Unlearn

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1. The Leadership Behaviors to Best Govern AI with Cassandra Pratt and John Marcante

   28 oktober 2026 · 39 min

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   AI capabilities are advancing quickly, but changing how people work takes time. Leaders need to give teams the confidence to experiment while protecting sensitive information and keeping responsibility clear. How they manage that balance shapes whether AI leads to better business outcomes. Lees meer

   In this episode, Cassandra Pratt, CHRO at Progyny, and John Marcante, former Global CIO of Vanguard, join me to explore how governance can support that change. Cass shares lessons from Progyny’s internal HR tool, Lumi, and efforts to redesign workflows while building employee trust. John recalls how a Vanguard team improved website conversion by listening to clients and simplifying their choices. He connects that experience to governing AI through clear business outcomes, accountability, and oversight matched to risk.

   This is the second podcast in The Adaptive Enterprise, our five-part article and podcast series on redesigning work, governance, and leadership for the age of AI.

   Key Takeaways

   Start with a business outcome: Define the problem you want to solve before choosing how to use AI. John’s Vanguard example shows how customer research and simpler choices helped address a conversion problem that previous technology investment had not resolved.

   Design governance into the whole workflow: Bring the relevant business, risk, and technical partners together early. Cass explains why understanding the data and the full process makes it easier to establish appropriate controls.

   Build trust through visible action: Explain how information is handled and show what happens when work is automated. Cass describes employees moving into a new member services area after automation, giving them evidence that leadership would follow through on its commitments.

   Put recovered capacity toward what matters: At Progyny, reducing manual work can give people more time to listen to and support members. Judge progress against the business outcome and the experience you want to improve.

   Match over…

2. Why AI Adoption Is No Longer Enough to Create Business Value with John Marcante

   14 oktober 2026 · 34 min

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   Making technology faster does not automatically make the business better. Lees meer

   John Marcante spent 28 years at Vanguard, including serving as Global CIO and leading its high-net-worth and advice business. As Deloitte’s U.S. CIO-in-Residence, he works with senior technology leaders navigating change.

   In our conversation, John and I explore why AI investment needs to change how work gets done. John shares what happened when Vanguard’s cloud journey made IT faster than the business could absorb, and how shifting to cross-functional teams and shared outcomes helped address that gap. We also discuss human judgment, the anxiety people feel when familiar tasks are automated, and how leaders can direct freed-up time toward work that matters.

   This is the first podcast in The Adaptive Enterprise, our five-part article and podcast series on redesigning work, governance, and leadership for the age of AI.

   Key Takeaways

   Redesign work around new capabilities: Speeding up one step in a process can leave the overall business unchanged. Leaders need to bring people together to rethink the work from end to end.

   Align teams around a business outcome: At Vanguard, engineers and business colleagues needed a shared definition of success. Measuring each function separately made it harder to improve the result for clients.

   Decide how recovered capacity will be used: Time saved through automation creates an opportunity. Leaders are responsible for helping people use that time where judgment and expertise can improve outcomes.

   Help people redefine their value: As portfolio activities became automated, Vanguard advisors could spend more time helping families navigate important life decisions. John describes how client loyalty became the goal guiding that shift.

   Build a continuous cycle of adaptation: The Adaptive Enterprise Loop connects emerging capabilities, work redesign, outcome measurement and governance, and learning. Organizations need to repeat this cycle as new possibilities emer…

3. When AI Makes Development Faster, What Should You Build with Ciaran Cosgrave

   30 september 2026 · 42 min

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   When building gets easier, choosing what to build becomes even more important. Lees meer

   Ciaran Cosgrave is CEO of Nearform, a global AI-native engineering company for the enterprise. Before moving into entrepreneurship and technology, he spent the first decade of his career running refugee camps across Africa and Central America. Those experiences shaped how he thinks about resilience, problem-solving, and building teams.

   In our conversation, we explore why Ciaran focuses on problems before technology, how AI software development is compressing months of prototyping into days and weeks, and why faster experimentation makes judgment even more important. We also get into the new challenge leaders face when teams can generate more ideas and prototypes than organizations can realistically absorb.

   Key Takeaways

   Start with the problem, not the technology: Understanding the underlying problem and speaking with the people experiencing it creates a stronger foundation than chasing technology or quick financial returns.

   Knowing what to build remains difficult: AI can change how software gets built, but leaders still need judgment to determine which problems matter and what is worth creating.

   Faster prototypes can improve judgment: Moving from idea to prototype in days or weeks gives teams more opportunities to explore alternatives and test assumptions before committing to large investments.

   More experimentation requires better filtering: As testing ideas becomes faster and cheaper, leaders need mechanisms for deciding which prototypes deserve to move forward.

   Growth is becoming less dependent on headcount: AI is challenging the traditional services model where growing a business was closely tied to adding more people.

   Additional Insights

   Resilience can come from unexpected experiences: Ciaran credits his years working in refugee camps with teaching him perseverance and giving him perspective when facing rejection and uncertainty.

   Great teams still matter: Ciaran emphasizes…

4. From SaaS Scale to AI Startups: What We Had to Unlearn to Build Again with Sam Kroonenburg

   16 september 2026 · 43 min

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   Building AI startups can make past success a liability when it convinces you that the way you built before is the way you should build next. Lees meer

   Sam Kroonenburg knows what it feels like to build at scale. He co-founded A Cloud Guru, helped grow it into a business serving millions of learners, and eventually sold the company to Pluralsight. Now, as CEO and co-founder of Cuttable, he’s back at the beginning: finding customers, testing assumptions, shipping imperfect products, and learning how different company-building looks in the AI era.

   In our conversation, we explore what Sam had to unlearn when he moved from running a 600-person company back into an early-stage startup. We get into why Cuttable walked away from nearly $1 million in annual recurring revenue, how enterprise customers can pull a young company away from the product it actually needs to build, and why AI is shifting product and engineering teams from owning parts of a system to owning measurable outcomes.

   Key Takeaways

   Past success can distort your judgment: Starting Cuttable reminded Sam that reputation means little if the product does not solve a problem customers will pay for.

   Experience does not transfer automatically: Sam could trust his instincts when building for engineers, but entering advertising required him to test assumptions and learn from customers.

   Revenue can hide the wrong product: Cuttable reached nearly $1 million in ARR while engineers were still manually doing work the product was supposed to automate.

   Taking the long view can require a reset: Sam and his co-founders released every customer, returned to zero revenue, and rebuilt around end-to-end automation.

   AI shifts the focus from tasks to outcomes: Instead of teaching AI to copy a human process, teams should define the desired result and let AI determine how to achieve it.

   Additional Insights

   Speed can be an advantage over scale: A Cloud Guru prepared courses before AWS certifications launched, then updated them using re…

5. Building Better Products Through Better Product Operations with Denise Tilles

   2 september 2026 · 43 min

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   AI can accelerate a strong operating model, but when decision rights, incentives, or data are already unclear, it can make the mess spread faster. Lees meer

   In this episode, I sit down with Denise Tilles, a leading voice in product operations, to unpack how her career moved from editorial work at Condé Nast into product management, commercial leadership, and eventually product operations. Denise shares how learning to work with revenue data, product analysts, and operating models changed the way she thought about product leadership and led to her work helping enterprise organizations make faster, better-quality decisions.

   We explore what product operations actually does, why an operating model needs to define how decisions get made, and where incentives can quietly undermine even a well-designed process. We also dig into what happens when AI makes producing documents, specifications, and analysis nearly effortless: generating more output doesn't remove the work of judgment. In many cases, it makes clarity about inputs, outputs, ownership, and what “good” looks like even more important.

   Key Takeaways

   Product ops should improve decision-making: Denise defines it around business and data insights, customer and market insights, and the operating model.

   Commercial context changes product thinking: At Cision, learning P&L, ACV, and recognized revenue helped Denise connect product decisions directly to business outcomes.

   Good analysis reveals hidden opportunities: A product analyst uncovered an add-on opportunity that generated roughly $1 million within a year.

   Operating models need clear decision rights: Teams need to know what gets worked on, who decides, and how work actually gets done.

   AI amplifies the system already in place: If ownership, data, or processes are unclear, AI can make those weaknesses spread faster.

