Advertise on podcast: Unlearn

Making technology faster does not automatically make the business better. 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 TakeawaysRedesign 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 emerge. Additional InsightsTechnology foundations still matter: John cautions that organized data, modern platforms, and addressing technical debt remain important. Work redesign does not remove the need for those foundations.Technical capability and organizational influence are different: John describes a gap between technology leaders’ confidence in scaling AI and their confidence in changing how the business operates.Leaders need to model the change themselves: Barry shares how Progyny CEO Pete Anevski experimented with new ways of working and openly discussed what he was learning, helping others feel able to experiment.Humility helps reduce learning anxiety: John emphasizes honest communication and vulnerability when people are uncertain about their roles. Leaders do not need every answer before showing how they are learning.Stay close to the people doing the work: John moved his office onto the advisor floor to listen to client conversations, understand concerns, and help connect the change to what mattered for clients. Episode Highlights00:00 – Boards Are Asking Where the AI Value Is John opens with the gap between AI adoption and business value, and the questions boards are asking about their investment. 01:41 – Introducing The Adaptive Enterprise Series Barry introduces John and explains how their conversations grew into five articles and five companion podcasts. 02:54 – What Should Change Because of AI? Barry frames the leadership challenge around what new capabilities make possible for work. 05:30 – When IT Gets Faster Than the Business John recounts Vanguard’s cloud journey, the bottleneck that shifted into the business, and the move to cross-functional product teams. 07:50 – Business and Technology Need to Move Together Barry shares a lesson from American Airlines about involving the wider business in transformation. 09:45 – Scaling AI Without Changing the Operating Model John discusses why technical readiness does not necessarily give leaders the influence to change established ways of working. 11:07 – Redesign the Whole Process Barry explains how local productivity gains can leave end-to-end work unchanged, while John describes aligning teams around shared outcomes. 15:21 – When Automation Challenges People’s Roles Barry explores the anxiety people feel when tasks that have defined their work begin to change. 17:13 – What Will You Do With the Time You Free Up? John explains why efficiency alone is insufficient and why leaders need to direct capacity toward valuable work. 19:01 – Leaders Go First Barry shares how Progyny’s CEO modeled experimentation, and John discusses humility and vulnerability in leadership. 22:46 – Helping Advisors Redefine Their Value John describes moving the focus from portfolio mechanics and larger client books to client loyalty and important family decisions. 27:26 – The Adaptive Enterprise Loop John explains the continuous cycle of capabilities, work redesign, outcomes, governance, and learning. 30:50 – Where Executive Teams Can Start John offers practical starting points: rethink work, identify where judgment creates value, and align people around business outcomes. Useful ResourcesJohn Marcante on LinkedIn - https://www.linkedin.com/in/john-t-marcante/?isSelfProfile=false The Adaptive Enterprise Series: Article 1 - https://barryoreilly.com/explore/blog/how-ai-is-changing-work-and-leadership/Measure AI Outcomes survey - https://artificialorganizations.com/measure-ai-outcomesArtificial Organizations - https://geni.us/artificialorgsVanguard on LinkedIn - https://www.linkedin.com/company/vanguard/ Deloitte on LinkedIn - https://www.linkedin.com/company/deloitte/

When building gets easier, choosing what to build becomes even more important. 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 TakeawaysStart 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 InsightsResilience 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 finding good people, helping them work together, and building strong teams around difficult problems.Luck belongs in the entrepreneurship story: Timing, advice, unexpected opportunities, and good fortune can play an important role even when successful founders later describe a more deliberate strategy.Nearform was built around open source and small, senior teams: The company was built on a belief in open technology and in smaller groups of experienced people creating greater impact.The transformation experts are being disrupted too: After helping companies navigate mobile, cloud, and ecommerce, Nearform now finds its own services business directly affected by AI. Episode Highlights 00:00 – Solve Problems Before Chasing Money Ciaran explains why he has always been more interested in solving meaningful problems than pursuing technology or quick financial returns. 02:32 – Guest Introduction: Ciaran Cosgrave Meet Ciaran Cosgrave, CEO of Nearform, whose unconventional career began with a decade spent running refugee camps before moving into entrepreneurship and technology. 03:52 – From Refugee Camps to Technology Ciaran shares how he spent the first ten years of his career working in refugee camps before entering technology. 05:46 – Giving His Early Career to Others A teenage encounter with a technology entrepreneur inspired Ciaran to spend the early years of his career serving others and tackling difficult problems. 08:18 – Becoming an Entrepreneur by Accident After struggling to enter traditional corporate roles, Ciaran found an opportunity in financial services and started building technology businesses. 11:45 – Focus on Problems, People, and a Little Luck Ciaran explains why understanding the problem, finding great people, and acknowledging luck have shaped how he builds businesses. 15:43 – What Drew Ciaran to Nearform Ciaran describes Nearform’s origins and its early belief in open-source technology, small senior teams, and software as a force for good. 21:03 – When the Transformation Experts Get Disrupted After years of helping companies navigate digital transformation, Nearform now finds its own industry being reshaped by AI. 25:45 – Knowing What to Build Is Still Hard Ciaran argues that judgment about what to build remains one of the biggest challenges for organizations using AI. 29:38 – From Months of Prototyping to Days and Weeks AI-assisted development allows teams to move from ideas to prototypes much faster and examine more alternatives before committing. 33:20 – Making More Bets With Greater Optionality Barry explores how cheaper experimentation allows teams to test multiple possibilities instead of placing one large bet. 36:06 – When Innovation Becomes the Bottleneck Ciaran explains why organizations need stronger filtering mechanisms as teams generate and prototype more ideas. 39:18 – Turning AI’s Potential Into Reality Ciaran shares his optimism about putting AI into production and using it to build new services and tackle difficult problems. Useful ResourcesCiaran Cosgrave on LinkedIn - https://www.linkedin.com/in/ciarancosgrave/Nearform Website - https://www.nearform.com/Nearform on LinkedIn - https://www.linkedin.com/company/nearform/Artificial Organizations – https://geni.us/artificialorgsRead Artificial Organizations? Leave a review on Amazon – https://www.amazon.com/review/create-review/ref=cm_cr_othr_d_wr_but_top?ie=UTF8&channel=glance-detail&asin=1067626328 Follow Barry O’ReillyLinkedIn: https://www.linkedin.com/in/barryoreilly Website: https://barryoreilly.com Facebook: https://www.facebook.com/barryoreillyauthor/ X: https://x.com/barryoreilly Instagram: https://www.instagram.com/barryoreilly/

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. 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 TakeawaysPast 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 InsightsSpeed can be an advantage over scale: A Cloud Guru prepared courses before AWS certifications launched, then updated them using real exam feedback.Playfulness can strengthen a product: Short, entertaining lessons made cloud training easier to engage with and helped A Cloud Guru stand out.Paying customers provide stronger signals: Payment shows whether a problem matters enough to solve and makes the customer’s feedback more meaningful.AI is changing team ownership: Cuttable is moving from teams that own system components to teams accountable for measurable customer outcomes.Founder optimism needs a counterweight: Glitch Capital uses analysts and experienced operators to challenge assumptions and add rigor to investment decisions. Episode HighlightsEpisode Highlights00:00 – Episode Recap Sam reflects on starting again after a major exit and learning that customers care more about solving their problem than a founder’s reputation. 02:13 – Guest Introduction: Sam Kroonenburg Meet Sam Kroonenburg, CEO and co-founder of Cuttable, co-founder of A Cloud Guru, and founder investor at Glitch Capital. 03:29 – The Moment Technology Clicked Sam traces his entrepreneurial journey back to receiving his first computer and learning to code through an online pen pal. 09:11 – Winning Through Speed and Play Sam explains how A Cloud Guru used informed bets, rapid updates, and playful learning experiences to move faster than larger organizations. 12:26 – Starting Again and Unlearning Founder Instinct Moving from a 600-person company to an early-stage startup forced Sam to test his assumptions and relearn how to earn customer trust. 17:51 – Running a Startup Like a Large Company Sam initially brought too much structure, reporting, and sales pressure into Cuttable before refocusing on product-market fit. 20:56 – Why Cuttable Went Back to Zero After reaching nearly $1 million in ARR, Cuttable released every customer and rebuilt around end-to-end automation. 27:48 – Why Paying Customers Change the Signal Sam explains why payment reveals whether a problem matters enough to solve and makes customer feedback more meaningful. 29:43 – Stop Teaching AI to Work Like a Human Cuttable learned to give AI the available data and desired outcome instead of forcing it to follow a prescribed human process. 34:45 – Organizing Teams Around Outcomes Sam explores why engineers focused on customer impact often embrace AI and how Cuttable is organizing teams around measurable outcomes. 38:41 – Why Founder Optimism Needs a Filter Sam explains how Glitch Capital balances founder optimism with analysts and experienced operators who challenge assumptions. 42:03 – Smaller Teams and Bigger Impact Sam shares his optimism that AI will help smaller teams create impact at a scale that once required much larger organizations. FAQsQ1. What did Sam Kroonenburg have to unlearn after building A Cloud Guru?Sam had to unlearn the confidence and operating habits that came with previous success. At Cuttable, he found that reputation could not replace customer value, evidence, or the search for product-market fit. Q2. Why did Cuttable give up nearly $1 million in annual recurring revenue?Enterprise demands were pulling Cuttable toward custom work and manual intervention. When engineers began generating and fixing campaigns themselves, the team released those customers and rebuilt around end-to-end automation. Q3. How does Sam Kroonenburg think AI changes product development?Sam believes teams should spend less time telling AI how to perform a task and more time defining the desired outcome. AI can then use the available data to determine the best method. Q4. Why does Sam prefer feedback from paying customers?Payment shows that a problem matters enough to solve. Free users can offer feedback without genuinely valuing the product, while paying customers provide a stronger signal. Q5. How is AI changing the way Cuttable organizes engineering teams?Cuttable is organizing teams around measurable customer outcomes instead of software components. Teams can use AI and evaluation systems to improve those outcomes continuously. Useful ResourcesCuttable — Sam Kroonenburg’s AI-powered creative production system for advertising.A Cloud Guru — The cloud computing education company co-founded by Sam and later acquired by Pluralsight.Glitch Capital — The Australia and New Zealand founders fund Sam started with fellow founders and operators.AWS re:Invent — Discussed in Sam’s story about how A Cloud Guru prepared training for new AWS certifications at launch speed.Square Peg — Cuttable’s investor, whose response to the company’s decision to reset revenue reinforced Sam’s understanding of seed-stage investing.Artificial Organizations — https://geni.us/artificialorgsRead Artificial Organizations? Leave a review on Amazon - https://www.amazon.com/review/create-review/ref=cm_cr_othr_d_wr_but_top?ie=UTF8&channel=glance-detail&asin=1067626328 Follow Barry O'Reilly:LinkedIn: https://www.linkedin.com/in/barryoreilly Website: https://barryoreilly.com Facebook: https://www.facebook.com/barryoreillyauthor/ X:a href="https://x.com/barryoreilly" rel="noopener...

AI can accelerate a strong operating model, but when decision rights, incentives, or data are already unclear, it can make the mess spread faster. 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 TakeawaysProduct 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 InsightsInformal decisions can override formal processes: Denise learned that hallway conversations and executive requests often mattered more than the documented workflow.Proof does not create authority: Better analysis can earn credibility, but changing a system still requires ownership, sponsorship, and permission.Incentives shape behavior: Product and sales can both act rationally while optimizing toward conflicting measures of success.AI can shift work downstream: Faster artifact creation still leaves someone responsible for checking the reasoning, evidence, and assumptions.Operations may become more connected: Denise sees product ops, design ops, sales ops, and other functions moving toward a more unified “Omni Ops” model. Episode Highlights00:00 - Episode Recap Denise explains why the starting point for operating-model and AI work should be the pain a company is actually experiencing, from PRD structure to data quality, rather than adopting AI simply because the technology is available. 02:01 - Guest Introduction: Denise Tilles I introduce Denise Tilles and her work in product operations and operating models, setting up our discussion about data, decision-making, incentives, and how product organizations can operate more effectively. 03:13 - From Editor to Product Manager Denise traces her move from media and content strategy at Condé Nast into product management, a role she initially had to define for herself because the discipline was still relatively young. 04:50 - Learning the Economics of Product Moving to Cision gave Denise access to commercial data she had never had before, pushing her to learn from the CFO and understand product through revenue, P&L, ACV, and business outcomes. 08:41 - The Analyst Who Changed the Team Denise explains how hiring a product analyst gave her team more objective insight into customer and product data, including an overlooked opportunity that generated roughly $1 million in its first year. 12:55 - Finding Revenue Hidden in Behavior I share how an analyst at Lastminute.com identified a collapse in same-day booking conversion after 7 p.m., giving the team a specific customer behavior to investigate and improve through experiments. 15:14 - The Three Pillars of Product Ops Denise defines product operations through business and data insights, customer and market insights, and the operating model, all designed to help product managers make faster and better-quality decisions. 17:01 - The Process You Don't See Denise describes discovering that documented processes were often competing with informal conversations and executive requests, revealing why operating models need to account for how decisions really get made. 19:59 - Who Actually Gets to Decide? At its core, Denise says an operating model defines what a company works on, who has the right to decide, and how the work gets done within the organization's real constraints. 24:41 - Operating Models Need Owners Denise warns against treating an operating model as something you publish once and forget, because without clear ownership, maintenance, onboarding, and reinforcement, the system quickly falls out of use. 25:48 - Incentives Beat Better Process A mismatch between how product and sales were measured taught Denise that people naturally optimize around their incentives, even when that produces conflicting outcomes for the wider business. 29:09 - AI Makes Drive-By Requests Harder to Reject Denise explains how a senior leader's opinion can now arrive with an AI-generated specification, data, and outcomes attached, making an untested idea look more rigorous without necessarily improving the underlying thinking. 33:22 - Define What Good Looks Like As AI increases the volume of work teams can produce, Denise argues that operating models need clearer standards for what should be created, what evidence belongs in it, and how colleagues are expected to consume it. 35:33 - Your Output Is Someone Else's Input We explore the value of looking at work end to end, because one team's output often becomes another team's input and localized optimization can create problems elsewhere in the system. 38:56 - Use AI Where the Pain Justifies It Denise is increasingly advising companies to use less AI than they initially expect, starting instead with the problem, the value AI might add, and the human judgment and context that still need to remain in the loop. 41:10 - From Product Ops to Omni Ops Denise looks ahead to a more connected model where operational disciplines work across functional boundaries, allowing companies to design the engine of operations as one system rather than a collection of independent silos. 42:09 - Closing Reflections I close by encouraging listeners to explore Denise and Melissa's Product Ops and to think of their own organizations as systems that can be deliberately redesigned and experimented on. FAQsWhat is product operations? Denise describes product operations as helping product managers make faster and better-quality decisions. Her model has three pillars: business and data insights, customer and market insights, and the operating model or ways of working that support product teams. What is a product operating model? At its simplest, Denise says an operating model determines what a company decides to work on, who gets to decide, and how the work gets done. Every organization has one in practice, but many have accumulated theirs informally rather than designing and communicating it intentionally. How does AI affect product operations? AI can accelerate activities across product operations, but Denise argues that judgment and context remain essential. When organizations apply AI to an unclear operating model, poor data, or unresolved decision rights, the technology can amplify those existing weaknesses rather than solve them. Why do incentives matter when designing an operating model? People tend to optimize around what they are measured on. Denise experienced this when product was focused on recognized revenue while sales celebrated closed contracts, creating different definitions of success even though both teams were acting rationally according to their incentives. How should a company decide where to use AI in its operating model? Denise starts with the pain points rather than the technology. She looks at issues such as PRD structure, data analysis, source quality, ownership, and decision-making first, then asks whether AI genuinely improves that part of the system and where human judgment still needs to remain. Useful ResourcesProduct Operations — the book Denise co-authored with Melissa on building the systems and capabilities that help product managers make faster and better decisions.a...

