IBM's Enterprise Transformation 01:41 The Role of AI in IBM's Success 03:25 Rejoining IBM: A Strategic Decision 04:33 Key Components of AI Implementation 07:21 Employee Engagement and Hackathons 08:59 Technical Leadership and AI 10:37 Global Tax Optimization with AI 11:17 Scaling AI Solutions for Clients 22:00 Monetizing Digital Labor 26:50 Digital Labor and Procurement Projects 27:29 Unbundling and Economic Implications 28:44 Technological Shifts and Market Expansion 30:04 AI-Powered Business Transformations 32:22 Case Study: L'Oreal's AI Integration 39:13 HR Automation and Cost Reduction 42:09 Creative Innovations in AI Applications 43:59 Advice for Leaders on AI Integration 45:43 Final thoughts 📜 Read the transcript for this episode: Transcript of How IBM Used AI to Cut 40% of HR Operating Costs and Reinvest in the Company | For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Martin Reeves has spent decades advising CEOs on how to think about strategy. As head of BCG’s Henderson Institute, he has built a career challenging leaders to balance efficiency with imagination and to prepare for the next disruptive shift. In this conversation, Martin tells Henrik and Jeremy why AI alone will not give companies an edge and might even strip them of advantage. He unpacks the “two jobs of business”: playing the current game better than anyone else while simultaneously asking what the next game will be. He argues that AI only sharpens this paradox, forcing leaders to think faster, experiment more, and draw on human imagination in new ways. The discussion covers the risks of over-optimization, the future of consulting, and the paradoxes of AI adoption. Along the way, Reeves explains how AI can accelerate exploration, why framing the right questions is the strategist’s most important job, and why times of disruption are when number twos become number ones or disappear altogether. Key Takeaways: Strategy is the double game Long-term success means playing today’s game efficiently while also inventing tomorrow’s. Henrik and Jeremy stress how rare it is for leaders to do both, yet this is exactly what AI demands. AI efficiency without imagination is a trap Adopting the same tools as competitors drives efficiency but commoditizes advantage. The hosts underline that imagination and unique use are what create real differentiation. The strategist’s edge is asking the right question Martin highlights that strategy starts with framing the real question. Henrik and Jeremy note that questioning and cognitive diversity are crucial in the AI era. Disruption reshuffles winners and losers Times of change are when number twos become number ones and leaders disappear. The wrap-up emphasizes the urgency of experimenting and adapting now. Human imagination stays essential AI can accelerate exploration, but creativity, ethics, and originality remain uniquely human — and decisive for future leadership. LinkedIn: Martin Reeves | LinkedIn BCG Henderson Institute: Home - BCG Henderson Institute Martins books: The Imagination Machine // Like: The Button That Changed the World 00:00 Intro: Two Jobs in Strategy, Today’s Game and Tomorrow’s Game 01:33 Martin Reeves and the Henderson Institute 04:02 Defining Strategy in the AI Era 05:12 AI and Human Imagination 09:20 Efficiency vs. Competitive Advantage 13:18 Organizational Design for the Future 23:09 The Paradox of Imagination in Business 33:02 Harnessing Serendipity for Innovation 35:18 Devil’s Advocacy and Meeting Optimization 36:51 Where AI Helps and Hurts Organizations 38:16 The Limits of AI Training Data 42:56 How Martin Uses AI Day to Day 47:09 What’s the Next Game for Consulting 53:15 Final Reflections 📜 Read the transcript for this episode: Transcript of How Do You Strategize in the AI Era? – with Martin Reeves, Head of BCG’s Think Tank For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Adam Brotman and Andy Sack sit down with Henrik and Jeremy to unpack their book AI First and the framework they have developed for leaders. They argue that AI is not just another technology wave but a leadership reset that demands new playbooks, new structures and new ways of thinking. They explain why AI should be seen as an augmentation of human intelligence, an “Ironman suit” for leaders, and how mindset, experimentation and governance are essential to adoption. The conversation also explores organizational redesign, the role of executives in fostering AI literacy and the urgency of adapting quickly as the technology advances. This episode offers a practical and forward-looking discussion on how leaders can integrate AI across their organizations, build cultures of experimentation