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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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen. 

In this teaser, Henrik and Jeremy debrief their conversation with Humza Teherany, Chief Strategy and Innovation Officer at MLSE. They reflect on how Humza rebuilt his technical fluency, why he believes leaders can't delegate this moment, and what it actually looks like to launch an internal AI lab that ships in 24 hours. Full episode out next week. Full episode LIVE NOW.   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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen. 

In this teaser, Jeremy and Henrik debrief their conversation with Mikkel B. Rasmussen, founder of the Human Activity Laboratory. They expected a conversation about AI’s limitations, but got a rethinking of insight itself. They explore Mikkel’s definition of insight as the gap between how we think the world is and how it actually is, why surprise is a critical signal, and how pain often precedes clarity. They also touch on Mikkel’s experiments with AI interviewers that sometimes outperform human researchers, and why this episode challenged how they think about narrative, understanding, and the role of AI.  Full episode LIVE NOW.   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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen. 

In this teaser, Jeremy and Henrik debrief their conversation with Diarra Bousso, founder of the AI-first fashion startup DIARRABLU. They reflect on Diarra’s use of the word “yet” as a signal of growth, what it means to run a fashion brand more like a lab, and how her team “manages her back” when the ideas overflow. They also explore how AI is reshaping speed, sustainability, and experimentation in the fashion industry, and why your own lived experience might be your biggest asset in an AI-powered world. Full episode dropping next week.    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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen. 

In this teaser, Jeremy and Henrik reflect on their conversation with Illia Polosukhin, co-author of the “Attention Is All You Need” paper and founder of Near Protocol. They dig into Illia’s early expectations for ChatGPT, why “owning your AI” isn’t just a catchphrase, and how blockchain could help protect the information we rely on. They also explore what it really means to work with AI and why your own experience might be more powerful than you think. Full episode dropping soon.   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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen. 

In this teaser, Jeremy and Henrik break down their immediate takeaways from their conversation with Christian Keller, including model fidelity, hallucinations, and the surprising ways AI is already reshaping everyday workflows. Full episode drops tomorrow.   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/werdelinJeremy: 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/werdelinJeremy: 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/werdelinJeremy: 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/werdelinJeremy: 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen.