Unlearn

AI investment can give people new tools without changing the way the business performs. Leaders need to explain what should improve, help teams develop new ways of working, and ensure their goals and incentives support that change.In this episode, Melanie Steinbach, whose people leadership experience spans McDonald’s, Cameo, and MasterClass, joins me to explore how organizations turn AI investment into better business outcomes. Mel explains why benefits take time to emerge, why training needs ongoing practice, and how individual KPIs can undermine shared goals. We also discuss leaders’ responsibility for redesigning work across departments and how recognition can encourage people to share what they learn.This is the third podcast in The Adaptive Enterprise, our five-part article and podcast series on redesigning work, governance, and leadership for the age of AI.Key TakeawaysConnect the purpose to the person: Explain why the organization is investing in AI, what it expects to achieve, and what employees need to do differently. Melanie’s example of changing a promotion model shows why both organizational context and individual expectations matter.Measure progress along the way: Capability development and business benefits may emerge on different timelines. Identify meaningful milestones, such as stronger analysis or better cross-functional collaboration, and explain how they contribute to the intended outcome.Treat training as the beginning: A workshop or certification does not establish lasting behavior change. Repeated practice, feedback, and opportunities to apply learning help people develop capability in everyday work.Make work redesign a leadership responsibility: Individual productivity improvements do not automatically improve the whole business. Leaders need to define shared outcomes, remove obstacles, and support teams in changing work across departmental boundaries.Align KPIs with the change you want: Goals and incentives shape what people prioritize. Melanie urges leaders to give individual KPIs the same care as budgets, so that they support collaboration rather than pull people toward competing departmental targets.Additional InsightsLearning can be an explicit outcome: Barry describes bank pilots designed to build knowledge about privacy, develop capability, and help risk and engineering work together. Leaders need to be clear about what each investment is intended to teach them.Fear needs context and practical experience: Employees may interpret AI through concerns about job loss. Barry’s bank example combines a CEO’s explanation of the direction with hands-on work on decisions and agents.Managers help turn shared goals into daily decisions: Executives need to commit to common outcomes and resources, while directors and managers coordinate the work, dependencies, and trade-offs required to deliver them.Recognition can sustain knowledge sharing: Barry’s Capital One example shows how celebrating people who taught colleagues helped extend learning beyond certification. Melanie emphasizes that recognition is an incentive and that established lessons about human behavior still apply.Episode Highlights00:00 – Episode RecapMelanie explains why access to technology does not automatically change behavior and why people need a clear organizational and individual purpose.01:24 – Introducing the Conversation on AI OutcomesBarry welcomes Melanie back to discuss measuring behavior change, developing capability, and moving beyond counts of AI activity.03:58 – Explain Why the Change MattersMelanie uses the analogy of a scale or fitness tracker and shares a promotion-policy example to show why context and expectations matter.09:35 – What Should an AI Pilot Help You Learn?Barry describes a bank’s hands-on learning sessions and a discussion about whether pilot success should include knowledge, capability, and cross-functional collaboration.11:56 – Investment and Benefits Have Different TimelinesMelanie explains the importance of an investment thesis and milestone measures when capability development takes time to produce business results.18:00 – Training Is the Start of Behavior ChangeMelanie describes why one training day is insufficient and how small, repeated practice helps people develop new ways of working.21:36 – Who Is Responsible for Redesigning Work?Melanie challenges the expectation that individual employees should identify the wider organizational redesign needed to turn productivity gains into value.23:35 – Leaders Create the Conditions for Shared OutcomesBarry explains why executives need firsthand experience with AI, then must clear obstacles and support work that spans multiple departments.27:14 – Directors and Managers Connect Strategy to ExecutionBarry discusses coordination, local incentives, competing priorities, and the executive commitment required to support cross-functional work.29:20 – Give KPIs the Attention They DeserveMelanie argues that individual goal setting needs the same care as budget planning because incentives can support or undermine behavior change.31:11 – Recognition That Helps Learning SpreadBarry shares how Capital One encouraged certification, peer teaching, and visible