Beyond The Prompt - How to use AI in your company

Jeremy Utley & Henrik Werdelin

Beyond the Prompt dives deep into the world of AI and its expanding impact on business and daily work. Hosted by Jeremy Utley of Stanford's d.school, alongside Henrik Werdelin, an entrepreneur known for starting BarkBox, prehype and other startups, each episode features conversations with innovators and leaders to uncover pragmatic stories of how organizations leverage AI to accelerate success. Learn creative strategies and actionable tactics you can apply right away as AI capabilities advance exponentially.

  1. Greg Shove on Why Most Companies Are Not Seeing ROI On AI (yet)

    MAR 18

    Greg Shove on Why Most Companies Are Not Seeing ROI On AI (yet)

    Greg Shove describes a growing gap between individual and organizational AI adoption. A small group of employees are already using AI effectively, while most companies are still early. AI is generating real productivity gains, but those gains are not being captured at the company level. Instead, they are absorbed by individuals who use AI to work faster, often without changing team outputs or structures — raising a central question: if AI creates time, where does that time go? The conversation explores why enterprise AI adoption remains uneven. Many organizations lack a clear point of view on AI, and workflows take time to adapt, making it difficult to turn individual gains into coordinated results. At the same time, AI is breaking capability boundaries, allowing people to take on work across roles while companies remain structured around existing ways of operating. From a leadership perspective, Greg emphasizes that the challenge is not just efficiency. AI creates capacity, but without clear direction on how to use it, that capacity disappears. Leaders must decide how to reinvest the time AI creates if they want to capture real business value.Key Takeaways:  AI’s ROI is leaking, not missing Companies are generating value from AI, but it’s being captured by employees rather than the organization. A small group drives most of the impact Roughly 10–15% of employees adopt AI early and use it effectively, creating an uneven distribution of gains. AI is breaking capability boundaries Individuals can now take on work across roles, but organizations are still structured around fixed responsibilities. Most companies lack a clear point of view on AI Without direction from leadership, adoption becomes fragmented and employees are left to figure it out themselves. Leaders must decide what to do with the time AI creates Efficiency gains alone don’t create value. Organizations need to define new, higher-value work or the gains disappear. Greg's LinkedIn: linkedin/gregshove Section LinkedIn: linkedin/company/sectionai Section AI: sectionai.com Prof AI: prof.ai 00:00 Intro: Entering the Era of AI Chaos00:31 Meet Greg Shove01:32 Enterprise AI Is a C Minus01:51 AI’s ROI Is “Leaking” to Employees03:04 When Individuals Outrun the Organization05:44 When AI Breaks Workflows06:47 Disposable Software and New Ways of Building09:10 Cut vs Create12:01 Using the Calendar as a Lever16:24 Why Enterprises Don’t Move17:32 When Customers Force Change21:31 AI Breaks Capability Boundaries25:44 The Productivity Firehose27:49 Who Actually Captures the Value28:45 Why Everyone Needs Good AI32:00 Adoption Beats Buying More Tools40:17 Teaching the 90 Percent43:48 Where Humans Still Matter48:09 The Debrief 📜 Read the transcript for this episode: greg-shove-on-why-most-companies-are-not-seeing-roi-on-ai-yet/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.

    59 min
  2. How to Subtract: The Most Underrated Skill of the AI Era - with Leidy Klotz

    MAR 4

    How to Subtract: The Most Underrated Skill of the AI Era - with Leidy Klotz

    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 Add00:28 Meet Leidy Klotz01:15 The Subtract Idea02:56 Organizations Get Bloated03:49 Scandinavian Design Mindset04:32 New Book: In a Good Place05:59 AI Abundance and Indigestion08:12 Curate Context, Not More11:38 Cues and Stop-Doing Lists15:00 Default Debt and Kill Dates17:10 Odysseus Contracts and Biases21:28 Reengage the Physical World29:17 Bike Shedding and Priorities36:10 Making Is Thinking49:16 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/werdelinJeremy: https://www.linkedin.com/in/jeremyutley   Show edited by Emma Cecilie Jensen.

    59 min
  3. From Roadmaps to R&D: How AI Is Changing Product Development - with Richard White, Founder of Fathom AI

    FEB 18

    From Roadmaps to R&D: How AI Is Changing Product Development - with Richard White, Founder of Fathom AI

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

    57 min
  4. Here’s How to Know If You’re Getting the Most Out of AI – with Bryan McCann, CTO of You.com

    FEB 4

    Here’s How to Know If You’re Getting the Most Out of AI – with Bryan McCann, CTO of You.com

    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.

    1 hr
  5. Building An Enterprise AI Innovation Lab: A Master Class with Humza Teherany, Chief Strategy Officer of Maple Leaf Sports and Entertainment

    JAN 21

    Building An Enterprise AI Innovation Lab: A Master Class with Humza Teherany, Chief Strategy Officer of Maple Leaf Sports and Entertainment

    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.

    58 min
  6. Why AI Gets People Wrong: The Real Source of Insight with Anthropologist Mikkel B. Rasmussen

    JAN 6

    Why AI Gets People Wrong: The Real Source of Insight with Anthropologist Mikkel B. Rasmussen

    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.

    56 min
  7. How the World’s Leading AI-First Fashion House Flips the Cash Flow Equation - with Diarra Bousso

    12/24/2025

    How the World’s Leading AI-First Fashion House Flips the Cash Flow Equation - with Diarra Bousso

    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.

    1h 9m
  8. The Future of AI with Illia Polosukhin: The Man Who Put the T in GPT

    12/09/2025

    The Future of AI with Illia Polosukhin: The Man Who Put the T in GPT

    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.

    55 min
4.8
out of 5
68 Ratings

About

Beyond the Prompt dives deep into the world of AI and its expanding impact on business and daily work. Hosted by Jeremy Utley of Stanford's d.school, alongside Henrik Werdelin, an entrepreneur known for starting BarkBox, prehype and other startups, each episode features conversations with innovators and leaders to uncover pragmatic stories of how organizations leverage AI to accelerate success. Learn creative strategies and actionable tactics you can apply right away as AI capabilities advance exponentially.

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