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? 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 TakeawaysHuman 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 InsightsOrganizational culture can be consciously designed: Tatyana frames culture as the interaction of rules, tools, and unwritten norms that shape behavior.Relationships become more valuable as outreach gets automated: When AI increases the volume of generic communication, trust and genuine relationships become more important.AI can generate options, but humans still decide what matters: Judgment becomes critical when the challenge shifts from what can be done to what is worth doing.Small failure rates compound across multi-agent systems: Edge cases that seem minor with one agent can become more significant as agents are connected into workflows.Building an agent and managing one are different jobs: Engineering can help create the agent, but functional owners must take responsibility for its ongoing performance. Episode Highlights00:00 - Episode Recap Tatyana frames the AI shift as a deeper question about what it means to create value as a human when intelligent systems increasingly share responsibility for thinking and decision-making. 02:10 - Guest Introduction: Tatyana Mamut I introduce Tatyana Mamut, CEO of Wayfound and former CPO of Nextdoor, whose experience across anthropology, organizational design, and product leadership shapes her approach to human and AI systems. 05:06 - From Economics to Anthropology Tatyana explains how the failure of economic models to predict events such as Russia’s 1998 economic collapse pushed her toward studying the mental models and social institutions that shape human behavior. 08:07 - Designing Culture Through Rules, Tools, and Norms Tatyana breaks organizational culture into practical components, showing how formal rules, available technologies, incentives, status, beliefs, and unwritten norms interact to shape behavior. 13:16 - The Multi-Sapiens Workplace Tatyana argues that AI is forcing people to reconsider their comparative advantage as humans as intelligent systems begin participating in higher-level thinking and decision-making. 18:42 - Four Human Capabilities to Develop Tatyana identifies taste, judgment, relationships, and performance as four areas people can strengthen as AI absorbs more routine mental work. 23:32 - Why Human Relationships Matter More Barry and Tatyana discuss how automation can remove administrative work while making authentic relationships, lived experience, trust, and storytelling increasingly valuable. 27:21 - Fear Is the Enemy of Progress Tatyana explains how fear can push organizations either to rush into AI using outdated assumptions or retreat when the technology does not behave like traditional software. 29:11 - AI Agents Need Different Supervision Tatyana describes why AI agents require governance and oversight based on organizational context, including goals, rules, norms, and definitions of what good performance looks like. 31:32 - The Incentive Problem The conversation turns to how model behavior and organizational goals interact, including Tatyana’s view that sycophantic behavior can create unexpected feedback loops in production agents. 32:34 - When an Agent Breaks the Intent of the Rule Using a customer service example, Tatyana shows how an agent trying to maximize case closure and customer satisfaction can suggest a refund even when its guardrails tell it not to. 38:59 - Escaping Endless Pilot Mode Barry explores why uncertainty can keep organizations trapped in experimentation rather than allowing AI systems to move into real operating environments. 40:10 - Who Owns the Agent After Deployment? Tatyana argues that the functional experts closest to the work need direct responsibility and visibility once an AI agent is operating in production. 44:41 - Treat AI Agents Like New Employees Tatyana compares engineering teams to recruiters who can help bring an agent into the organization, while the business owner remains responsible for coaching, compliance, and ongoing performance. 46:14 - Closing Reflections Barry closes by reflecting on the emerging skills leaders and individuals will need as they learn to manage systems in which humans and AI agents increasingly work together. FAQsQ1. What human skills become more valuable as AI takes on more work?Tatyana highlights four areas: taste, judgment, relationships, and performance. Her argument is that AI may generate options, perform routine mental work, and help with administration, but humans still create value by deciding what is good, what is worth doing, whom to trust, and how to motivate or persuade other people. Q2. What does Tatyana Mamut mean by a “multi-sapiens workplace”?She uses the phrase to describe a workplace where humans are no longer the only entities carrying out higher-level thinking and decision-making. In that environment, organizations need new rules, tools, and norms for determining how humans and AI systems work together and where responsibility sits. Q3. Why does Tatyana say AI adoption is an organizational design challenge?Because deploying an AI system changes more than the technology stack. Organizations also have to consider incentives, governance, supervision, communication, accountability, cultural norms, and who has the authority to evaluate and correct an agent’s behavior. Q4. Why do AI agents need supervision even when they have guardrails?Tatyana explains that an agent can technically pursue its assigned goal while still behaving in a way the organization did not intend. Her customer service example shows an agent suggesting a refund because doing so could resolve the case and improve customer satisfaction, despite instructions designed to prevent refunds. Q5. Who should manage AI agents after they are deployed?Tatyana argues that responsibility should move toward the subject-matter experts who understand the work the agent performs. Engineering or IT may build and deploy the system, but salespeople should oversee sales agents, finance teams should oversee finance agents, and other functional experts should have direct access to monitor, correct, and improve their agents. Useful ResourcesTanyana Mamut on LinkedIn - https://www.linkedin.com/in/tmamut/ Wayfound - https://www.wayfound.ai/ Artificial Organizations - https://geni.us/artificialorgs Nextdoor - https://blog.nextdoor.com/ Claude - https://claude.com/ Salesforce - a href="https://www.salesforce.com/" rel="noopener...

Before leaders can redesign their organizations with AI, they have to reconsider how they personally think, work, and make decisions. 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 TakeawaysAI 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; speed without insight creates chaos, while insight without timely action becomes irrelevant.Most leadership time is allocated away from leadership value: Barry describes an 80/20 mismatch in which meetings, updates, administration, and context reconstruction consume most of a leader’s time, while the greatest value comes from framing problems, evaluating tradeoffs, and making high-stakes decisions.Organizational AI adoption begins with personal behavior change: Broad mandates, task forces, and tool rollouts are often the wrong starting point. Leaders build more credible adoption when they experiment in their own workflows, share what they learn, and make new behaviors visible to their teams. Additional InsightsPresence can be more valuable than productivity: Barry’s first AI experiment used a meeting assistant to capture and synthesize conversations. The practical gain was not only faster preparation and follow-up; it allowed him to stop carrying every detail in his head and remain fully present during critical discussions.Experience becomes a liability when it is not augmented: The instincts and institutional knowledge that helped leaders succeed can become limiting when they are treated as sufficient. Experience keeps compounding only when leaders combine it with broader recall, faster synthesis, and continuous testing.Senior leaders need psychological safety to learn: Barry’s executive study found that leaders preferred one-on-one coaching and small peer cohorts over large workshops or self-paced courses. Being a beginner is uncomfortable at senior levels, so smaller environments make experimentation and honest questions easier.New capacity should create thinking space, not more workload: In the Progeny case study, AI reduced the effort required to capture meetings, actions, owners, and deadlines. CEO Peter Nevsky framed the resulting capacity as time for strategic thought and better decisions rather than an invitation to place people on a faster operational treadmill.The unit of change is the judgment inside a role: AI may automate parts of a project manager’s, analyst’s, or executive’s work while increasing the importance of interpretation, challenge, and decision quality. The role may remain, but the judgment required within it changes. Episode Highlights00:00 – Episode Introduction & Why I Created the Audiobook Barry introduces this special preview of the Artificial Organizations audiobook, shares why he chose to narrate it himself, and explains why leaders need a different approach to AI adoption through the 3T framework: Traits, Tasks, and Tools. 04:33 – A Quick Favor Before We Begin Barry invites listeners to leave an Amazon review, recommend the audiobook to their network, and help more leaders discover its ideas. 05:16 – Preface: Why the Way We Work Is Broken Barry introduces the central challenge facing modern organizations: leaders are overwhelmed by information, while better decisions remain harder than ever. 07:11 – Judgment Under Pressure Barry explores why more data, more tools, and more technology haven't created better leadership, arguing that the real constraint is our ability to process information and make sound judgments. 10:07 – The First AI Leadership Experiment A simple experiment with an AI meeting assistant transformed Barry's leadership by improving clarity, presence, and decision-making, leading to a new perspective on AI's role. 12:00 – AI as Judgment Infrastructure Barry reframes AI as more than a productivity tool, explaining how it strengthens judgment, improves decisions, and helps leaders focus on what matters most. 16:07 – Part One: The Judgment Constraint Barry introduces the first section of the book, explaining why AI creates little value unless it changes how leaders think, decide, and lead. 18:08 – Chapter One: Your Legacy Is Now Your Liability Barry examines why experience alone is no longer enough and how decision velocity and continuous learning are becoming the defining advantages of modern leadership. 22:20 – The AI ROI Blind Spot Barry challenges the common focus on efficiency and cost reduction, arguing that AI's greatest value lies in helping leaders make better and faster decisions. 28:16 – From Linear Leadership to Exponential Innovation Barry explains why traditional leadership models struggle in the AI era and why organizations must adopt new ways of learning, experimenting, and making decisions. 34:10 – What Makes an Artificial Organization Barry defines artificial organizations and shares how leaders can replace memory-based management with shared judgment systems that accelerate decision-making and collaboration. 40:09 – The New Leadership Divide Barry explores the widening gap between leaders who actively experiment with AI and those who continue relying on legacy ways of working, and why that difference will compound over time. 43:15 – Where to Start Barry explains why successful AI transformation begins with personal experimentation, encouraging leaders to model new behaviors before scaling change across their organizations. 45:15 – Closing Reflections Barry concludes the first chapter, thanks listeners for joining this special audiobook preview, and invites them to continue the journey with Artificial Organizations. FAQsQ1. Is this Unlearn episode an audiobook preview?Yes. This special episode includes Barry O’Reilly’s recorded introduction followed by the opening chapter of the audiobook edition of Artificial Organizations, narrated by Barry himself. He explains why the audio edition was created, what recording the book required, and how its ideas connect to his work with executive leaders and teams. Q2. What is an artificial organization?An artificial organization is a company that deliberately combines human and machine intelligence to redesign how context is captured, information is synthesized, and decisions are made. Instead of adding AI to the edges of existing workflows, it builds judgment infrastructure into how the organization operates. Q3. How can leaders use AI to make better decisions?Leaders can use AI to capture conversations, summarize context, test assumptions, explore scenarios, prepare for meetings, and identify unresolved actions. This reduces the information leaders must carry mentally and gives them more capacity to focus on tradeoffs, consequences, and high-value judgment. Q4. What is the difference between decision velocity and decision advantage?Decision velocity is the speed at which a leader moves from a question to insight, decision, and action. Decision advantage is the quality, accuracy, and depth of context behind that decision. Strong leadership requires both because speed without insight creates chaos, while insight without action loses relevance. Q5. Why do many enterprise AI initiatives fail to create business value?They often begin with tool purchases, pilots, and mandates without redesigning how work, context, and judgment flow through the...

