Beyond The Prompt - How to use AI in your company

Jim VandeHei joins Henrik and Jeremy around the launch of his new book, Simplify: Do 50% More With 50% Less , co-authored with Mike Allen and Roy Schwartz. In the conversation, Jim shares how he’s leading Axios through AI, from turning himself into an “AI lab rat” to rethinking how the company works. At Axios, Jim gave everyone access to ChatGPT, brought in training, and invited employees who took naturally to the technology to help others across the company. He also shares how AI fellows working directly with him prototype new ideas, why companies should delete unnecessary work before automating it, and how Axios is preparing for a world where more information moves from the open web into social platforms and personalized LLMs. The conversation ends with a bigger question: what story are we telling people about an AI-enabled future? Jim worries that the negative case for AI is vivid and easy to understand, while the positive case often amounts to curing cancer or eliminating work. He makes the case for something more tangible: using AI to make people’s work and lives better while preserving human connection and the feeling that what people do still matters. Jim's new book: Simplify: Do 50% more with 50% less “Confessions of an AI Lab Rat”: Read here! Axios: Axios.com Jim's LinkedIn: LinkedIn.com/JimVandeHei Key Takeaways Leaders need to become AI lab rats Leaders need firsthand experience with AI to understand what it can do and give others permission to experiment. Give the CEO an AI “SEAL team” Jim’s AI fellows help him prototype ideas quickly, stay current, and experiment outside the normal company processes. Delete before you automate Before using AI to make existing work faster, ask which meetings, processes, and activities shouldn’t exist at all. AI needs a better story about what goes right The negative future of AI is easy to picture. Jim argues we need a more tangible vision of how AI can improve people’s lives without losing purpose and human connection. 00:00 Intro: Predictions for the future 00:46 Meet Jim VandeHei 01:06 Becoming an AI Lab Rat 04:21 How Axios Got Everyone Using AI 05:46 Who Gets the Most Out of AI? 08:27 The AI Understanding Gap 11:04 How Jim Keeps Up With AI 14:20 Building Axios for an AI Future 20:47 The CEO’s AI “SEAL Team” 23:52 Writing for Humans and LLMs 26:49 Delete Before You Automate 30:46 The Monthly Mentality 35:58 AI-Assisted Writing and Authenticity 41:47 The Coming Political Backlash 47:42 A Better Story for AI 55:25 The Debrief 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.

After helping lead AI adoption at Moderna, Brice Chalamel has a new goal at OpenAI: help create 1,000 AI adoption success stories across large organizations. Brice takes Henrik and Jeremy inside how work happens at OpenAI, where Slack has largely replaced internal email, agents help process information and draft responses, and some workflows are already moving agent to agent. But as AI takes on more responsibility, Brice argues that trust can’t be mandated. It has to be earned. The conversation goes beyond tools into what it actually takes to lead people through change. Brice explains why, in a ten-step journey, nine steps may only be halfway, why our existing mental models shape how we respond to AI, and why leaders need to listen before trying to change someone’s mind. They also explore Brice’s idea that AI could move us from “knowledge workers” to “intelligence workers,” a personal story about his mother using ChatGPT while caring for his father with Alzheimer’s, and why the benefits of AI need to extend beyond the people already living in abundance. Key Takeaways Trust in AI has to be earned As agents take on more responsibility, people need experience with them before they’re willing to hand over judgment and communication. AI adoption is a mind game and a heart game Successful change depends not just on what people know about AI, but on what they believe, fear, and care about. Nine steps can be only halfway there The final stage of transformation is often where the hardest work begins and assumptions need to be questioned. We’re moving from knowledge workers to intelligence workers As AI handles more information processing, human value shifts toward judgment, perspective, influence, and decision-making. Listen before you think Changing minds starts with understanding the experiences and mental models behind someone’s point of view, not simply making a better argument. Brice's LinkedIn: linkedin.com/in/bricechallamel/ Website: powerofwhy.ai 00:00 Intro: From Knowledge Worker to Intelligence Worker 00:30 Meet Brice Chalamel 01:18 From Moderna to OpenAI 04:50 The Mission: 1,000 AI Success Stories 07:45 How Work Happens at OpenAI 08:59 When Agents Talk to Agents 10:56 Trust Has to Be Earned 15:22 Brice’s Principles for Change 16:45 Nine Steps Is Halfway There 25:21 The Mind Game 30:24 Why Leaders Resist AI 35:28 The Human Side of AI 37:37 When ChatGPT Became a Lifeline 41:56 From Knowledge Worker to Intelligence Worker 43:37 Agency in the AI Era 46:31 Who Gets to Benefit From AI? 51:39 What Our Fear of AI Reveals 56:18 Listen Before You Think 01:03:07 The Debrief 📜 Read the transcript for this episode: Here! 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.