   Additional Insights

   Informal decisions can override formal processes: Denise learned that hallway conversations and executive requests often mattered more …

6. Redesigning Leadership for the Age of AI Agents with Tatyana Mamut

   19 augustus 2026 · 47 min

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   AI is forcing us to rethink a question most organizations have avoided for years: what is uniquely valuable about human work when intelligence itself is no longer scarce? Lees meer

   Tatyana Mamut, CEO of Wayfound and former CPO of Nextdoor, joins me to explore AI adoption through a lens that goes beyond software. Drawing on her background in anthropology, economics, organizational design, and product leadership, Tatyana explains why the real challenge is designing the rules, tools, norms, incentives, and relationships that shape how humans and AI agents work together.

   We get practical about what this means at both an individual and organizational level. We explore the human capabilities that become more valuable as AI takes on more routine mental work, why fear leads companies into bad AI decisions, how poorly designed incentives can push agents toward unexpected behavior, and why the subject-matter experts closest to the work need to become responsible for managing the agents operating alongside them.

   Key Takeaways

   Human comparative advantage is changing: As AI takes on more mental work, people need to rethink where their uniquely human value comes from.

   Develop taste, judgment, relationships, and performance: Tatyana sees these as practical areas where people can complement AI rather than compete with it.

   AI adoption is an organizational design problem: Success depends on redesigning rules, tools, norms, incentives, and accountability around the technology.

   Goals and guardrails can still produce the wrong behavior: An agent may follow its objective while still acting against the organization’s intent, as Tatyana’s refund example shows.

   Subject-matter experts need to become agent managers: The people closest to the work must be able to monitor, correct, and improve the agents operating in their domain.

   Additional Insights

   Organizational culture can be consciously designed: Tatyana frames culture as the interaction of rules, tools, and unwritten norms that shape beh…

7. Exclusive Audiobook Preview for Artificial Organizations

   22 juli 2026 · 46 min

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   Before leaders can redesign their organizations with AI, they have to reconsider how they personally think, work, and make decisions. Lees meer

   In this special episode of Unlearn, Barry O’Reilly shares the opening chapter of the audiobook edition of Artificial Organizations, narrated in his own voice. After hearing from readers who wanted another way to experience the book, Barry spent four days in the studio bringing its ideas and stories to life. The process was rewarding, demanding, and personal. As someone who is dyslexic, reading every word aloud required a different kind of focus from delivering a keynote, teaching a workshop, or hosting a podcast conversation.

   Barry then takes listeners into the central argument of the book: organizations often begin AI adoption with licenses, pilots, and tools while leaving leadership behavior and decision-making systems unchanged. Drawing from his own experiments, leadership research, and work with senior teams across North America, Europe, and Asia, he explains why meaningful AI transformation starts with human traits, high-value tasks, and the judgment leaders must preserve before selecting technology.

   Key Takeaways

   AI should strengthen judgment, not simply increase output: Machines can process and synthesize information at a scale no individual can match, but leaders still have to decide what matters. The opportunity is to use AI to prepare, capture, and pressure-test thinking so human attention stays focused on consequential decisions.

   Start with traits, then tasks, then tools: Barry’s 3T model begins with how a leader naturally thinks, creates, communicates, and decides. Only after identifying the tasks where that judgment creates the most value should leaders choose tools to support the work.

   Decision velocity must be paired with decision advantage: Decision velocity is how quickly a leader moves from question to insight, decision, and action. Decision advantage is the quality and depth of context behind that choice; spe…

8. Why AI Makes Great Systems Matter Even More with Eric Baxley

   8 juli 2026 · 36 min

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   AI is making it easier than ever to build products, automate work, and scale ideas. The challenge is no longer access to technology. It's designing the systems, stories, and customer understanding that turn AI into real business outcomes. Lees meer

   In this episode of Unlearn, I'm joined by Eric Baxley, Chief Marketing Officer at Nobody Studios. Eric shares how a seventh-grade summer teaching himself to program on an Atari 800 sparked a 30-plus year career spanning software development, product management, marketing, sales, business development, and partnerships.

   We explore what Eric has had to unlearn while building companies in the AI era—from challenging assumptions before execution and replacing corporate polish with authentic storytelling, to designing scalable systems instead of disconnected tools. Along the way, Eric explains why great marketing still starts with deeply understanding customers, and why AI works best when it amplifies human judgment rather than replacing it.