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. 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 TakeawaysChallenge 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 and not simply generated.Real messaging starts with real customers: When Eric joined Nobody Studios, one of the first things he did was speak directly with patent attorneys for Evalify. That helped him understand their language, their concerns, and what message would actually resonate. Additional InsightsStorytelling has to match the person and the moment: Eric explained that messaging should change based on persona, stage in the buyer journey, industry, and country. A CIO, CTO, or chief product officer may each need to hear the story differently.The right message needs different levels of depth: Eric talked about having a 30-second version, a 90-second version, and a longer version of the message ready. The point is to be prepared for the amount of attention and context the listener actually has.B2B messaging has to speak to head, heart, and wallet: Eric said that in larger enterprise deals, especially six- or seven-figure buying decisions, messaging needs to appeal to logic, emotion, and business economics.Consistency across channels is hard but necessary: Eric noted that messages can quickly become confusing when sales, TikTok, Instagram, LinkedIn, and other channels are all saying different things. The challenge is keeping the story consistent while still tailoring it to the audience.Automation still needs personalization: Eric uses some automated systems, but he emphasized that he still fine-tunes and personalizes messages based on someone’s background. Otherwise, outreach becomes the kind of generic message people ignore. Episode Highlights00:00 - Episode Recap Eric Baxley explains why company building in the AI era requires scalable systems, curiosity, grit, and a willingness to dig into the details rather than staying at a high level. 01:53 - Guest Introduction: Eric Baxley Barry introduces Eric Baxley, Chief Marketing Officer at Nobody Studios, and highlights his work across growth, marketing, partnerships, and company building. 03:13 - The Seventh-Grade Spark Eric shares how teaching himself to program on an Atari 800 while growing up in Germany sparked his interest in technology and shaped the rest of his career. 05:10 - Unlearning Assumptions Eric explains why he no longer assumes the base foundation of a business is solid, using a segmentation mistake from a large business as an example. 06:20 - Moving Past Corporate Polish Eric talks about unlearning the need for everything to be buttoned up and why showing the real journey can make the work more relatable. 07:39 - Authentic Stories in a Noisy Market Barry and Eric discuss why honest stories about what is working, what is difficult, and what is still being learned can stand out from exaggerated AI claims. 10:48 - Tailoring the Story Eric breaks down how messaging needs to be adapted by persona, buyer journey stage, industry, and country. 13:24 - Learning From Patent Attorneys Eric shares how he started shaping Evalify’s messaging by speaking directly with patent attorneys instead of creating sales and marketing materials in a vacuum. 18:08 - Head, Heart, and Wallet Eric explains why enterprise messaging needs more than a clinical problem-and-solution structure; it also needs emotion, business value, and a clear story. 22:40 - Building Systems Backwards From the Customer Eric talks about the explosion of marketing technology and why he starts with the persona, the outcome, and the channels where customers actually spend time. 27:37 - Writing Before AI Eric describes how he wrote and revised a LinkedIn post himself before involving AI, and why he believes the human work helped it resonate. 31:48 - Human-in-the-Loop AI for Patent Attorneys Eric explains how Evalify helps patent attorneys with work that can take 20 to 30 hours, while making clear that the product amplifies their work rather than replacing them. 33:49 - Building Companies Faster and More Frugally Eric shares why he is excited that small teams can now use AI capabilities to build, fund, and scale companies differently than in the past. 35:36 - Closing Reflections Barry thanks Eric for sharing lessons from his work at Nobody Studios and looks forward to continuing to build together. FAQsQ1. Who is Eric Baxley? Eric Baxley is the Chief Marketing Officer at Nobody Studios. In the episode, he describes a career that began in software development and later moved into product management, marketing, sales, business development, and partnerships. Q2. What does Eric Baxley say leaders need to unlearn? Eric says he has had to unlearn assuming the foundation is already right, relying too much on corporate polish, and building around siloed tools instead of scalable systems. Q3. Why does Eric Baxley focus so much on customer segmentation? Eric believes many teams jump straight to execution without checking whether they are going after the right customers. He shared an example where fixing segmentation helped a business focus on the right audience and hit its goals. Q4. How does Eric Baxley approach messaging for AI products? Eric starts by talking to the people the product is meant to serve. With Evalify, he spoke with patent attorneys, used their language, tested the message, and worked with an advisory board to see whether the story resonated. Q5. What role should AI play in marketing, according to this episode? AI can help with speed, systems, and efficiency, but Eric and Barry emphasize that human judgment still matters. Eric’s examples show that personalization, customer understanding, and careful writing are still needed for the message to land. Useful ResourcesEric Baxley on LinkedIn - https://www.linkedin.com/in/ericbaxley/ Nobody Studios on LinkedIn - https://www.linkedin.com/company/nobodycrowd/ Nobody StudiosEvalifyThe Challenger SaleArtificial Organizations - https://artificialorganizations.com/ Follow the HostBarry O’Reilly on LinkedIn: https://www.linkedin.com/in/barryoreillyBarry O’Reilly’s website:a href="https://barryoreilly.com/" rel="noopener noreferrer"...

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. 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 TakeawaysAI 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. The skill is recognizing whether AI should extend your own judgment or help you borrow from a domain expert. Teams using AI well can raise decision quality: Barry shares how AI can help teams pressure test assumptions, run scenarios, and ask disconfirming questions without losing momentum. The real advantage comes when AI strengthens collaboration rather than replacing it. AI can also accelerate bad instincts: John warns that AI can make poor thinking look polished. A team can paste AI onto an existing process and call it transformation without changing how decisions are actually made. Intentionality matters more than productivity: AI can reduce friction, but it can also remove the pauses where judgment forms. Leaders need to design space for reflection, not just optimize for more output. Additional InsightsIndividual metacognition: This is understanding how you think and make decisions. John’s examples show that leaders get more value from AI when they can first make their own judgment system visible. Social metacognition: This is understanding that other people think, perceive, and engage differently. AI becomes more useful when it supports the conversation between people instead of flattening everyone into the same process. Computational metacognition: This is understanding what LLMs are good at, where they fail, and how to work with them responsibly. John argues that leaders need this skill so they know when to trust AI, when to challenge it, and when to bring in human expertise. Objection handling as a repeatable system: John’s team did not ask AI to create a generic sales guide. They role-played real objections, captured the discussion, compared their responses against best practices, and turned that into a system that could review future calls. The deeper lesson: AI becomes more useful when it is connected to real work, real context, and a team’s actual judgment. Without that grounding, it risks creating more output without improving the quality of decisions. Episode Highlights00:00 – Episode Recap John Cutler opens with a story about how judgment often comes from repetition and tacit signals, not neat frameworks. The episode explores what happens when AI starts making those hidden decision systems visible. 02:02 – Guest Introduction: John Cutler Barry welcomes back John Cutler, product thinker, systems explorer, and Head of Product at Dotwork, for a conversation about judgment, decision making, and collaboration in the age of AI. 04:59 – How Judgment Gets Built John explains that judgment develops differently depending on the context: through individual practice, repeated exposure, mentorship, team discussion, and comparison against examples of quality. 08:58 – Making Prioritization Thinking Visible John shares how he used AI to document his own scoring heuristic for product prioritization, giving a teammate deeper insight into why certain ideas mattered more than others. 12:11 – Knowing When to Borrow Expertise Using survey design as an example, John explains how AI can help access existing expert knowledge when you are not the expert yourself. The key is being honest about the limits of your own judgment. 13:56 – From Answers to Better Questions Barry reflects on the shift from using AI to get answers toward using it to challenge thinking, improve decisions, and bring stronger questions to colleagues. 18:04 – Why Better Surveys Lead to Better Decisions John explains how improving a survey from average to strong can materially change the quality of insight a team gets back, which then affects the quality of product decisions. 23:04 – Teams, AI, and Decision Advantage Barry shares how AI can help teams maintain momentum during ideation by quickly pressure testing scenarios, asking disconfirming questions, and bringing outside information into the room. 27:48 – Turning Objection Handling into a System John describes how his team recorded a live objection-handling exercise, analyzed it against best practices, and turned the team’s collective knowledge into a reusable system. 31:32 – The Three Forms of Metacognition John introduces individual, social, and computational metacognition as three skills leaders need to work effectively with AI and with each other. 35:19 – AI Exposes Leadership Systems Barry and John discuss why AI can feel uncomfortable for leaders: it reveals whether there is a real decision-making system underneath the confidence. 37:34 – When AI Makes Every Decision Feel Hard John raises an important limitation: AI can remove small pauses in the workday, leaving people constantly operating at high cognitive load. 41:58 – Productivity Fatigue and Agent Overload Barry and John discuss the temptation to run too many AI-assisted tasks at once, and why that can create more noise rather than better outcomes. 44:23 – Designing Time to Think Barry shares how he intentionally creates time for walking, exercise, and reflection to avoid over-optimizing for fast, reactive decisions. 46:38 – Intentionality Over Process Theater John explains why intentionality is different from rigid process. The opportunity is to design better systems without flattening the richness of how teams actually work. 50:11 – Closing Reflections Barry wraps the conversation by reflecting on the opportunity for leaders to use AI not just to move faster, but to become more aware of how they think, decide, and scale judgment across teams. Useful ResourcesDotwork – John Cutler’s work focuses on helping teams and organizations better understand how work, decisions, and systems connect.Artificial Organizations – Barry references the book and the CSTA loop as part of his work on AI, decision making, and organizational performance.Daniel Kahneman’s System 1 and System 2 Thinking – Referenced in the discussion on snap decisions, deeper thinking, and productivity fatigue.Pugh Analysis – John mentions this as an example of a prioritization approach originally intended to help experts independently rate options and then discuss differences in judgment. Follow the HostLinkedIn: https://www.linkedin.com/in/barryoreilly Personal site: a href="https://barryoreilly.com/" rel="noopener noreferrer"...