and future-proof their businesses in a rapidly changing landscape. Key Takeaways: AI is a leadership reset, not just a technology shift. Adam and Andy argue that AI demands a new playbook for leaders. It is not simply another tool, like mobile or digital before it, but a force that changes how companies are structured, how decisions are made, and how leaders must think about competition. AI should be treated as a co-intelligence tool — an “Ironman suit” for leaders. Instead of replacing humans, AI augments their capabilities. Leaders who embrace AI can make smarter, faster decisions and guide their organizations more effectively. The metaphor of the Ironman suit captures this idea of augmentation rather than substitution. Culture and experimentation matter more than the tools. Mindset, governance, and a willingness to experiment are the foundations of becoming AI-first. Adam and Andy stress that companies need structures like AI councils, experimentation frameworks, and a culture that celebrates rapid prototyping in order to integrate AI across the organization. The urgency is real: companies that delay will fall behind. Jeremy and Henrik underline this in their closing reflections — businesses cannot treat AI as optional or wait for perfect clarity. The pace of change is accelerating, and organizations that don’t engage now risk losing ground permanently, while those that act can reinvent themselves and secure long-term advantage. Forum3: Digital Strategy for the AI Era | Forum3 AI First book: AI First Book | Forum3 Andy LinkedIn: Andy Sack | LinkedIn Adam LinkedIn: Adam Brotman | LinkedIn 00:00 Intro: The Urgency of AI 00:19 Meet the Authors & The Premise of AI First 03:43 Defining an AI-Forward Leader 05:02 Adoption, Resistance & the AI Wake-Up Call 08:01 Why Mindset Matters More Than Tools 09:39 Experimentation, Governance & AI Culture 14:09 Re-architecting Organizations for AI 28:42 Balancing Innovation and Safety 35:45 The Evolution of AI Safety 37:46 Open Source vs. Closed Source Debate 40:07 AI’s Role in Organizational Agility 41:32 Human Augmentation & Co-Intelligence 42:34 The Future of AI and Autonomous Agents 46:14 Prototyping, Vibe Coding & Rapid Innovation 54:02 The Future of Organizational Design & Final Reflections 📜 Read the transcript for this episode: Transcript of The AI Playbook Every Leader Needs: A Chat With Adam Brotman & Andy Sack For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In a shift from the usual format, Henrik Werdelin steps into the guest seat—alongside Nicholas Thorne—for a live conversation with Jeremy Utley about their new book Me, My Customer, and AI. They explore what it takes for entrepreneurs to compete in the age of AI — from redefining resourcefulness to thinking like founders, even inside a job. The discussion dives into the book’s central frameworks, including the Five Ps (powers, passions, possessions, positions, and potentials) and the “it sucks that…” approach to identifying real problems worth solving. Along the way, they reflect on how AI is changing the leap from idea to execution, why more people may need to think entrepreneurially, and the shift from operating to orchestrating. They also share lessons from the writing process itself—how they tried to use AI, where it fell short, and why Me, My Customer, and AI ends when it does. This episode isn’t just about launching a book. It’s about rediscovering agency, and the questions we all need to ask when starting something new. Key Takeaways: This isn’t a book about AI—it’s a book about you. Henrik and Nicholas share how the real questions emerging from AI are deeply human ones. The book focuses first on self-understanding, then on the customer, with AI as the third piece—not the center. The Five Ps framework helps you figure out what to build—and why. Powers, passions, possessions, positions, and potentials offer a structured way to explore personal founder-market fit. It’s a tool for generating ideas, but also for stress-testing them. Real problems often hide in plain sight—it just sucks that no one’s solved them. Using the phrase “it sucks that…” makes it easier to spot problems worth solving. It’s simple, emotional, and sharp enough to cut through vague ideas and find what really matters to people. Entrepreneurial thinking isn’t just for founders anymore. In a world shaped by AI agents and fluid roles, more people will need to act like entrepreneurs—taking initiative, connecting dots, and orchestrating rather than operating. Book site: Me, My Customer and AI - The New Rules of Entrepreneurship Buy the book: Amazon.com: Me, My Customer, and AI: The New Rules of Entrepreneurship