A year ago, Logitech had people experimenting with AI. Today, Eric says he can’t think of a single part of the company that isn’t building, exploring, or creating something with it. Eric shares what helped make that happen. There’s a Build Advisor that helps employees figure out what to build and connects them with people who may have already worked on something similar. AI in Action moments are now part of company and leadership meetings. And before leadership presents to the board, there’s an expectation that their work goes through an AI Board Advisor first. Henrik, Jeremy, and Eric also get into what comes next: how to measure whether all this AI activity actually creates value, why Eric built AI systems to manage his own information overload and sleep, and why creating something new should come with another question: what old report, process, or way of working can now disappear? Key Takeaways Embed AI into how work gets done AI becomes more valuable when it’s built into existing workflows and expectations, rather than simply made available for people to use. Make AI adoption visible and repeatable Logitech keeps AI present through AI in Action moments, leadership routines, office hours, shared Gems, and a 175-person volunteer Champions Network. Build resources that help people help themselves Tools like the Build Advisor give employees a place to start, surface work that already exists, and connect them with colleagues who have tackled similar problems. Ask what you can stop doing Eric argues that every new AI-enabled artifact should come with another question: what old report, process, or way of working can now disappear? Eric's website: porres.com/ Eric's LinkedIn: linkedin.com/eporres/ Logitech: www.logitech.com/ 00:00 Embedding AI Into the Workflow 00:52 Meet Eric Porres 01:15 The Cambrian Explosion of AI 06:20 Measuring the Value of AI 11:16 The Build Advisor 16:12 Keeping Up With AI 19:43 Making AI Part of the Culture 21:37 The AI Board Advisor 25:51 Building an AI Champions Network 29:15 Eric’s Personal AI Stack 32:17 The AI Vampire Problem 40:59 Building a Deep Memory 46:49 What Can AI Help You Delete? 55:19 The Debrief 📜 Read the transcript for this episode: Here! 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.

Most companies know how to innovate. Far fewer know how to scale innovation. Charles introduces the explore versus exploit framework, explaining why the same systems that help organizations succeed today can make them resistant to change tomorrow. As companies mature, they become better at serving existing customers, improving existing products, and optimizing existing processes. The harder question is how to create space for experimentation without undermining the business that already exists. Henrik, Jeremy, and Charles explore what this means in the age of AI. They discuss whether AI should be viewed as a substitute or a complement, why Microsoft's transformation under Satya Nadella succeeded, how Amazon has built exploration into its operating system, and why leaders don't create adaptable organizations through vision alone. They do it by shaping culture through incentives, systems, and the behaviors they reward. Key Takeaways: The biggest challenge isn't generating ideas. It's scaling them. Many organizations are good at innovation. The difficult part is giving promising ideas the support they need to grow. Great companies become trapped by what made them successful. The metrics, incentives, and culture that optimize today's business can make it harder to adapt to tomorrow's. Culture is built through systems. Leadership principles only matter when they're reflected in hiring, incentives, performance reviews, and everyday behavior. The goal isn't to predict the future. It's to discover it. The most adaptable organizations build processes that help them experiment, learn, and uncover new opportunities as the world changes. Charles' Stanford profile: stanford.edu/faculty/charles-oreilly 00:00 Intro: Why Companies Die Fast 00:36 Meet Charles O'Reilly 02:01 Explore Versus Exploit 03:50 AI Substitute Or Complement 06:51 Adaptability As Culture 09:19 Resistance To Change 12:32 Microsoft Culture Turnaround 15:25 Ambidexterity And Lifespans 18:20 Ideate Incubate Scale 20:10 Scaling Needs Separation 24:44 Amazon PRFAQ Machine 34:33 Rituals And Failure Signals 38:05 The Debrief 📜 Read the transcript for this episode: here! 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.