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. 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 TakeawaysAI 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, or sunk cost take over. In business, those thresholds might be cost, risk, customer impact, or reversibility.Leaders need to separate fast decisions from slow judgment: Some repeatable, data-heavy decisions can be automated with guardrails. Higher-context decisions still need human orientation, pattern matching, and time to think.Reflection turns experience into better pattern matching: Barry shares his practice of documenting decisions, what was known at the time, and why the call was made. That kind of review helps leaders improve the decision process, not just judge the outcome. Additional InsightsRole modeling beats mandates: Jim describes how Boyd taught by showing the mechanics of his performance. Barry connects this to AI adoption: leaders create more movement by sharing how they are using the tools in real work.Productivity fatigue is a real AI-era risk: Barry reflects on how AI can increase output while shrinking the space to think. That matters because senior leadership work often depends on judgment, not just throughput.AI transformation is still a people problem: Jim returns to Jerry Weinberg’s reminder that “no matter what they tell you, it’s a people problem.” Tools help, but organizations still need to redesign the work, behaviors, and decisions around them.Pattern matching is different from gut feel: Jim uses Phil Knight’s Nike decisions to show how instinct can come from years of context. What looks intuitive on the surface is often pattern recognition built through experience. Episode Highlights00:00 – Episode Recap – Jim Highsmith frames the core tension of the episode: AI can accelerate work, but it can also expose whether leaders have a real decision-making system or are quietly handing judgment to the machine. 01:45 – Guest Introduction – Barry introduces Jim Highsmith, a pioneer of adaptive leadership and original Agile Manifesto signatory whose work has shaped how organizations navigate uncertainty and make high-stakes decisions. (Jim Highsmith) 04:27 – Decision-Making Was Missing from the Playbook – Jim explains that when he wrote his first Agile Project Management book in 2004, he found surprisingly little practical guidance on decision-making in standard project management sources. 05:47 – The Real Power of the OODA Loop – Jim revisits John Boyd’s observe, orient, decide, act model and argues that orientation, the ability to update mental models under pressure, is the part leaders often underdevelop. 07:19 – From Process-Centric to Judgment-Centric Management – Jim makes the case that if AI takes over more process improvement work, organizations need decision-making capacity distributed through the system, not concentrated at the top. 09:14 – The Judgment Muscle Can Atrophy – Barry and Jim use the autonomous car example to show how useful automation can quietly weaken a capability when people stop practicing it. 12:33 – Role Modeling Beats Mandates – Jim explains how Boyd taught fighter pilots by showing the mechanics of superior performance, which Barry connects to leaders demonstrating their own AI experiments instead of simply telling others what to do. 15:50 – Capability Is More Than Knowledge – Jim defines capability as knowledge plus experience plus judgment, pointing out that LLMs can provide knowledge but not the consequence-bearing experience that shapes better calls. 18:56 – Thresholds Keep Decisions Honest – Jim shares the Rob Hall Everest story to show why thresholds only matter if leaders are willing to honor them when pressure, ambition, or sunk cost pushes the other way. 20:58 – Automate the Right Decisions – Jim distinguishes fast, data-dependent System One decisions from slower System Two judgments, giving leaders a practical way to decide what to automate and what to protect. 24:31 – From Search Engine to Human-Agent Teams – Jim describes his own progression from using AI as a search engine to working daily with multiple humans and agents, showing that the practice evolves through use. 27:06 – Productivity Fatigue and Constant Execution – Barry reflects on how AI can create more throughput while leaving less space for slow thinking, especially for leaders whose real value is making judgment calls. 31:05 – Relearning the People Problem – Jim returns to Jerry Weinberg’s reminder that “no matter what they tell you, it’s a people problem,” and Barry connects that to companies buying AI tools without redesigning how people work. 33:21 – Pattern Matching Is Not Gut Feel – Jim uses Phil Knight’s early Nike decisions to explain why seasoned executives often seem intuitive because they have built patterns from industry knowledge, relationships, and lived context. 36:09 – Decision Journaling Builds Better Judgment – Barry describes documenting decisions, the information available, and the rationale at the time as a way to learn from both strong and weak outcomes. 37:22 – A Nike Lesson in Collaborative Judgment – Jim recalls a project decision at Nike where the team agreed with the outcome but challenged the process, giving him a lasting lesson about when people need to be part of the call. 38:51 – Closing Reflections – Barry thanks Jim and points listeners toward his writing as these long-standing ideas about judgment, adaptability, and decision-making become even more relevant in the AI era. Useful ResourcesJim Highsmith’s website – Jim’s home base for his bio, books, articles, podcasts, and current work. (Jim Highsmith)The Adaptive EDGE – Jim’s Substack on leadership, adaptability, and AI. (jimhighsmith.substack.com)The Agile Manifesto – The original manifesto and signatories list, including Jim Highsmith. (Agile Manifesto)Adaptive Leadership: Accelerating Enterprise Agility by Jim Highsmith – The book Jim references when discussing his earlier work on adaptive leadership and decision-making. (Google Books)Robot-Proof: When Machines Have All the Answers, Build Better People by Vivienne Ming – The book Jim mentions as influencing his thinking about creative human capability in the AI era. (Google Books)Boyd: The Fighter Pilot Who Changed the Art of War by Robert Coram – A deeper look at John Boyd, the OODA loop, and the “40-second Boyd” story discussed in the episode. (a...

AI is changing how work gets done — but more importantly, it’s changing how people understand their value, identity, and ability to navigate uncertainty. That’s one of the reasons I wanted Chris Walker on the show. Chris has spent years helping companies rethink growth, systems, and organizational performance, but this conversation goes far beyond marketing or AI tactics. Drawing on ideas from his new book The Frequency Era, Chris explores what happens when the work that once made people feel valuable can suddenly be done by AI and automation. One idea that stood out to me most in this conversation is that decision quality depends less on information and more on the person making the decision's internal state. In a world where AI can accelerate execution and analysis, judgment, discernment, and emotional clarity become increasingly valuable leadership capabilities — the very qualities machines cannot replicate. Key TakeawaysAI is reshaping identity, not just jobs: Chris explains that many people attach their self-worth to the work they perform. As AI absorbs more execution-based tasks, leaders will need to help teams navigate the emotional disruption that comes with that shift.Judgment becomes more valuable as automation increases: AI can accelerate execution and analysis, but leaders are still responsible for interpreting context, weighing tradeoffs, and making decisions under uncertainty.Decision quality is driven by internal state: Chris argues that calm, present leaders consistently make better long-term decisions under pressure than leaders operating from anxiety or fear.Creativity requires psychological safety: The conversation explores why innovation suffers in environments dominated by pressure and fear, and why teams create better ideas when people feel safe enough to challenge assumptions.Leaders need a compass more than a map: In fast-changing environments, rigid plans become less useful. Adaptability, awareness, and self-trust become more valuable than certainty. Additional InsightsAI exposes weak leadership systems faster: As AI accelerates execution, unclear decision-making, poor communication, and weak organizational alignment become more visible.Fear changes how people interpret information: Chris explains how anxiety and subconscious patterns can distort communication, amplify uncertainty, and affect leadership behavior.Experienced leaders reduce noise and focus on signal: Barry and Chris reflect on how strong operators simplify complexity and make clear decisions even when conditions are uncertain.Self-awareness becomes a leadership advantage: Understanding personal triggers, assumptions, and subconscious patterns improves both decision-making and interpersonal effectiveness. Episode Highlights00:00 – Episode Recap AI is not just changing how work gets done. It is forcing people to rethink identity, judgment, leadership, and the human capabilities that matter most in an uncertain future. 01:42 – Guest Introduction: Chris Walker Barry introduces Chris Walker, entrepreneur, systems thinker, and author of The Frequency Era, exploring how subconscious patterns shape leadership, performance, and decision-making. 03:23 – Systems Thinking Beyond Marketing Chris explains how thinking like a CEO and understanding entire systems shaped his approach to business, leadership, and organizational growth. 08:11 – AI Is Elevating Human Capacity Chris shares the core idea behind The Frequency Era, arguing that AI is not replacing humans but pushing people toward higher-order capabilities like judgment, creativity, and discernment. 10:37 – When Identity Is Tied to Work The conversation explores why AI feels threatening for many people. Chris explains how attaching identity to specific tasks or roles creates fear and instability during periods of technological change. 14:21 – Judgment Becomes the Competitive Advantage Barry and Chris discuss why judgment may become the most important human skill in an AI-driven world, especially as people increasingly outsource interpretation and thinking to machines. 18:58 – Calm Leaders Make Better Decisions Barry reflects on why the best leaders are often the most present under pressure. Chris explains how emotional state directly affects decision quality and long-term outcomes. 20:58 – Creativity Requires Psychological Safety The discussion shifts toward innovation and team dynamics. Barry and Chris unpack why fear suppresses creativity and how strong leaders create environments where people feel safe to challenge ideas. 24:41 – Emotional Sovereignty and Uncertainty Chris explains why anxiety, imposter syndrome, and self-doubt should be viewed as trainable patterns rather than permanent traits, especially in periods of rapid change. 26:45 – Leaders Need a Compass, Not a Map The conversation explores why rigid planning becomes less effective in fast-changing environments and why adaptability, self-trust, and clarity matter more than certainty. 36:03 – The 30-Second Identity Test Chris shares a simple but revealing exercise that exposes how unclear most people are about their own identity and direction. 39:38 – Defining Your Own Direction Barry reflects on why intentionality and self-awareness become critical leadership tools during periods of ambiguity and constant change. 41:08 – Closing Reflections on Leadership and Identity The episode closes with reflections on self-awareness, adaptability, and the kind of leadership needed to navigate the AI era with confidence. FAQsQ1. What is The Frequency Era about?Chris Walker’s book explores how subconscious patterns, beliefs, and emotional states influence leadership, decision-making, and performance, especially during periods of rapid technological change. Q2. Why does Chris Walker believe judgment is becoming more important in the AI era?As AI automates more execution-based work, leaders still need to interpret context, evaluate tradeoffs, and make decisions under uncertainty. Judgment becomes a differentiator when information and output are abundant. Q3. How does AI affect leadership and organizational culture?The episode explains that AI increases the pace of work and exposes weaknesses in communication, trust, and decision-making. Leaders need stronger emotional regulation and clearer principles to guide teams effectively. Q4. Why is psychological safety important for creativity?Chris and Barry discuss how fear and anxiety limit experimentation. Teams are more likely to produce innovative thinking when people feel safe enough to challenge ideas, make mistakes, and contribute openly. Q5. What human skills become more valuable as AI advances?The conversation highlights judgment, empathy, ethical reasoning, adaptability, communication, and self-awareness as essential skills that remain difficult to automate. Useful ResourcesChris Walker’s book: The Frequency Era - https://a.co/d/0aUgBFeU Chris Walker on LinkedIn - https://www.linkedin.com/in/chriswalker171/ Encoded Website - https://www.encoded.ai/ Barry O’Reilly’s book: Artificial Organizations - https://geni.us/artificialorgs

Incorruptible with Eric Ries What if the companies that last the longest are the ones building enough trust that people want to keep participating in them? That’s the idea behind this conversation with Eric Ries — entrepreneur, author of The Lean Startup, and now Incorruptible. Through stories such as Volvo giving away the seatbelt patent, Tony’s Chocolonely opening its ethical supply chain to competitors, and Mary Parker Follett’s idea of the “invisible leader,” we explore how organizations create lasting advantage through trust, shared purpose, and systems that hold together as companies scale. We also unpack why so many businesses drift toward short-term extraction, what leaders misunderstand about organizational health, and why AI is exposing deeper weaknesses in how companies operate. If you’re building a company and questioning whether business-as-usual is still the right operating system, this conversation is for you. Key TakeawaysEthical business can outperform extractive business models: Eric argues that mission-driven companies are not sacrificing performance. In many cases, trust, alignment, and long-term thinking create stronger economic outcomes.Volvo used open ecosystems as strategy: Giving away the three-point seat belt patent helped establish safety as an industry standard while positioning Volvo as the global leader in automotive safety.Tony’s Chocolonely treats its mission as infrastructure: The company’s goal is not simply selling chocolate. Its mission is to eliminate child slavery from the cacao supply chain through systems that competitors can also adopt.Positive externalities can strengthen competitive advantage: Eric explains how companies can create value by improving the broader ecosystem around them instead of maximizing short-term value extraction.Organizations are shaped by invisible leadership: Mary Parker Follett’s idea of the “invisible leader” shows how shared purpose influences decisions when executives are not in the room.Organizational health cannot be commanded: Leaders can issue instructions, but trust, accountability, and commitment have to be cultivated through systems and behavior over time. Additional InsightsThe current business narrative rewards extraction over durability: Barry and Eric discuss how modern startup culture often glorifies hyper-efficient solo founders, aggressive cost-cutting, and short-term returns while ignoring long-term organizational health. AI is amplifying leadership weaknesses, not solving them: As companies use AI to accelerate decision-making and productivity, leaders are being forced to confront whether their systems actually create clarity, trust, and aligned behavior. Mission statements are easy. Mission transmission is harder: Eric argues that values only matter when they shape real decisions, incentives, hiring, product tradeoffs, and customer experience. Open systems can expand both impact and market position: From Linux and Git to Netflix influencing AWS through open source tooling, the episode explores how sharing infrastructure can strengthen an ecosystem while also benefiting the originating company. Profit becomes dangerous when it ignores externalities: Eric explains how traditional profit models often fail to account for long-term brand damage, human cost, environmental impact, and deferred liabilities. Episode Highlights00:00 – Episode Recap Eric Ries explains why organizations are living systems, not machines to be controlled. Leaders can command action, but organizational health has to be cultivated through purpose, trust, and the systems people use when no one is watching. 00:57 – Barry’s Opening Reflection Barry connects AI, leadership, and decision-making systems before introducing Eric’s new book, Incorruptible. 02:14 – Guest Introduction: Eric Ries Barry introduces Eric Ries, entrepreneur, author of The Lean Startup, and author of Incorruptible, framing the conversation around ethical business as a path to long-term prosperity. 04:34 – Researching the Stories Behind Incorruptible Eric shares how much research went into the book, including the challenge of finding stories that were not just interesting, but genuinely useful for leaders. 08:07 – Volvo and the “Seatbelt Heist” Eric breaks down how Volvo’s decision to give away the three-point seat belt patent created a prosperity cascade that reshaped the industry while strengthening Volvo’s long-term brand position around safety. 16:45 – Open Source as Strategy Barry connects Volvo’s story to Netflix and cloud computing, where open sourcing internal tools helped shape the direction of the broader ecosystem. 17:57 – Positive Externalities as Business Strategy Eric explains why companies often overlook opportunities to create value by improving the wider system around them. 20:18 – Tony’s Chocolonely and Slave-Free Chocolate Eric tells the story of how a Dutch journalist turned frustration over child labor in cacao production into a fast-growing chocolate company with a much larger mission. 24:03 – Mission Beyond the Product Tony’s mission is not simply making chocolate. The business exists to eliminate child slavery from the cacao supply chain and align economics with ethical sourcing. 26:00 – Tony’s Open Chain Eric explains how Tony’s opened its ethical supply chain to competitors while requiring them to commit to the same standards across all their chocolate products. 30:32 – The False Tradeoff Between Ethics and Performance Eric challenges the business-school assumption that companies must choose between mission and profit, arguing that the data often shows the opposite. 33:23 – Redefining Profit Barry and Eric discuss why traditional definitions of profit often ignore externalities, deferred liabilities, human cost, and long-term brand damage. 39:19 – The Myth of the Solo Founder Barry pushes back on modern founder mythology and explains why anything built to last depends on systems, teams, and shared ownership. 40:36 – Mary Parker Follett and the Invisible Leader Eric introduces management thinker Mary Parker Follett and explains why her ideas about shared purpose and distributed authority were decades ahead of their time. 45:00 – What Guides Decisions When Leaders Aren’t Present Eric explores Follett’s idea of the invisible leader: the shared sense of purpose that influences behavior when no executive is in the room. 49:35 – Organizations as Living Systems Eric compares organizations to emergent intelligence systems like ant colonies or the human body, arguing that leaders can cultivate organizational health but cannot directly command it. 52:30 – Closing Reflections Barry and Eric reflect on the need for new business models that prioritize trust, mission alignment, and long-term value creation over extraction. Useful ResourcesEric Ries — IncorruptibleEric Ries — The Lean StartupEric Ries on LinkedIn - https://www.linkedin.com/in/eries/ The Eric Ries Show YouTube - https://www.youtube.com/@theericriesshow Barry O’Reilly — Artificial Organizations - https://geni.us/artificialorgs FAQsQ1: What is Eric Ries’ book Incorruptible about?Incorruptible explores how leaders can build companies that stay aligned with their mission as they grow. Eric looks at stories from business history to show how purpose, governance, incentives, and ownership shape whether companies create long-term value or lose their way. Q2: Why does Eric Ries use Volvo as an example?Volvo’s three-point seat belt story shows how a company can create value by spreading a mission beyond its own products. By making the patent available to others, Volvo helped establish safety as an industry standard while strengthening its own reputation for safety. Q3: What is Tony’s Chocolonely trying to change?Tony’s Chocolonely is trying to eliminate child slavery from the cacao supply chain. The company sells chocolate, but the deeper mechanism is building an ethical supply chain that other companies can use through Tony’s Open Chain. Q4: What does Mary Parker Follett mean by the invisible leader?The invisible leader is the shared purpose that guides people’s decisions when no formal leader is present. It is what shapes behavior in everyday moments, such as how teams handle quality issues, customer problems, or ethical tradeoffs. Q5: Can leaders...