Audos: Audos Audos Instagram: Direct • Instagram Nicholas LinkedIn: Nicholas Thorne | LinkedIn 00:00 Intro: The Human Questions Behind AI 00:37 Personal Reflections on AI 01:26 The Book’s Unique Perspective 02:55 AI and Human Resourcefulness 05:46 Entrepreneurship in the AI Era 13:05 The Five Ps Framework 23:53 Identifying Real Problems 25:39 Why Identifying and Reframing Problems Matters 26:27 The Concept of “It Sucks That” 27:23 Historical Context and Practical Applications 28:22 The Role of Language in Problem-Solving 29:43 AI’s Influence on Writing and Creativity 31:47 Challenges and Limitations of AI in Writing 35:38 The Future of AI in Creative Processes 43:30 Entrepreneurial Skills for the Modern Era 48:26 Audience Interaction and Final Thoughts 📜 Read the transcript for this episode: Transcript of How Entrepreneurs Can Compete in the Age of AI: Henrik Werdelin & Nicholas Thorne on Their New Book Me, My Customer and AI For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

Wade Foster, co-founder and CEO of Zapier, joins Henrik and Jeremy to talk about how AI is changing the company from the inside out. He shares the moment Zapier declared a “code red” on AI and the steps they took to turn urgency into action — encouraging more experiments, removing tolerance for inaction, and celebrating wins along the way. Wade discusses his own AI use cases, the importance of internal examples in driving adoption, and why duplication of efforts can speed up learning. He reflects on the leadership challenge of guiding a 14-year-old company through cultural transformation, balancing productivity gains with employee well-being, and preparing for a future where AI agents work with each other. This episode offers a clear, practical look at what it takes to embed AI into an established organization, and keep it moving forward. Key Takeaways: A “code red” can be a catalyst for real change. When Zapier declared a company-wide “code red” on AI, it wasn’t just a signal. It pushed people to experiment more, act faster, and rethink established ways of working. Culture is harder to change than technology. The real challenge wasn’t getting the tools in place, it was getting people to use them. Zapier’s approach focused on rewarding curiosity, sharing internal examples, and removing tolerance for inaction. Duplication can drive innovation. Instead of centralizing all AI projects, Zapier encouraged parallel efforts. When multiple teams tackled similar problems, they often uncovered different and better solutions more quickly. Leadership in the AI era is about speed and sustainability. Henrik and Jeremy highlight how Wade’s approach blends urgency with care for the people doing the work. Productivity gains matter, but so does avoiding burnout and making AI adoption last. Zapier: Zapier: Automate AI Workflows, Agents, and Apps LinkedIn: Wade Foster | LinkedIn 00:00 Setting Company Culture: Rewards and Tolerances 00:43 The Rise of AI at Zapier 02:19 Wade's Social Media Presence 05:06 Challenges in AI Adoption 07:32 Personal Use of AI: Health Tracking 10:21 Business Applications of AI 13:34 Automating Repetitive Tasks 20:35 Voice of Customer Program 24:26 Customer Brief Generator 33:27 Code Red: Embracing AI 35:32 Subtle Encouragement and the Impact of GPT-4 36:38 Code Red: A Turning Point 36:51 Embracing AI: From Fear to Familiarity 38:13 The Journey to AI Adoption 39:11 Challenges in Organizational Change 40:41 Managing Resistance and Encouraging Experimentation 43:55 Building a Remote Culture with AI 46:29 The Future of Work and AI 48:33 Agent-to-Agent Communication 51:32 The Importance of Duplication in Innovation 56:43 Final Thoughts 📜 Read the transcript for this episode: Transcript of Inside Zapier’s Code Red: How CEO Wade Foster Hit Pause to Reinvent for AI For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Evan Ratliff, journalist and creator of the podcast Shell Game, shares the wild and personal story behind his experiment in AI voice cloning. What began as curiosity turned into a six-month dive into building an AI version of himself—one that could answer phone calls, conduct interviews, and even fool friends and family. From scamming the scammers to testing AI therapy, Evan walks us through what it’s like to put a synthetic version of yourself into the world and watch how people respond. The conversation explores the uneasy collision of identity, automation, and ethics. Evan talks about the emotional reactions people had when they realized they weren’t actually talking to him, the disturbing effectiveness of AI in fraud, and the strange intimacy of hearing your own voice say things you didn’t write. He also reflects on what it means to resist optimization—not because tech can’t help, but because some parts of life aren’t meant to be outsourced. This episode is a human story wrapped inside a technological one—about trust, loneliness, and how we navigate a world where even our voices aren’t entirely our own. Key takeaways: AI voice agents challenge more than trust—they challenge identity. Evan’s experiment revealed just how disorienting it is when people hear your voice and think it’s you —only to realize it’s not. The emotional impact was real: friends felt tricked, disconnected, and in some cases, deeply lonely. Scammers are already using AI—and they’re getting better at it. Far from being hypothetical, AI-powered scams are already widespread and industrialized. Voice cloning isn’t just a curiosity—it’s a weapon, and we’re all potential targets. A family safe word might be your best defense. Not everything should be optimized—and maybe that’s the point. Evan pushes back on the idea that life should be frictionless. In the pursuit of efficiency, we risk removing the small, inconvenient interactions that actually make life meaningful—like small talk, shared confusion, and human error. This moment feels like early social media—and we should be paying attention. Henrik and Jeremy reflect on the eerie parallels between today’s AI boom and the rise of the social web. Back then, few anticipated the long-term impact on mental health and connection. With AI, we may be walking into similar territory—unless we ask harder questions now. LinkedIn: Evan Ratliff | LinkedIn Website: Evan Ratliff – Journalist Shell Game Podcast: Shell Game | Evan Ratliff NY Times article referred to: Nytimes/ThisMachine-madeWorldConquersOneMoreRebel 00:00 Intro: Thoughts on AI Deception 00:40 Meet Evan Ratliff: Technology, Crime, and Identity 01:13 The Shell Game Podcast: Exploring AI Voice Cloning 03:50 Challenges and Improvements in AI Voice Technology 04:57 Inspiration Behind the Voice Cloning Experiment 11:05 Practical Applications and Ethical Considerations 17:31 AI in Scamming: Risks and Realities 25:04 Protecting Yourself from AI Scams 27:49 Reflecting on Technological Change and Human Adaptation 29:59 The Reluctance to Embrace New Technology 30:36 The Dangers of Social Media 31:59 AI in Therapy and Personal Experiences 33:39 Creating an AI Agent of Yourself 38:09 The Challenges of Small Talk with AI 38:55 Personal Tech Stack and AI Usage 42:59 Balancing Efficiency and Meaningfulness 45:32 The Future of AI and Human Interaction 52:18 Concluding Thoughts and Reflections 📜 Read the transcript for this episode: Transcript of Can AI Replace Me? Evan Ratliff on Letting an AI Clone Live His Life For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Jeff Benjamin, Global CCO of Tombras, shares how AI helps him get unstuck, build confidence, and push bold ideas forward—even when self-doubt creeps in. From romcom scripts to Arby’s pitches, he shows how AI acts as a sparring partner: sharpening thinking, stress-testing ideas, and keeping momentum alive. We get into what separates distinct from generic, why affirmation can be a trap, and how the urge to share is still at the heart of creativity. If you're chasing big ideas—or just trying to beat the blank page—this one hits home. Key Takeaways: Affirmation builds momentum—but can also blind you — One of AI’s biggest features is how confidently it backs you up. That “glazing” energy feels great—but if you don’t challenge it, you risk falling in love with something average. Confidence needs a counterbalance: taste. The best prompt is a person—not a question — Jeff gets better output by asking AI to role-play voices he respects—like Don Draper or a cold war-era Olympic judge. The magic isn’t in better instructions. It’s in asking from a more interesting perspective. Your idea is ready when it bubbles over — Jeff doesn’t go to his team with half-baked concepts. He waits until the idea is bubbling—when he can’t not share it. That moment is emotional, not procedural. AI helps him reach it faster—but the instinct to share is still deeply human. Big ideas have width—AI helps him see the shape — For Jeff, a great idea isn’t a line—it’s a landscape. If it’s a real “big idea,” it spawns more ideas: social angles, activations, scripts. AI helps him test whether a concept has legs—or if it’s just a clever line with no room to run. Jeff's LinkedIn: Jeff Benjamin | LinkedIn Tombras: Tombras | Full-Service Independent Advertising Agency 00:00 Overcoming Self-Doubt in Business 00:37 Meet Jeff Benjamin: Creative Leader at Tombras 00:56 The Role of AI in Creative Processes 