Definitely, Maybe Agile

Peter Maddison and Dave Sharrock
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Adopting new ways of working like Agile and DevOps often falters further up the organization. Even in smaller organizations, it can be hard to get right. In this podcast, we are discussing the art and science of definitely, maybe achieving business agility in your organization.

  1. 6d ago

    How to Validate AI Output Before You Trust It

    AI agents will tell you everything works. Here is how to validate AI output when the mistakes are ones no human would ever make. Peter and Dave compare notes from building software, training material, and presentations with AI. The problems they keep running into are strange ones: the wrong database ID dropped into a system, 236 passing tests that test nothing, and five layers of abstraction added to a solution that needed none of them. Their answer borrows from good agile practice. Define what validation means before the agent starts, the same way a team agrees on its definition of done. Keep checking that the context you gave the AI is still relevant as the work moves. And keep someone in the loop who knows the problem well enough to look at the result and say, "Actually, you got this wrong." This week's takeaways: - Decide what "done" and "valid" mean before you hand work to an AI agent, and use a separate agent to grade what comes back. - A test suite that pings components or mocks everything out can report hundreds of passes and prove nothing, so ask for synthetic transactions that run through the whole system. - AI will happily over-engineer a small problem, so someone needs enough knowledge to ask whether the solution fits the size and scale of what you are actually trying to solve. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  2. Sep 24

    Why AI Agents Don't Belong on Your Org Chart

    Giving AI agents names and a spot on the org chart makes people feel less accountable for the work, and it squeezes the agent into a role it doesn't need. Dave Sharrock and Peter Maddison dig into new research from the September/October 2026 issue of Harvard Business Review on what happens when organizations add named AI agents to the org chart. When people use AI as a tool, they own the result and check it carefully. When the same work comes from a personified agent, accountability quietly shifts to the software, and nobody is really holding it. Peter connects this to the idea of accountability sinks, the layers in an organization where responsibility disappears. They also talk about review fatigue, the growing pile of AI-generated documents, slides, and code that someone still has to read, understand, and sign off on. Onboarding an agent like a new hire still makes sense. Leaving it on the org chart with a name is where the trouble starts. This week's takeaways: - How people work alongside capable AI agents is still poorly understood, and the old problems of ownership, accountability, and decision making do not go away just because an agent is involved. - Review fatigue is real and getting worse, because cheap generation still means expensive human review for every artifact someone has to approve. - Naming an agent into a human role limits it to the shape of that role, while treating it as a tool in the workflow lets it work across boundaries in ways a person cannot. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  3. Sep 17

    AI Changes The Real Bottleneck with Bernie Maloney

    AI speeds up building. It doesn't speed up deciding. The real bottleneck has moved to management, and organizations don't see it yet. In this episode, Peter and Dave talk with Bernie Maloney about why AI adoption is exposing a critical gap in organizational leadership. AI is accelerating delivery and implementation. But the pressure for deciding what's worth building has moved upstream faster than most companies can adapt. That means managers can't just be administrators anymore. They need to understand the work hands-on (the "player" part) while coaching teams to see the bigger strategic picture. Bernie walks through a three-dimensional view of Agile: output (how to build), outcome (how to solve problems), and impact (what problems are worth solving). Most organizations only operate in one. He also explains why the discovery plane—figuring out what to build in the first place—has to move closer to teams instead of staying locked at the management layer. That's where the real bottleneck lives now, not in engineering. This week's takeaways: Agile moved the bottleneck from production to deployment. AI is moving it again, this time to deciding what's worth building in the first place.Managers need to become player coaches: staying close to the actual work so they understand what's possible, while coaching people to think strategically about the system they operate in.Psychological safety and tolerance for intelligent failure have to come before speed. Without them, faster delivery just means faster mistakes.Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  4. Sep 9

    Why Faster Isn't Better with Dave West

    Don't abandon Agile for AI. The real bottleneck isn't speed of delivery, it's organizational decision-making and clarity about value. Dave West, CEO of Scrum.org, warns that organizations adopting AI are making a critical mistake. They're abandoning foundational Agile practices, sprint planning, daily standups, retrospectives-  under the assumption that AI's speed removes the need for those ceremonies. But Dave's seen this pattern repeatedly: when organizations get faster delivery tools, the real bottlenecks become visible. And they're never about speed. They're about decision-making clarity, aligned incentives, and organizational structures that can't move that fast anyway. This conversation with Peter and Dave explores what's actually breaking in organizations that try to bolt AI onto broken systems. This week's takeaways: Don't throw out sprint ceremonies just because AI makes delivery faster. The intent behind planning, reviews, and retrospectives is still valid. Rethink how they work differently, not whether they're still needed.Understand the incentives driving individual behaviors, not just the organizational KPIs you set. Invisible incentives like status, power, and recognition often outweigh what's written on paper.When navigating organizational change around AI adoption, spend time understanding what actually motivates the people in the room. Ask simple questions and learn what's in it for them personally. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  5. Aug 13

    What Happened to the Scrum Master Role?

    The Scrum Master role hasn't disappeared. It's been diluted, pushed outside the organization, or absorbed into everyone's job description. Peter Maddison and Dave Sharrock dig into what's actually happened to the Scrum Master and agile coach role as AI reshapes how teams work. They trace how a role that once had real impact got watered down as two-day certifications flooded the market, and why the deeper value was never the role itself but the culture of continuous improvement it was meant to build. Using Toyota's andon cord as a lens, they compare organizations that treat problems as learning opportunities against those just racing to get the line moving again. The stronger realization is understanding when continuous learning shifts from being a role-driven practice to a part of the culture. This week's takeaways: - The standalone Scrum Master role got diluted as it became widespread, and the certifications never guaranteed the facilitation and coaching skill the role actually required. - The real value was never the role, it was building a culture of continuous improvement, and that culture works best as a shared responsibility across a team rather than one person's job. - Toyota's andon cord shows the difference between organizations that treat a pulled cord as a signal to learn versus organizations that only care about getting the line moving again. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  6. Aug 6

    What Your AI Token Spend Is Actually Buying

    AI vendors are shifting from flat-fee pricing to consumption-based billing, and most organizations have no idea what their token spend is actually producing. Dave Sharrock and Peter Maddison break down what's driving the shift to AI token economics, why the old "$20 per user" budget model is breaking down, and why usage alone is the wrong thing to optimize for. They dig into the pattern showing up across organizations, where a small share of users account for half the token spend, and why chasing that number down misses the real question: what value did that spend create? The conversation covers KPI traps, model selection tradeoffs, and how to build the kind of honest, open culture that lets you actually govern AI spend without punishing your best people. This week's takeaways: - Token usage by itself is a bad KPI once your organization has moved past early AI adoption, because it stops measuring exploration and starts driving the wrong behavior. - The 10% of users driving 50% of the token spend aren't automatically the problem. Some are generating outsized value, and the only way to know is to ask them directly. - Managing AI cost well means pairing spend visibility and caps with an honest conversation about the value that spend is producing, not just sorting a table by usage. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  7. Jul 30

    Why AI Agents Need Room to Fail Before They Learn

    Giving an AI agent real autonomy means accepting it will fail early and often before it gets good, the same curve organizations hit during any real change. Peter Maddison brings a stuck OAuth problem to the table: an AI agent that kept going in circles and couldn't find its way through. That leads into a conversation from Dave Sharrock's local AI meetup about an AlphaGo-style approach to AI agent autonomy: instead of specifying every step, you define hard constraints and let the model work out its own strategy inside them. Peter and Dave connect this to the Virginia Satir change curve, the same dip in performance that shows up when an organization tries a new way of working, and to the difference between using AI to optimize what you already do versus using it to rethink the business itself. They also get into how experiments like Andon Labs' AI-run cafes and vending machines use small dollar constraints to let a model learn from failure without real financial risk. This week's takeaways: - A well-articulated objective with clear guardrails lets an AI agent find its own path to a solution, even one you didn't expect or fully understand. - Real learning, whether it's an AI agent or an organization adopting a new way of working, comes with an unavoidable dip in performance that can't be planned away. - The bigger opportunity with AI isn't squeezing more efficiency out of an existing process, it's using AI to test entirely different ways a business could operate. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

  8. Jul 23

    AI Coding Speed Isn't Agile's Real Bottleneck

    AI can write code faster than ever, but Peter Maddison and Dave Sharrock argue that coding was never the actual bottleneck in software delivery. In this conversation about AI software delivery, Peter and Dave dig into why faster coding hasn't solved the two problems that always mattered: knowing whether what you built is actually valuable, and knowing what to build in the first place. They connect this back to sprint length, arguing it was never set by how hard the coding is, but by how fast an organization can learn and decide. As AI generates more options and even makes decisions on our behalf, the conversation turns to what happens when judgment can't keep pace with output, and why product owners and stakeholders still need real time to validate high-risk calls. This week's takeaways: - Coding speed was never the real constraint. The two problems that still matter are knowing if something is valuable and knowing what to build in the first place. - Sprint length should be set by your organization's decision and learning latency, not by how fast code can be written. - As AI generates more options and even makes decisions for you, leaders need time and context to validate high-risk calls, because judgment doesn't speed up as easily as output does. Listen to the full episode at definitelymaybeagile.com Subscribe so you never miss an episode. Have a question or topic you'd like us to cover? Reach out at feedback@definitelymaybeagile.com

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Adopting new ways of working like Agile and DevOps often falters further up the organization. Even in smaller organizations, it can be hard to get right. In this podcast, we are discussing the art and science of definitely, maybe achieving business agility in your organization.

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  • Creator
    Peter Maddison and Dave Sharrock
  • Years Active
    2021 - 2026
  • Episodes
    235
  • Rating
    Clean
  • Copyright
    © 2026 Definitely, Maybe Agile
  • Show Website
    Definitely, Maybe Agile