Most people think growth comes from doing more—more services, more offers, more complexity. But in this episode, I sit down with Tas Bober, who did the exact opposite. She stripped everything back, focused on one problem, and built a business so clear people can describe it in a single sentence. This conversation is about the courage to simplify—and why that’s far harder (and more powerful) than it sounds. Tas didn’t plan to become an entrepreneur. After layoffs, burnout, and a side experiment on LinkedIn, she found herself with unexpected demand—but no clear direction. It wasn’t until she made a bold, uncomfortable decision to niche down into landing pages that everything changed. What followed is a masterclass in clarity, positioning, and designing a business that actually fits your life—not the other way around. Key TakeawaysNiching down creates clarity: Focusing on one problem made it obvious what Tas does—and why clients should choose her.Doing less accelerates growth: Eliminating distractions and context switching improved both quality and income.Clarity beats capability: Being known for one thing is more valuable than being able to do many.Positioning drives inbound demand: Clear positioning meant clients showed up with defined problems—making selling easier.Data should guide decisions: Tracking time revealed which work actually delivered the highest return.Design your business around your life: Tas optimized for time, flexibility, and energy—not scale for the sake of it. Additional InsightsTrying to do everything can make you lose authority: You shift from expert to order taker.Community accelerates growth: Trusted peers help challenge thinking and shorten the learning curve.Scarcity mindset delays focus: Holding onto everything early can prevent meaningful progress.AI amplifies thinking—it doesn’t replace it: Expertise and nuance still drive better outcomes.Simplicity requires discipline: Even after success, the temptation to expand never goes away. Episode Highlights00:00 – Episode Recap Tas shares how narrowing her focus to one specific problem transformed her business, income, and lifestyle. 01:00 – The Accidental Entrepreneur Tas reflects on being laid off twice and how a side experiment on LinkedIn unexpectedly opened new opportunities. 05:00 – The Struggle of Starting Out She describes the early chaos of offering everything, underpricing, and trying to figure out what problem she actually solved. 08:30 – The Niching Down Breakthrough A peer challenges Tas to focus on landing pages—and within a week, everything changes. 12:30 – Why Clarity Wins in Business Barry and Tas unpack why being known for one thing beats showcasing a wide range of capabilities. 17:00 – The Power of Focused Repetition Tas explains how working on the same problem repeatedly builds deep expertise and pattern recognition. 20:30 – The Economics of Specialization Tracking her time reveals a stark difference in earnings between general consulting and niche work. 24:30 – Cutting Everything Else Tas makes the difficult decision to eliminate all other services and go all-in on landing pages. 26:00 – Resisting the Urge to Expand Even after success, the temptation to do more returns—and why discipline is required to stay focused. 29:00 – Fast Decisions and Iteration Tas shares her approach to reversible decisions and rapid experimentation. 31:00 – Building a Values-Driven Business She discusses choosing clients based on alignment and maintaining an audience-first mindset. 34:00 – The Role of Simplicity in Growth Barry highlights how clear positioning is often the biggest unlock for entrepreneurs. 36:50 – Designing a Business Around Life Tas reflects on working three days a week and prioritizing enjoyment and flexibility. 38:00 – AI, Creativity, and Human Insight Why AI can’t replace nuanced expertise—and how human judgment remains critical. 39:30 – Closing Reflections A final look at growth, experimentation, and the ongoing journey of building something meaningful. FAQsQ1. Why is niching down important for business growth? Niching down creates clarity in your positioning, making it easier for customers to understand what you do and why they should choose you. It also improves inbound demand and simplifies sales conversations. Q2. Can focusing on one service really increase revenue? Yes. Specializing allows you to become more efficient, deliver higher-quality results, and charge premium rates—often earning more while working less. Q3. How do you choose the right niche for your business? The best niche sits at the intersection of your experience, market demand, and repeatable problems you’ve solved. Testing a niche for a defined period can help validate it quickly. Q4. What are the benefits of clear positioning in a crowded market? Clear positioning helps you stand out by making you the first person people think of when they have a specific problem, reducing competition and increasing trust. Q5. How does specialization compare to using AI tools in business? AI can support execution, but it lacks the nuanced insight and pattern recognition that comes from deep specialization. Experts who focus on one problem can deliver more valuable and differentiated outcomes.

Most leaders think AI is a technology shift. It’s not. It’s a behavior shift. In this episode, I sit down with Melanie Steinbach—former Chief HR Officer at McDonald’s, Cameo, and MasterClass—to unpack what’s actually changing inside organizations as AI becomes embedded in how we work. Melanie has spent her career solving business problems through people. But she challenges a core assumption: that performance problems are solved by replacing people. Instead, the real leverage comes from coaching, clarity, and creating the conditions for people to do their best work. We explore why AI doesn’t replace leadership—it exposes it. And what that means for AI leadership and decision-making inside modern organizations. Same tools. Same access. Completely different outcomes. The difference comes down to how leaders think, make decisions, and design systems around their teams. We also unpack a critical shift most organizations aren’t ready for: redefining what “valuable work” actually means. For years, being busy—and being in meetings—has been treated as a proxy for value. But when AI handles execution, value moves to judgment, context, and decision quality. If you’re leading teams, navigating transformation, or trying to understand where AI actually fits in your organization, this conversation will change how you think about leadership, work, and performance. Key TakeawaysSolving business problems through people isn’t about replacement: The real leverage comes from coaching, clarity, and creating the conditions for people to succeed.AI exposes how you lead: The same tools produce radically different outcomes depending on how you think and make decisions.Clarity drives performance: When expectations are vague, even high performers struggle to deliver.Context is now the constraint: Information is everywhere, but leaders create value by helping teams interpret and act on it.Busy work is losing its signal: Meetings and activity no longer define value—decision quality does.AI requires behavior change, not just adoption: The advantage goes to leaders who change how they work, not just what tools they use.Judgment is the differentiator: AI can generate answers, but leaders are still responsible for making the call. Additional InsightsPerformance problems are often system problems: Most people want to do a good job, but unclear expectations and missing context get in the way.Onboarding is being rebuilt in real time: AI enables “what you need to know, when you need to know it” instead of static training programs.Leadership is shifting from answers to perspective: The value is no longer having information—it’s providing context and nuance.Meetings were a proxy for value: Being busy created the illusion of impact, but that signal is breaking down fast.Work is being unbundled: Roles are no longer fixed—they’re collections of tasks being redistributed between humans and machines. Episode Highlights00:00 – Episode Recap Melanie Steinbach reframes how organizations solve business problems, shifting the focus from replacing people to unlocking their potential through clarity, coaching, and better systems. 01:30 – Guest Introduction: Melanie Steinbach Former Chief HR Officer at McDonald’s, Cameo, and MasterClass, Melanie has led transformation at scale across some of the world’s most recognized organizations. 03:49 – From Replacement to Development Melanie shares the moment she realized solving business problems through people isn’t about hiring differently—it’s about developing the people you already have. 06:35 – Why People Want to Do a Good Job Most employees aren’t underperforming by choice—they’re missing clarity, skills, or expectations. 08:24 – The Cost of Missing Clarity Unclear systems create friction, confusion, and unnecessary failure—even in high-performing environments. 11:18 – Culture Shapes Behavior In some organizations, asking questions signals curiosity. In others, it signals weakness—and that changes everything. 18:14 – AI Changes How People Learn Onboarding and development become dynamic, personalized, and driven by real-time needs. 22:02 – From Knowledge to Context Leadership evolves from delivering information to helping teams interpret and apply it effectively. 24:41 – Presence Becomes a Superpower AI reduces cognitive load, allowing leaders to show up focused, prepared, and ready to make decisions. 28:06 – Why Humans Still Matter Technology amplifies systems, but judgment, meaning, and connection remain human. 32:00 – Rethinking Valuable Work Being busy is no longer proof of impact—decision quality is. 35:16 – A New Metric for Performance High-quality decisions—made faster with better context—become the new standard. 38:58 – Thinking Is the New Advantage Creating space to think clearly becomes one of the most valuable leadership skills. 41:55 – Work Is Being Redefined Jobs are breaking into tasks, with AI handling execution and humans focusing on judgment. 42:33 – Why This Moment Matters Melanie shares why she’s stepping in to help organizations navigate this shift across industries. 44:04 – Closing Reflections This isn’t a small shift—it’s a fundamental redesign of how work gets done and how leaders create value.

