Two weeks ago, I asked my audience whether they wanted a short course on moving from Scrum to a Product Operating Model, and 22 answered. That was not the Scrum-to-POM dataset I hoped for, but it was valuable for the conversations. Interestingly, one pattern ran through more than a quarter of the responses: The people writing back were not asking about transformation practices or operating models. They were asking what was about to happen to their jobs.
Let me paraphrase some of their replies: One Agile Coach wrote that their role had already been made redundant, and the internal training their employer offered was not enough. Another asked a blunt question: “What will happen to my role?” A third described leadership, saying they wanted this shift, while their behavior remained inconsistent. A fourth reported confusion about what a product coach actually is. A fifth dismissed the whole discourse as high-level fluff, transformational buzzwords, zero accountability, and vague systems thinking with no teeth.
My takeaway: While the organizational design debate appears to be the surface, the ongoing role repositioning is what the people on the ground are living through.
AI tools are reshaping how Scrum Teams work, and Scrum Masters who cannot coach their teams through this shift are not ready for 2026. This article presents ten Scrum Master interview questions that test whether a candidate can facilitate AI adoption without losing self-management. As usual, each question includes guidance on answers and red flags. The questions are drawn from the seventh edition of the 97 Scrum Master Interview Questions guide.
Most micromanagement is not a control problem; it is a clarity failure in disguise. This article introduces Commander’s Intent: a five-part briefing model that replaces prescriptive instructions with shared purpose, hard constraints, and room to adapt.
Bonus: As a Claude user, you can download the Commander’s Intent1 Skill.
by Stefan Wolpers|FeaturedAgile and ScrumAgile Transition
Using AI at Work Does Not Mean You Understand It
Many agile practitioners use ChatGPT at work. That does not mean they understand AI well enough to trust your own judgment. The problem is not that agile practitioners ignore AI. The problem is that many already use it confidently without knowing where their judgment breaks down. The free AI4Agile Foundational Assessment measures precisely this skill gap. (Download your access file below.)
The assessment comprises 40 scenario-based questions. It does not ask for definitions, but puts you into situations that agile coaches, product managers, and Scrum Masters face every week: weak prompting producing generic output, misleading data analysis, questionable agent output, and, possibly, organizational pressure to treat AI output as “good enough” to go with it.
Most people who use AI do not fail because they lack knowledge, but because they cannot distinguish between plausible outputs and trustworthy judgment. But see for yourself!
Jira was named after Godzilla and built to track bugs. It became the default agile tool because it satisfied a deeply human desire: controlling work by putting it in boxes with statuses, assignees, and due dates. That system works for humans scanning dashboards. It does not work for autonomous agents that need to reason about patterns across iterations, detect recurring problems, and forecast what is likely to break next. This article argues that the tool on which 62% of agile teams rely is about to be demoted from knowledge authority to execution interface. We need to move from Jira to AI Agents.
by Stefan Wolpers|FeaturedAgile and ScrumAgile Transition
TL; DR: AI Thinking Skills for Agile Practitioners
Most agile practitioners use AI to produce outputs more quickly. Few use it to think better. This free download gives you three AI thinking skills (Socratic Explorer, Brutal Critic, Pre-Mortem) that turn Claude into a partner for diagnosing problems, stress-testing plans, and anticipating failures before they happen.