   Key Takeaways

   Challenge the foundation before execution: Eric shared that in a previous large business, the team was “doing things right,” but not “doing the right things.” The segmentation was off, and correcting who they were really going after helped the team hit its goals for the next three years.

   Authentic stories build trust: Eric has had to unlearn the corporate habit of making everything polished before sharing it. He believes people respond when you show the journey, talk about the hardships, and ask for feedback along the way.

   Systems matter more than scattered tools: Eric warned against building a set of siloed point products. In a venture studio building multiple companies at once, he said you need systems that can scale, especially with the speed of AI.

   Use AI to strengthen your thinking, not replace it: Eric described writing and revising a LinkedIn post himself before turning to AI. The post performed well, and his takeaway was that people could sense it was naturally written a…

9. Judgment in the Age of AI with John Cutler

   24 juni 2026 · 51 min

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   AI is changing how leaders think, decide, and work with their teams. But as John Cutler points out in this conversation, the real shift is not simply about faster answers or more productivity. It is about becoming more aware of the judgment systems we already use, often without noticing. Lees meer

   In this episode of the Unlearn Podcast, I’m joined again by John Cutler, product thinker, systems explorer, and Head of Product at Dotwork. We explore how AI can help leaders expose their thinking, pressure test decisions, and build stronger team judgment, while also making it easier to accelerate poor habits, shallow work, and false confidence.

   John shares practical examples from product prioritization, survey design, objection handling, and team collaboration to show where AI can genuinely improve decision quality. We also get into the tradeoffs: why AI can make work feel like “hard mode,” why downtime still matters, and why intentionality is becoming one of the most important leadership skills in this moment.

   Key Takeaways

   AI exposes how leaders make decisions: AI tends to amplify the decision system already there. When a leader’s thinking is clear, AI can help make it visible and reusable; when it is vague, AI can make that vagueness move faster.

   Judgment is built differently depending on the situation: John explains that some judgment comes from repetition and tacit pattern recognition, while other judgment develops through coaching, discussion, and working alongside people with more experience.

   AI can help turn intuition into something teams can use: John’s example of documenting his product prioritization heuristic shows how AI can help make internal judgment concrete. The value comes from helping others understand why certain decisions matter, not just what the decision is.

   Better AI use starts with knowing what you know: John contrasts product prioritization, where he has deep experience, with survey design, where he knows there is established expertise to draw from…

10. Unlearning Executive Judgment: Building Decision-Making Muscles in the Age of AI with Jim Highsmith

    10 juni 2026 · 40 min

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    Jim Highsmith has been thinking about decision-making for a long time. When he wrote Agile Project Management in 2004, he went looking for practical guidance on decision-making in the project management literature and found very little. That gap matters even more now. Lees meer

    In this episode, Jim and I talk about why AI raises the stakes for executive judgment. AI can remove friction, speed up work, and take on repeatable tasks, but it can also make it easier for leaders to stop practicing the very capabilities they are paid to use. Jim brings this to life through John Boyd’s OODA loop, the risk of judgment atrophy, mountaineering decisions, Rob Hall’s Everest threshold, Phil Knight’s pattern recognition at Nike, and a personal story from Jim’s own time leading a collaborative project team at Nike.

    This conversation is really about how leaders build judgment deliberately: by making consequence-bearing decisions, setting thresholds before pressure arrives, creating space for slow thinking, and reflecting honestly on how decisions were made.

    Key Takeaways

    AI can weaken judgment when leaders stop practicing it: Jim compares the risk to driving an autonomous car: the more the system takes over, the less sharp the driver becomes. AI can remove low-value effort, but leaders still need to practice making consequence-bearing decisions.

    The OODA loop is mostly about orientation: Jim explains that John Boyd’s edge was not just speed, but his ability to update his mental model quickly. For leaders, the real work is noticing when old assumptions no longer fit the situation.

    Capability is knowledge plus experience plus judgment: AI can make knowledge easier to access, but it cannot replace the experience of carrying consequences. Judgment develops when people make real decisions, reflect on the outcome, and adjust how they think.

    Thresholds only work when enforced under pressure: Jim uses Rob Hall’s Everest story to show why decision thresholds matter before emotion, ambition, o…

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