02:24 Using AI as a Sparring Partner 04:34 Practical Examples of AI in Action 09:31 The Impact of AI on Team Dynamics 11:37 Balancing AI and Human Creativity 14:13 The Future of AI in Creative Industries 21:06 Exploring Human Skills for AI Mastery 22:09 The Art of Asking Better Questions 22:40 AI as a Creative Partner 24:41 The Excitement of Sharing Ideas 30:09 Generational Differences in AI Interaction 32:35 The Risk of AI Dehumanization 38:19 Concluding Thoughts 📜 Read the transcript for this episode: Transcript of How the Chief Creative Officer of an Award-Winning Ad Agency Prompts the Perfect Pitch For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Joshua To, VP of Product Design at Meta, shares how AI is reshaping how—and where—we interact with technology. He walks us through Meta’s evolving approach to AR and wearables, why notifications are still the killer use case, and how AI is becoming the “brain behind empathy.” We dig into what it means to build interfaces that understand you, why audio might be the future’s most underrated platform, and how designing for emotion changes everything—from form factor to function. Joshua also reflects on his path from launching a clothing brand to leading design at Google and Meta, and what those worlds taught him about craft, context, and human-centered systems. This one’s for anyone designing AI into the real world—where every interface choice carries weight, and intelligence starts with listening. Key takeaways: Empathy Is the Real Intelligence — Joshua flips the definition of smart tech. It’s not just about outputs—it’s about understanding you. Context, tone, emotion— that’s what great AI will sense and respond to. Design for the Moment, Not the Feed — AR’s killer use case isn’t games—it’s restraint. Joshua shares why the best AI product might just be the one that knows not to ping you. Context-aware computing is the real unlock. Audio Is the Interface to Watch — Forget screens. The most powerful interface might be your ears. From wearables to ambient signals, Joshua explains why audio design is the next big frontier for human-centered AI. AR Isn’t a Feature—It’s a System of Consideration — Joshua reframes augmented reality as quiet, ambient infrastructure. The real power of AR isn’t spectacle—it’s subtlety. It helps you move through the world with less friction, not more. LinkedIn: Joshua To | LinkedIn Website: Home - Joshua To Meta: Meta Careers 00:00 Intro: Fixing Notifications With AI 00:54 Meet Josh: VP of Product Design at Meta 02:06 From Hoodies to Hardware: Josh's Journey 03:53 The Google Experience: From Ads to Product Management 10:37 The Evolution of Google Glass and AR 19:12 The Role of AI in Josh's Career 22:25 Designing the Future: AR, VR, and Attention Management 32:49 Contextually Aware Suggestions 33:38 Leveraging Generative AI in Design 34:52 AI's Role in Concept Art and Storyboarding 41:24 AI Tools and Model Capabilities 45:54 The Future of AI and Wearables 51:58 Reflections and Takeaways 📜 Read the transcript for this episode: Transcript of Chief of Staff for the Masses: How Meta’s Joshua To Designs Wearables with AI For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, Greg Shove, CEO of Section and founder of Machine and Partners, joins us for a "where are they now" follow-up—and doesn’t hold back. Greg walks through the rise of Pro AI, his new AI-powered coach, and why traditional upskilling is already obsolete. We explore the overlooked friction points in AI adoption, from cultural taboos (“it feels like cheating”) to failed enterprise rollouts. Greg challenges the prevailing mental models and warns that the real upheaval is still ahead: business model disruption, not product disruption. From royalty-based agents to outcome-based pricing, Greg lays out why service-heavy industries—from law firms to SaaS—are heading for a margin-crushing future. Plus: the moral responsibility of CEOs, the fallacy of lifelong learners, and why working with AI means holding onto your own judgment. A sharp, honest look at what it really means to work smarter—not just faster—in the age of AI. Key takeaways: AI use is no longer optional—it's the new baseline. Proficiency with AI tools isn’t a competitive edge anymore—it’s a basic requirement. Greg argues that “being in the AI class” is now table stakes, and organizations must rapidly close the gap between aspiration and actual adoption. Business model disruption will hit harder than tech disruption. Greg makes a compelling case that AI’s biggest impact won’t come from the tools themselves, but from entirely new ways of charging for value—like outcome-based pricing and AI-native service models that undercut human capital costs. Leaders must shift from AI policies to AI manifestos. Adoption is stalling because organizations lead with fear. Instead, Greg urges leaders to clearly message that using AI is smart, encouraged, and expected—and to model that behavior themselves. Most people won't be lifelong learners—so give them outputs, not courses. With Pro AI, Greg confronts a hard truth: most users don’t want to learn; they want results. AI-powered coaching that delivers outcomes—not just education—is the future of upskilling. Linkedin: Greg Shove | LinkedIn Website: Greg Shove | AI Strategist & Keynote Speaker for Enterprise Leaders Section: Section | AI workforce transformation for real ROI Machine & Partners: AI Consulting Services | Machine and Partners 00:00 Embracing AI: Changing Work Culture 00:29 Introduction: Meet Greg Shove 01:10 AI in Daily Work: Tools and Changes 03:59 Business Model Disruption: The Next Big Shift 12:45 Training and Adoption Challenges 19:14 The Future of Work: AI's Impact on Jobs 32:02 Leadership and AI: Strategies for Success 35:20 Embracing AI in the Workplace 36:51 Workflow Redesign with AI 39:39 The Role of AI Agents 40:12 Challenges in AI Adoption 45:14 Pro AI: The AI-Powered Coach 51:03 Disrupting Business Models with AI 57:52 Cognitive Offloading and AI 01:03:02 Final Thoughts and Reflections 📜 Read the transcript for this episode: Transcript of The AI Implementation Audit: What Section’s CEO Learned in 18 Months For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

In this episode, John Waldmann, CEO of Homebase, shares how the 10-year-old SaaS company blew up its roadmap and rebuilt around AI—from culture to code. He walks us through the shift from 20-page PRDs to lightning-fast demos, reclaiming product leadership, and pushing teams into their “oh shit” moment with AI. We explore the leadership reckoning, cultural resistance, and practical playbook behind the transformation—and what it means for the future of SaaS, small businesses, and human-centered AI. If you're leading (or bracing for) an AI shift, this one’s packed with hard-earned lessons and honest insight. Key Takeaways: You Can’t Wait for Buy-In—Leadership Means Pushing the Shift — John didn’t wait for excitement or alignment—he took back product leadership and forced the move toward AI. It wasn’t about consensus, it was about momentum. If you’re leading a team through this kind of shift, your job isn’t to ask for permission—it’s to create urgency before it's obvious. Speed Over Specs — Prototypes Are the New Strategy — Homebase moved from 20-page PRDs to live demos built in hours. That switch didn’t just make shipping faster—it changed the way teams learn, think, and listen to customers. The takeaway? Stop planning in the abstract. Ship something real, now. Culture Is the Real AI Roadblock — The hardest part of going AI-first isn’t tech—it’s trust, fear, and inertia. From engineers to support teams, John had to help people reach their “oh shit” moment with AI. That’s when change sticks. Until then, it’s just optional homework. Leaders need to make adoption inevitable. AI Should Bring You Closer to Your Customers, Not Farther — This episode isn’t about chasing shiny tools. It’s about using AI to reduce the noise—so your team can focus more on humans, not less. For John, pragmatic AI is about freeing up time, getting closer to customer problems, and making the org feel smaller, not colder. LinkedIn: John Waldmann | LinkedIn Homebase: All-in-one Employee Scheduling, Time Clocks, Payroll, & More | Homebase 00:00 Introduction and Initial Reactions to AI 00:31 Meet John Waldmann and the Story of Homebase 00:53 Reinventing Homebase as an AI-First Company 01:46 From PRDs to Prototypes: Building Faster, Learning Smarter 05:02 How AI Is Reshaping the Customer Experience 09:19 Culture Shock: Resistance, Skepticism, and AI Adoption 14:03 The End of SaaS as We Know It? 19:34 Leading Through Disruption: Ownership, Urgency, and Org Design 25:12 Forcing the Shift: Getting Teams to Embrace AI 27:50 Hiring the Unemployed—and Other Nontraditional Talent Bets 28:56 Curiosity > Credentials: What to Look for in AI-Ready Teams 31:57 New Expectations, OKRs, and Holding Teams Accountable 37:10 Serving Small Businesses Better with AI 44:52 Final Thoughts: Team Dynamics, Founder Risk, and What’s Next 📜 Read the transcript for this episode: Transcript of Rebuilding from Inside: How John Waldmann Led an AI Shift Without Breaking His Team | For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin: Henrik: https://www.linkedin.com/in/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.