The conversation begins with an experiment that caught Jeremy's attention. Dan asked ChatGPT to tell him what his friends wouldn't. From questions about his blind spots to what people might say behind his back, some responses felt completely wrong, while others landed with surprising force. Rather than accepting every answer, Dan explains why the real value comes from wrestling with AI's perspective, not simply believing it. From there, Henrik, Jeremy, and Dan explore what it takes to use AI well. They discuss intellectual humility, prompting models to challenge rather than flatter us, and why AI works best as a sparring partner that exposes weaknesses in our thinking. The conversation also touches on taste, agency, and why, in a world where execution is becoming easier, discernment and original thinking become even more valuable. Key Takeaways: Use AI to make your thinking visible The best AI conversations don't just generate answers. They help you understand your own assumptions, reactions, and ideas more clearly. Treat AI as a sparring partner Challenge AI's responses, ask it to critique your work, and use it to strengthen your thinking rather than replace it. Taste is developed by creating As AI makes execution easier, judgment and discernment become more valuable. The best way to develop both is by creating, not just consuming. Agency depends on context People don't become more agentic through willpower alone. The right environment, with autonomy and room to take risks, makes initiative possible. Daniel's website: danpink.com LinkedIn: linkedin.com/danielpink AI Self-Reflection Prompts: danpink.com/ai-guide/ 00:00 AI as a Brutally Honest Advisor 00:32 Meet Daniel Pink 00:47 AI for Self-Knowledge 01:40 A Brutally Honest AI 06:10 What Do People Say Behind Your Back? 07:45 Should You Trust AI's Advice? 13:28 The Fear of Irrelevance 17:44 Intellectual Humility 20:04 AI as a Sparring Partner 24:35 Teaching AI to Think Like You 27:38 Why Taste Matters 29:59 Agency Starts with Context 37:08 A Future That's a Little Better 41:49 Nostalgia vs. Reality 45:18 The Debrief 📜 Read the transcript for this episode: Here! 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.

Geoff Woods returns to Beyond the Prompt to discuss the updated edition of The AI-Driven Leader and what has changed over the past 18 months. Rather than focusing on the latest AI models, Geoff argues that leaders need to use AI themselves before asking others to, using it to think more clearly, shape strategy, and make better decisions. The conversation explores why many organizations confuse access with adoption, why strategy should come before use cases, and how AI can change the way leaders approach everything from business strategy to organizational design. Along the way, Henrik and Jeremy challenge Geoff's ideas on authorship, judgment, and whether understanding AI changes what leaders believe is possible. Key Takeaways: Leaders need to use AI themselves Using AI personally is what qualifies leaders to shape strategy and lead others from practice rather than theory. Use AI to improve your thinking The biggest opportunity isn't automating work. It's using AI to think better, solve better problems, and imagine new possibilities. Start with problems, not use cases Begin with the biggest challenges facing the business, then use AI to rethink how to solve them. AI still needs human judgment AI can generate ideas, but people are still responsible for reviewing the output and standing behind it. Focus AI on your highest-value work Use AI to amplify the small set of activities where your human strengths create the greatest impact. The AI-Driven Leader: aileadership.com Geoff's LinkedIn: linkedin/geoff-woods 00:00 Are You Qualified to Lead on AI? 00:35 Meet Geoff Woods 00:54 The AI Slop Dilemma 05:48 Putting Your Stamp of Approval 09:19 What Changed in 18 Months 12:13 Access Isn't Adoption 15:50 Why Leaders Can't Delegate AI 20:09 Strategy Before Use Cases 22:02 BarkBox's AI Strategy 26:23 Reinventing Strategy with AI 33:11 Compressing Months into Hours 37:22 Human Skills as Superpowers 42:46 The Debrief 📜 Read the transcript for this episode: are-you-qualified-to-challenge-your-team-on-ai-with-geoff-woods-author-of-the-ai-driven-leader/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.