recognition, then describes learning communities where people shared experiments and failures.34:22 – Remember What We Know About PeopleMelanie closes by emphasizing recognition, safety, incentives, and the established principles of behavior change as leaders navigate new technology.Useful ResourcesThe Adaptive Enterprise: Stop Measuring AI Activity. Start Managing Outcomes.Artificial Organizations Research Program: Measure AI OutcomesArtificial OrganizationsMelanie Steinbach’s previous Unlearn Podcast episode: Solve Business Problems with PeopleMelanie Steinbach on LinkedInL AdvisorsIncorruptible by Eric RiesFollow Barry O’ReillyLinkedIn: https://www.linkedin.com/in/barryoreillyWebsite: https://barryoreilly.comFacebook: https://www.facebook.com/barryoreillyauthor/X: https://x.com/barryoreillyInstagram: https://www.instagram.com/barryoreilly/

AI capabilities are advancing quickly, but changing how people work takes time. Leaders need to give teams the confidence to experiment while protecting sensitive information and keeping responsibility clear. How they manage that balance shapes whether AI leads to better business outcomes.In this episode, Cassandra Pratt, CHRO at Progyny, and John Marcante, former Global CIO of Vanguard, join me to explore how governance can support that change. Cass shares lessons from Progyny’s internal HR tool, Lumi, and efforts to redesign workflows while building employee trust. John recalls how a Vanguard team improved website conversion by listening to clients and simplifying their choices. He connects that experience to governing AI through clear business outcomes, accountability, and oversight matched to risk.This is the second podcast in The Adaptive Enterprise, our five-part article and podcast series on redesigning work, governance, and leadership for the age of AI.Key TakeawaysStart with a business outcome: Define the problem you want to solve before choosing how to use AI. John’s Vanguard example shows how customer research and simpler choices helped address a conversion problem that previous technology investment had not resolved.Design governance into the whole workflow: Bring the relevant business, risk, and technical partners together early. Cass explains why understanding the data and the full process makes it easier to establish appropriate controls.Build trust through visible action: Explain how information is handled and show what happens when work is automated. Cass describes employees moving into a new member services area after automation, giving them evidence that leadership would follow through on its commitments.Put recovered capacity toward what matters: At Progyny, reducing manual work can give people more time to listen to and support members. Judge progress against the business outcome and the experience you want to improve.Match oversight to the risk: John contrasts an internal productivity tool with autonomous trading to explain why different uses need different levels of oversight. Clear responsibility, human involvement, and monitoring remain important as AI becomes more autonomous.Additional InsightsAn initial tool can be a starting point: Cass describes Lumi evolving beyond its original HR use. At the time of recording, Lumi Landing, which would compile and present information across the company, was still being developed.Learning needs support across levels: Progyny’s champions program brings employees from different roles and seniority levels together, with engineers helping on more complex challenges.Leaders need firsthand experience: Cass’s executive team built skills and agents themselves. Experiencing the difficulties helped make the learning process more tangible and reinforced the importance of sharing mistakes.Human judgment remains a leadership responsibility: John describes AI’s value in gathering a fact base while emphasizing leaders’ responsibility to imagine the organization’s future and determine its direction.Episode Highlights00:00 – Episode RecapCass describes the gap between the speed of AI and organizational change, while John introduces his analogy of roadblocks, speed bumps, and guardrails.02:39 – What Should Leaders Govern Now?Barry introduces the conversation, and Cass explains why progress in HR has been slower than she expected.04:37 – Start With the Outcome at VanguardJohn shares how a cross-functional team used customer feedback to identify confusing language and excessive choices affecting website conversion.08:18 – Protecting the Human ExperienceCass describes how operational automation can free people to focus on member conversations and the human service Progyny values.11:56 – Building Trust in an Internal HR ToolBarry introduces the employee experience behind Lumi, and Cass explains how the team addressed concerns about information handling and anonymity.14:06 – From an Initial Tool to Wider LearningCass discusses Lumi’s evolution, initiatives still in development, and the champions program supporting experimentation across the organization.18:09 – Redesigning Work With Fresh PerspectivesCass explains how colleagues from other disciplines help challenge familiar processes and design governance around the whole workflow.21:17 – The Courage to Lead While LearningJohn and Barry discuss vulnerability, visible experimentation, and leaders’ responsibility to keep attention on business outcomes.24:15 – Giving Employees Evidence They Can TrustCass shares how employees moved into new work after automation and how hands-on executive learning supports confidence in change.30:27 – Guardrails, Risk, and AccountabilityJohn explains oversight matched to risk, enterprise-level coordination, and the governance questions raised by increasingly autonomous AI.34:48 – Where to Start on Monday MorningCass recommends involving the right partners early, defining the problem, and understanding the data needed to redesign the workflow.36:18 – Machine Intelligence and Human IngenuityJohn explains why leaders should use AI to inform strategy while retaining responsibility for imagining the future of the business.Useful ResourcesThe Adaptive Enterprise: From Software Governance to Behavioral GovernanceArtificial Organizations Research Program: Measure AI OutcomesArtificial OrganizationsCass Pratt’s previous Unlearn Podcast episodeCass Pratt on LinkedInJohn Marcante on LinkedInProgynyVanguard

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 IsJohn 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 SeriesBarry 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 BusinessJohn 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 TogetherBarry shares a lesson from American Airlines about involving the wider business in transformation.09:45 – Scaling AI Without Changing the Operating ModelJohn discusses why technical readiness does not necessarily give leaders the influence to change established ways of working.11:07 – Redesign the Whole ProcessBarry 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 RolesBarry 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 FirstBarry shares how Progyny’s CEO modeled experimentation, and John discusses humility and vulnerability in leadership.22:46 – Helping Advisors Redefine Their ValueJohn describes moving the focus from portfolio mechanics and larger client books to client loyalty and important family decisions.27:26 – The Adaptive Enterprise LoopJohn explains the continuous cycle of capabilities, work redesign, outcomes, governance, and learning.30:50 – Where Executive Teams Can StartJohn 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 Highlights00:00 – Solve Problems Before Chasing MoneyCiaran explains why he has always been more interested in solving meaningful problems than pursuing technology or quick financial returns.02:32 – Guest Introduction: Ciaran CosgraveMeet 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 TechnologyCiaran 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 OthersA 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 AccidentAfter 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 LuckCiaran explains why understanding the problem, finding great people, and acknowledging luck have shaped how he builds businesses.15:43 – What Drew Ciaran to NearformCiaran 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 DisruptedAfter 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 HardCiaran 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 WeeksAI-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 OptionalityBarry explores how cheaper experimentation allows teams to test multiple possibilities instead of placing one large bet.36:06 – When Innovation Becomes the BottleneckCiaran explains why organizations need stronger filtering mechanisms as teams generate and prototype more ideas.39:18 – Turning AI’s Potential Into RealityCiaran 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=1067626328Follow Barry O’ReillyLinkedIn: https://www.linkedin.com/in/barryoreillyWebsite: https://barryoreilly.comFacebook: https://www.facebook.com/barryoreillyauthor/X: https://x.com/barryoreillyInstagram: 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 RecapSam 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 KroonenburgMeet 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 ClickedSam 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 PlaySam 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 InstinctMoving 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 CompanySam initially brought too much structure, reporting, and sales pressure into Cuttable before refocusing on product-market fit.20:56 – Why Cuttable Went Back to ZeroAfter reaching nearly $1 million in ARR, Cuttable released every customer and rebuilt around end-to-end automation.27:48 – Why Paying Customers Change the SignalSam 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 HumanCuttable 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 OutcomesSam 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 FilterSam explains how Glitch Capital balances founder optimism with analysts and experienced operators who challenge assumptions.42:03 – Smaller Teams and Bigger ImpactSam 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=1067626328Follow Barry O'Reilly:LinkedIn: https://www.linkedin.com/in/barryoreillyWebsite: https://barryoreilly.comFacebook: https://www.facebook.com/barryoreillyauthor/X: