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.

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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.

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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.

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