Christian believes the AI era will be defined less by generating outputs and more by evaluating them. As intelligence becomes cheaper and more accessible, the people who create the most value may be those who can distinguish good work from exceptional work and help guide increasingly capable systems. The conversation explores verification, judgment, and why expertise still matters in a world where AI can perform many tasks at a high level. Christian explains why today's experts are both highly valuable and simultaneously training the systems that may eventually replace parts of their work. Jeremy and Henrik also explore what this means at a personal level. They discuss building AI agents that reflect your own preferences, creating personal verification systems, and why AI may make it easier to learn new skills, switch careers, and pursue more ambitious ideas. Key Takeaways: Verification becomes more valuable as intelligence gets cheaper As AI makes generating outputs easier, the ability to recognize what is actually good becomes increasingly important. Experts are training their own replacements The people best positioned to verify AI outputs are also helping codify the expertise that trains future systems. Human value shifts from doing to directing As AI handles more execution, people create value through judgment, direction, and orchestration. Build your own verification system The best AI users are developing agents, workflows, and tools that reflect their own preferences and standards. Christian's LinkedIn: linkedin.com/ccatalini/ Christian's X: x.com/ccatalini Christian's website: catalini.com Economics of AGI: full paper Jeremy's Persona File Template: YouTube/The8Files 00:00 Non-Measurable Frontiers 00:32 Meet Christian Catalini 01:08 The Economics of AGI 03:09 Why Verification Matters 06:37 Can Everything Be Measured? 10:32 The Rise of the Verifier 14:35 When Intelligence Gets Cheap 21:46 Building Your Verification Harness 24:08 Human + AI Augmentation 30:18 Persona Files and Privacy 33:12 Reasons for Optimism 36:00 Career Switching in the AI Era 39:31 The Debrief 📜 Read the transcript for this episode: the-unexpected-economics-of-agi-with-christian-catalini-tech-founder-and-co-creator-of-libra/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.

Chantel Prat studies how different brains make sense of the world. Her work starts from a simple idea: every experience leaves a mark. The inputs we consume shape how we think, what we notice, and ultimately who we become. The conversation explores why people often choose familiar rewards over uncertain opportunities to learn. Chantel explains the tension between exploration and exploitation, why curiosity is essential for growth, and how fear can prevent us from engaging with new technologies like AI. They also discuss theory of mind, cognitive offloading, and what happens when we increasingly rely on AI for thinking. The goal is not simply to do better work, but to use AI in ways that help us become better versions of ourselves. Key Takeaways: Curiosity requires safety When people feel threatened, they become defensive rather than exploratory. Fear gets in the way of learning. Better inputs create better outputs Every experience leaves a footprint on the brain. The ideas, conversations, and information we consume shape how we think and who we become. We naturally favor certainty over exploration Our brains are biased toward familiar rewards, even when something new may offer greater long-term value. Curiosity starts with admitting you might be wrong Learning requires recognizing that you do not already have the answer. Without that openness, exploration never begins. Use AI to become better, not just produce more The most important question is not what AI can do for you, but what you still want to get better at yourself. Chantel Prat: linktr.ee/chantelprat The Neuroscience of You: The-Neuroscience-of-you/book 00:00 Curiosity Versus Threat 00:31 Meet Chantel Prat 01:02 Why Input Shapes Brains 04:08 The Output Pressure Trap 05:52 Exploration Versus Exploitation 10:05 Average Brains And Teams 15:35 Theory Of Mind Defined 22:12 Practicing With AI Feedback 24:31 Offloading Thinking To AI 29:50 Humans In The Loop 35:16 Age And Tech Reactions 42:15 Why Curiosity Requires Safety 48:15 Personal Codex And AI 50:54 Becoming More Yourself 54:34 The Debrief 📜 Read the transcript for this episode: why-fear-kills-curiosity-and-what-that-means-for-ai-with-chantel-prat-cognitive-neuroscientist/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.

Eric Ries, author of The Lean Startup and the newly released Incorruptible , joins Beyond the Prompt to explore why most companies drift from their original mission over time. The conversation dives into governance, shareholder primacy, Anthropic’s unusual structure, and why AI makes these questions more important than ever. Eric Ries argues that most companies are built on a contradiction. Founders say they care about customers and impact, but legally, the company is structured to serve shareholders first. Over time, that mismatch tends to win. The conversation explores what that looks like in practice, why it is so hard to fix, and how a small number of companies have tried to design around it from the beginning. Eric reflects on advising Anthropic in its earliest days and what it actually takes to protect a mission as a company scales. A big part of the discussion is how governance gets treated as a legal formality when it is really a design problem. In the age of AI, Eric argues that the principles baked into a company’s structure early on may determine whether it stays true to its mission or slowly drifts away from it. Key Takeaways: Mission drift is often built in from day one Founders may say they care about customers and impact, but legally the company is structured to serve shareholders first. Over time, that mismatch tends to win. Governance is one of the highest leverage founder decisions If the structure is misaligned early on, founders can lose control of the company and its mission no matter how strong the original vision was. The system is stacked against mission-driven founders Even well-intentioned founders operate inside structures designed to prioritize short-term shareholder returns. Most do not realize it until it is too late. “Why not try?” is more powerful than it sounds Eric’s argument is not that fixing governance is easy. It is that most founders never even ask the question. AI makes this more urgent than ever As AI systems act more autonomously, the principles built into a company early on will shape whether it stays true to its mission or drifts away from it. Eric's new book: Amazon: Incorruptible Website: incorruptible.co Socials: X: x.com/ericries LinkedIn: linkedin.com/ericries The Lean Startup: theleanstartup.com 00:00 Mission vs Shareholder Value 00:32 Meet Eric Ries 01:37 Why Anthropic Needed Governance 06:47 The Long-Term Benefit Trust 10:00 Why Great Companies Drift 13:47 From Lean Startup to Incorruptible 18:14 Is It Too Late To Fix? 23:06 Governance As A Superpower 25:48 The Lies Founders Tell Themselves 28:49 The Rise Of Shareholder Primacy 33:09 The Unaccountability Machine 35:51 Profit vs Human Flourishing 37:24 The ROI Trap 38:26 The H-E-B Loyalty Story 41:14 Principles Beyond Metrics 42:54 AI, Thick Data, And Human Judgment 46:43 The Debrief 📜 Read the transcript for this episode: why-your-favorite-brand-stopped-caring-about-you-eric-ries-author-of-the-lean-startup/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.

Ryan Holiday argues that while AI can generate outputs, it cannot generate wisdom. Drawing on a story from Seneca about a Roman who used educated slaves to sound intelligent, he compares outsourcing thinking to outsourcing exercise: the value comes from becoming the kind of person who can do the work, not simply producing the answer. The conversation explores the difference between useful cognitive offloading and surrendering judgment entirely. Ryan explains that while tools like GPS may replace navigation skills without much consequence, writing, decision-making, and critical thinking shape the person on the other side of the process. AI, he argues, tends to amplify existing tendencies. People satisfied with mediocre work will settle faster, while people pushing for exceptional work can use AI to refine and challenge their thinking. Throughout the episode, Stoicism serves as a counterweight to both panic and hype. Change and uncertainty are constants throughout history, not exceptions. Ryan reflects on leadership, family, adaptability, and skepticism, arguing that in a world where AI can confidently produce both insight and nonsense, the ability to question, verify, and think independently becomes increasingly valuable. Key Takeaways: You cannot outsource wisdom AI can generate answers, but judgment and understanding still come from doing the work yourself. AI amplifies who you already are People who settle for mediocre work will do so faster with AI. People who push for better work can use it to deepen and refine their thinking. Bullshit detection is becoming a core skill As AI produces increasingly convincing answers, skepticism and verification become essential. Change is not new The Stoics viewed uncertainty and disruption as constants of human life. AI may feel unprecedented, but humans have always had to adapt to major change. Agency matters more than ever You cannot control technological change, but you can control how you respond to it and how you choose to use it. Ryan's website: ryanholiday.net Daily Stoic: dailystoic.com/podcast/ 00:00 Intro: You Can’t Outsource Wisdom 00:29 Meet Ryan Holiday 02:03 The Dream Was To Work Less 03:07 Who Actually Gets The Time? 06:32 Leadership, Culture, And Family First 08:38 How Will You Measure Your Life? 10:11 The Stoic View Of Change 14:44 AI Hallucinations And Shameless Confidence 17:21 You Cannot Outsource Wisdom 19:08 Cognitive Offloading Vs Real Understanding 20:22 Ego, Flattery, And AI 22:52 AI As Editor And Thought Partner 24:59 Mediocre Vs Exceptional Work 31:15 Why Bullshit Detection Matters 38:06 Stoicism, Agency, And Adapting To Change 43:31 The Debrief 📜 Read the transcript for this episode: you-cant-outsource-wisdom-bestselling-author-ryan-holiday-on-what-the-stoics-have-to-say-about-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.

Laura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works. She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign. The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what’s generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer. Key takeaways: Mediocre is easier than ever, which raises the bar for originality When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher. Brand becomes trust when products converge As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that. Only do what only you can do Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view. Need-finding still requires real people Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did. Human plus bot plus human Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process. Instacart: instacart.com Super Bowl ad: Super Bowl (Instacart ad) Laura LinkedIn: linkedin/laurajones ro's post: ro.co/perspectives/super-bowl-economics 00:00 Intro: Originality vs AI Complacency 00:27 Meet Laura Jones 01:23 Brand as trust when products converge 03:50 Personalization and reducing mental load 06:24 What still matters in marketing 10:33 Why need-finding cannot be shortcut 14:09 Using AI without losing judgment 16:33 New channels and where customers actually are 21:35 Why “dopey ideas” matter 25:42 Human plus bot plus human 28:44 Inside the Super Bowl ad 31:47 From banana insight to product 34:49 Taking creative risks at scale 37:34 Fear, pressure, and team chemistry 46:24 AI and faster prototyping 53:26 The debrief 📜 Read the transcript for this episode proof-of-craft-what-it-takes-to-stand-out-when-everything-looks-good-with-laura-jones-cmo-of-instacart/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.

Dan Klein, professor at UC Berkeley and CTO at Scaled Cognition, explains that AI systems generate answers based on patterns in language rather than verified knowledge. This makes them highly capable across many tasks, but also means they can produce confident answers even when they are not fully accurate. He introduces the “jagged frontier,” where AI performs very well in some areas and less reliably in others. Because responses are fluent and convincing, it is often hard to see where those limits are, which makes it important to stay engaged when using these systems. The conversation also explores hallucinations as a natural part of generative systems. In some cases, this is what makes them valuable, especially for creative or open-ended tasks, while in other cases reliability becomes more important. Finally, Dan highlights that working effectively with AI is a skill. As more people start using these systems in their daily work, knowing how to guide them, evaluate outputs, and apply them in the right contexts becomes increasingly important. He also shares how his team at Scaled Cognition is tackling this challenge by building AI systems with fundamentally different architectures, focused on determinism and reliability — aiming to ensure systems follow rules, reflect underlying data accurately, and behave predictably in high-stakes, policy-driven use cases. Key Takeaways: AI is designed to sound right, not to know it’s right Models generate fluent answers without knowing whether they are correct, which means users need to actively evaluate outputs You have to learn where AI works and where it doesn’t Capabilities are uneven, and understanding those limits is key to using AI effectively Working with AI shifts your role from creator to editor Instead of starting from scratch, you are reviewing, refining, and validating what the model produces Most people are using AI without knowing how to manage it Skills like delegation, verification, and judgment are becoming essential, but are not widely taught Dan's LinkedIn: linkedin/dan-klein/ Scaled Cognition Website: scaledcognition.com Scaled Cognition LinkedIn: linkedin/company/scaledcognition/ Scaled Cognition X: x.com/ScaledCognition 00:00 Intro: Fluency vs Truth 00:34 Meet Dan Klein 02:53 Why Fluency Misleads 05:11 How LLMs Guess 07:30 What Is Hallucination 08:54 Deception and Alignment 11:22 Why Agents Break 12:48 Chaining and Determinism 16:01 When Hallucination Helps 22:33 Beyond Scale for Reliability 30:40 Synthetic Data Training 31:10 Enterprise Agent Use Cases 33:44 Healthcare Risks 39:13 Enterprise Literacy Gap 41:27 Delegation and AI Management 54:37 The Debrief 📜 Read the transcript for this episode: nobody-is-getting-new-manager-training-for-their-ai-team-with-dan-klein-uc-berkeley/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.