AI isn’t changing the game—it’s exposing how you think. In this episode, I sit down with Misty Shafer Sterne, Vice President of Commercial Technology at American Airlines, to explore what it really takes to lead with AI inside a complex, high-stakes organization. We go beyond the usual productivity narrative and dig into something far more powerful: how AI can sharpen decision-making, surface better questions, and help leaders operate with greater clarity and intent. Misty shares her journey from chasing efficiency to building a personal system for thinking—using AI as a partner to capture ideas, pressure test decisions, and improve how she shows up as a leader. We also unpack why experimentation matters more than metrics early on, how to avoid automating broken processes, and what it looks like to lead in the open so teams can follow. This is a conversation about performance, not productivity—and what it means to truly unlearn how we work. Key TakeawaysAI amplifies how you think: The real advantage isn’t speed but improving clarity, judgment, and decision-making quality.Productivity is just the entry point: The biggest gains come from using AI to enhance performance, not just efficiency.Experimentation must come before measurement: Over-indexing on productivity metrics too early can shut down innovation.Leaders must unlearn being the “answer person”: Great leadership shifts toward asking better questions rather than having all the answers.Decision velocity matters more than idea volume: Success comes from quickly identifying which ideas are worth pursuing, not generating more ideas. Additional InsightsAI as a thinking partner: Misty’s breakthrough came when she used AI to pressure test ideas instead of manage tasks.Externalizing thinking creates clarity: Capturing raw, messy thoughts helps reveal patterns and improve decision-making.Your thinking becomes a reusable asset: AI enables leaders to build a system that stores and evolves their ideas over time.Automating bad processes makes them worse: AI should be used to redesign workflows, not just accelerate existing inefficiencies.Leadership requires learning in public: Vulnerability and visible experimentation help drive adoption across teams. Episode Highlights00:00 – Episode Recap A shift from chasing productivity to unlocking better thinking transforms how leaders use AI—turning it into a true decision-making partner rather than just a tool. 02:00 – Guest Introduction: Misty Shafer Sterne Barry introduces Misty, VP of Commercial at American Airlines, leading AI-driven decision-making across pricing, customer experience, and revenue. 04:25 – From Perfection to Curiosity Misty reflects on her journey from needing all the answers to embracing vulnerability and asking better questions as a leader. 07:45 – The Productivity Trap Early AI use focused on inbox management and efficiency, but Misty realized this wasn’t where real value lies. 09:30 – AI as a Thinking Partner Using AI to externalize thoughts, identify patterns, and pressure test ideas unlocks a new level of clarity and decision-making. 12:30 – Performance Over Productivity The real benefit of AI is improving how leaders show up, think, and collaborate—not just getting more done. 15:15 – Capturing Ideas Before They’re Lost Verbal processing and real-time capture help preserve insights and connect ideas over time. 18:18 – Building a System of Thinking Accumulating ideas creates a long-term asset that helps leaders identify patterns and improve decisions. 21:25 – Why Experimentation Needs Space Over-measuring productivity too early can limit exploration and reduce the potential of AI adoption. 24:33 – Context Matters in Decisions Capturing why decisions were made enables better future judgment as conditions change. 29:04 – Leading by Example Misty shares how modeling experimentation helped shift her organization from fear to adoption. 33:40 – The Danger of Automating Bad Processes AI can amplify poor systems—leaders must rethink workflows, not just speed them up. 36:03 – Redesigning Work for Better Outcomes True transformation comes from changing behavior and systems, not just adopting tools. 38:45 – Unlocking Ideas Across the Organization AI democratizes innovation, requiring leaders to step back and let the best ideas emerge from anywhere. FAQs1. What is the biggest mistake leaders make when adopting AI? Focusing too much on productivity metrics early on instead of creating space for experimentation and learning. 2. How should leaders actually use AI in their daily work? As a thinking partner—to capture ideas, pressure test decisions, and improve clarity, not just automate tasks. 3. What does “decision velocity” mean? It’s the ability to make faster, higher-quality decisions with confidence by using better information and structured thinking. 4. Why is experimentation so important in AI adoption? Because the real value emerges through exploration—rigid expectations can limit discovery and innovation. 5. How can leaders avoid damaging their teams when introducing AI? By leading with transparency, modeling behavior, and ensuring AI enhances collaboration rather than creating pressure or fear. Useful ResourcesMisty Shafer on LinkedInAmerican Airlines on LinkedInAmerican Airlines WebsiteArtificial Organizations by Barry O’ReillyBrené Brown – “Rumbling” and leadership frameworks Follow the HostLinkedIn: https://www.linkedin.com/in/barryoreillyPersonal site: https://barryoreilly.comFacebook: https://www.facebook.com/barryoreillyauthor/Twitter: https://x.com/barryoreillyInstagram: https://www.instagram.com/barryoreilly/

What role should businesses play in society today? In this episode of the Unlearn Podcast, I sit down with investor, ecosystem builder, and Foundry Group co-founder Seth Levine to explore how capitalism itself may be evolving. Seth’s latest book, Capital Evolution, examines a fundamental question: can modern capitalism create broader opportunity, stronger communities, and more inclusive ownership? Our conversation ranges from declining economic mobility and generational uncertainty to the role of values-driven companies and the rise of AI-powered work. We also dive into how leaders can navigate uncertainty—balancing profit, purpose, and technological disruption. Along the way, Seth shares how a single dinner conversation sparked a two-year research journey, interviewing more than 100 leaders, academics, and entrepreneurs to understand where capitalism may be headed next. This conversation also builds on ideas we’ve explored before on the podcast—particularly with Seth’s long-time collaborator Brad Feld. In our earlier discussion on Give First leadership, Brad challenged the idea that success comes from extraction, instead arguing that generosity, long-term thinking, and community building are the real drivers of sustainable impact. Together, these conversations offer a powerful lens on how leadership, capitalism, and value creation are being redefined. If you’re thinking about the future of work, leadership in an AI-powered world, or how organizations can create both economic and societal value, this episode will challenge some assumptions. Key TakeawaysCapitalism is already evolving: Many leaders still operate as if we’re in a shareholder-first world—but that model has already shifted. Businesses now face growing pressure to balance profit with broader societal impact.Economic mobility is declining: While economies continue to grow in aggregate, fewer people are able to move up financially. This shift is reshaping how younger generations view opportunity and fairness.Values-driven companies are becoming more visible: Organizations like Patagonia, Hobby Lobby, and Chick-fil-A show that companies can operate with clear values. What matters most is transparency—being honest about what the organization stands for.AI is changing how leaders work: Seth describes using AI tools like Claude as an operating system for his daily work—drafting ideas, exploring questions, and accelerating thinking. Leaders who combine human judgment with machine intelligence can dramatically increase their effectiveness.Curiosity and listening are leadership superpowers: One of Seth’s biggest lessons from writing the book was the value of listening to perspectives outside his own experience. Engaging with different viewpoints reveals insights leaders often miss. Additional InsightsThe danger of misleading economic averages: Seth describes the “Jeff Bezos walks into a bar” problem—where averages distort reality. Aggregate growth can hide widening inequality and declining mobility.The difference between values and politics in business: Companies should be clear about their values—but that doesn’t mean every company needs to take political positions. Transparency builds trust with employees, investors, and customers.Why younger generations feel uncertain: Many are entering a world shaped by rapid technological change, rising costs of living, and shifting job markets. AI both excites and worries people about what work will look like.AI rewards experimentation: The people benefiting most from AI are those who continuously experiment. Treating AI as a collaborator—not just a tool—opens entirely new ways of working. Episode Highlights00:00 – Episode Recap Seth Levine reflects on the changing nature of capitalism and why declining economic mobility, shifting values, and AI-driven disruption are forcing leaders to rethink how businesses create value for society. 02:40 – Guest Introduction: Seth Levine Barry introduces Seth Levine, co-founder and partner at Foundry Group, entrepreneur advocate, and author of Capital Evolution, a book exploring how capitalism can evolve to create broader opportunity. 05:00 – The Dinner Conversation That Sparked a Book Seth describes the boardroom conversation that sparked the idea behind Capital Evolution—a question about whether investors would accept lower returns in order to live their values. 07:53 – Why the Capitalism Debate Is Growing Research for the book revealed that roughly half of people under 40 believe capitalism isn’t working—prompting a deeper exploration of how the system might evolve. 10:33 – Values-Driven Businesses in Practice Examples like Patagonia and Hobby Lobby show how companies can operate with strong values while still pursuing business success. 19:14 – Generational Anxiety About the Future Younger generations face growing uncertainty about careers, technology disruption, and economic opportunity. 23:38 – Learning From Different Perspectives Travel, conversations, and research helped Seth recognize how limited our understanding can be when we stay within familiar social and professional circles. 29:30 – Experimenting With AI at Work Seth shares how tools like Claude have become part of his daily workflow—helping him explore ideas, draft writing, and accelerate thinking. 33:38 – Why AI Feels Exciting Again After decades in venture capital, Levine says experimenting with AI tools has reintroduced a sense of novelty and curiosity into his work. 40:51 – The Pattern of Every New Technology From automobiles to AI, every major technological shift initially sparks fear before becoming normalized. 42:07 – A Simple Leadership Habit: Listen More Seth encourages leaders to actively seek conversations with people who hold different perspectives—and listen without trying to persuade. 45:00 – Closing Reflections Barry wraps the conversation, highlighting the importance of curiosity, experimentation, and openness as leaders navigate the evolving relationship between business, society, and technology. FAQsQ1. What is Capital Evolution and why is it important?Capital Evolution describes how capitalism is shifting beyond a shareholder-first model toward creating broader societal value. It’s important because businesses are now expected to balance profit with impact, ownership, and community outcomes. Q2. Why is economic mobility declining today?Economic mobility is declining because wealth and opportunity are increasingly concentrated. Even as economies grow overall, fewer people can move up income levels, making it harder for younger generations to improve their financial position. Q3. What role should businesses play in society today?Businesses should generate profit while also contributing to society through clear values, responsible decision-making, and long-term thinking. The most effective companies align economic success with positive societal impact. Q4. How is AI changing leadership and the future of work?AI is transforming leadership by enhancing decision-making and productivity. Leaders who combine human judgment with AI tools can move faster, process more information, and make better strategic decisions. Q5. Why are younger generations questioning capitalism?Younger generations are questioning capitalism due to rising living costs, job uncertainty, and reduced economic mobility. These challenges make traditional systems feel less fair and less effective than in the past. Useful Resources:Seth Levine on LinkedIn - https://www.linkedin.com/in/sethjlevine/ Foundry Website - https://foundry.vc/ Seth Levine Book: Capital Evolution - https://www.amazon.com/Capital-Evolution-New-American-Economy/dp/1637747780 Unlearn Episode 160 with Brad Feld - https://barryoreilly.com/explore/podcast/give-first-leadership-brad-feld/  Follow the Host:LinkedIn: https://www.linkedin.com/in/barryoreilly Personal site: https://barryoreilly.com Facebook: https://www.facebook.com/barryoreillyauthor/ Twitter/X: https://x.com/barryoreilly Instagram: https://www.instagram.com/barryoreilly/