Melissa Cheals leads Smartly, a payroll and people management platform serving 24,000 small and medium businesses in New Zealand. In this conversation, she shares how AI is reshaping product development, leadership, and how organizations operate. A key moment comes when her team estimates new features will take 12 months and $1M to build. Instead of accepting it, Melissa pushes back, using AI to better understand her team’s perspective and communicate the need for change more effectively. This becomes a broader shift in how she approaches leadership, using AI to think more clearly and navigate conversations with less friction. The discussion expands into strategy. Companies now face a fundamental choice: become AI-native or continue building on existing systems. As AI adoption increases, it also exposes silos and bottlenecks. Melissa shares why cross-functional collaboration—and leaders actively engaging with AI themselves—is critical to navigating this shift. Key Takeaways: Becoming AI-native is a defining decision It’s not just a technology shift. Leaders need to decide whether to rebuild around AI or continue layering it onto existing systems, and that choice shapes how the company operates. AI shifts us from scarcity to abundance Many organizations still think in terms of limited time and resources, but AI changes what’s possible and forces leaders to rethink how big they can think and what they can achieve. AI is a leadership amplifier Beyond productivity, AI helps leaders think more clearly, reframe conversations, and communicate change in a way that is both effective and respectful. Leaders can’t delegate AI Without hands-on experience, it becomes difficult to challenge assumptions, guide teams, or make informed decisions about what’s possible. Smartly: smartly.co.nz LinkedIn Melissa: linkedin.com/melissa-cheals LinkedIn Smartly: linkedin.com/company/smartlynz/ 00:00 Intro: Challenging AI Assumptions 00:28 Meet Melissa Cheals 01:17 The Spark For Change 02:36 Vision And Early Signals 03:48 Hiring For Transformation 06:12 Unlocking Data With AI 08:27 Breaking Silos Across Teams 10:39 Why Leaders Must Learn AI 13:42 Leading With AI And Clarity 17:05 The AI-Native Decision 21:45 Thinking Bigger With AI 25:23 Less Meetings More Writing 26:33 The Self-Disruption Imperative 29:11 Breaking Silos With Value Streams 31:28 Managing Fear And Change 32:50 Learning And Shipping Faster 34:58 Debrief 📜 Read the transcript for this episode: ai-native-or-not-the-defining-choice-for-companies-right-now-with-melissa-cheals-ceo-of-smartly/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.

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 Chaos 00:31 Meet Greg Shove 01:32 Enterprise AI Is a C Minus 01:51 AI’s ROI Is “Leaking” to Employees 03:04 When Individuals Outrun the Organization 05:44 When AI Breaks Workflows 06:47 Disposable Software and New Ways of Building 09:10 Cut vs Create 12:01 Using the Calendar as a Lever 16:24 Why Enterprises Don’t Move 17:32 When Customers Force Change 21:31 AI Breaks Capability Boundaries 25:44 The Productivity Firehose 27:49 Who Actually Captures the Value 28:45 Why Everyone Needs Good AI 32:00 Adoption Beats Buying More Tools 40:17 Teaching the 90 Percent 43:48 Where Humans Still Matter 48:09 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/werdelin Jeremy: https://www.linkedin.com/in/jeremyutley Show edited by Emma Cecilie Jensen.

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.