Stefan Wolpers

Food for Agile Thought 551: AI Confidence Theater, GitHub for PMs, Product Alignments, We Tried Agile; Didn’t Work

by Stefan Wolpers | 2026-07-03 News

TL; DR: AI Confidence Theater — Food for Agile Thought #551

Welcome to the 551st edition of the Food for Agile Thought newsletter, shared with 35,473 peers. This week, Elena Verna calls out AI confidence theater and asks teams to show real workflows, which pairs well with Teresa Torres and Petra Wille’s advice to start AI adoption with one messy to-do item. Also, Janna Bastow brings the same discipline to alignment meetings: clarify decisions before vague input becomes commitment. Anthropic frames Fable 5’s return as governance, while Alberto Romero questions the safety bargain, and Mike Cohn redirects failed Agile blame toward broken conditions.

Next, Aakash Gupta and Shubham Saboo treat PM work like code, while Hamel Husain extends that discipline to AI evaluation: track changes, show provenance, and make review paths obvious, and Tomasz Tunguz adds the cost pressure that will force selective adoption. Joost Minnaar reminds teams that rituals without shared power rot into theater, and Anthropic frames Claude Fable 5 as a teammate needing clearer boundaries.

Lastly, Ethan Mollick sees AI work shifting toward agent management, while Peter Yang expects model portfolios to reshape software economics. Charity Majors argues that leaders must support learning rather than demand unpaid adaptation, and Gergely Orosz reminds us that reinvention beats nostalgia. Finally, Abraham Thomas ties lasting progress to data quality that delivers real business outcomes by matching fitness for purpose with measurable value rather than relying solely on checklists.

Food for Agile Thought 551: AI Confidence Theater, GitHub for PMs, Product Alignments, We Tried Agile; Didn’t Work - Age-of-Product.com

If You Can Facilitate a Retrospective, You Can Audit Your AI

by Stefan Wolpers | 2026-06-28 Agile and Scrum

TL;DR: The AI Delegation Audit

Scrum teams inspect how the last Sprint went during the Retrospective. They are much less likely to inspect the work they have handed to AI, because no meeting on the calendar owns it. That gap is where a working AI automation quietly turns into risk: it keeps producing fluent, on-brand output long after the decision to trust it has expired. The AI Delegation Audit closes the gap by leveraging the facilitation skills teams already use in a Retrospective.

If You Can Facilitate a Retrospective, You Can Run the AI Delegation Audit of the A3 Framework - Age-of-Product.com

Thesis: The Delegation Audit is the missing inspection cadence for delegated AI work. It checks four things: whether the work still meets the standard, whether the model still fits the task, whether the team can still stop the automation, and whether reviewed assistance has quietly become unreviewed automation. You can try it on one workflow in fifteen minutes.

Food for Agile Thought 550: Make AI Boring, Everyone’s a Product Manager Soon, Fixing Procrastination, Agentic “Team” Topologies

by Stefan Wolpers | 2026-06-26 News

TL; DR: Make AI Boring — Food for Agile Thought #550

Welcome to the 550th edition of the Food for Agile Thought newsletter, shared with 35,481 peers. This week, Charity Majors rejects AI purity theater and urges disciplined workplace experiments, just make AI boring again, while Gojko Adzic warns that faster builders without product judgment will ship polished waste. Dave Hora names the organizational traps that keep teams from seeing reality, and Johanna Rothman brings the fix down to flow data and human judgment. Azeem Azhar and colleagues see AI demand rising, but Satya Nadella argues that a durable advantage comes from owning learning itself.

Next, Paweł Huryn moves AI work from prompt craft to agent loops with goals, guardrails, budgets, and independent checks, while Jeff Gothelf argues that AI pilots fail when firms bolt tools onto stale workflows. Joe Hudson adds that emotional clarity now beats knowledge hoarding, and John Cutler names fear, incentives, and executive fantasies as the real bottlenecks. David Burkus brings the pattern back to procrastination, where stress and ambiguity demand clarity without control.

Lastly, Elena Verna pushes experimentation beyond tiny UI tweaks toward larger monetization bets and longer engagement signals, as Zvi Mowshowitz warns AI policy needs calibrated safeguards rather than theater. Deborah Rim Moiso brings the same discipline to facilitation through communities that review real work, and Olivier Wulveryck applies Team Topologies to agentic platforms before shadow IT hardens. Finally, Itamar Gilad grounds the pattern in value, not misleading productivity counts.

Food for Agile Thought 550: Everyone’s a PM, Make AI Boring, Fixing Procrastination, Agentic "Team" Topologies - Age-of-Product.com

The AI Definition of Done: Human in the Loop Is Not a Quality Standard

by Stefan Wolpers | 2026-06-21 Agile and ScrumAgile Transition

TL;DR: The AI Definition of Done

Your team has a Definition of Done for a product increment. It has none for the 20-plus AI-supported outputs that leave the team each week: status reports, stakeholder emails, release notes, and updates for the C-level. Each one carries your team’s name. “I know quality when I see it” is the standard most teams actually run by, and you cannot audit it, teach it to a new colleague, or defend it when a claim turns out to be wrong. The AI Definition of Done fixes that with one page per task class, agreed by the team, before the output ships.

The AI Definition of Done: The Human in the Loop Is Not a Quality Standard; Check out the new template — Age-of-Product.com

Food for Agile Thought 549: AI in Product 2026, Makers Manifesto, AI POM, Open Knowledge Format

by Stefan Wolpers | 2026-06-19 News

TL; DR: AI in Product 2026 — Food for Agile Thought #549

Welcome to the 549th edition of the Food for Agile Thought newsletter, shared with 35,498 peers. This week, Product Circle and Product Institute share the AI in Product 2026 survey show AI coding tools spreading faster than stronger operating models, while Elena Verna sees cheaper software creation opening a Mom-and-Pop SaaS lane for domain experts. Petra Wille counters AI possibilities with accountable product principles, and Sam McVeety and Amir Hormati tackle agent-ready context. Also, Isabel Juniewicz and Ed Zitron question whether increasing hyperscaler spending and the economics of generative AI can sustain the rush, or bubble?

Next, Janna Bastow warns that Slack loses product feedback once channels move on, and Sarah Guo argues that AI shifts durable advantage toward private data, judgment, and trust. Aakash Gupta and Rohan Varma push the logic further, describing AI-native teams that build before they coordinate as the AI way, while Matthew Hodgson adds that enterprises need persistent funding and governance to make AI product operating models work. Then, Gregor Ojstersek shows that top engineering teams are already reshaping structures around AI.

Lastly, Mark Graban warns that tone policing in teams drives bad news underground, while Barry O’Reilly argues that AI raises the premium on visible, codified judgment that requires transparency, not enforced harmony. Johanna Rothman and Sonya Siderova shift the focus from faster tasks to slower systems, where wait times and flow debt shape delivery. Finally, Matteo Tittarelli extends that logic to GTM, where context, skills, orchestration, and integrations must compound across cycles.

Food for Agile Thought 549: AI in Product 2026, Makers Manifesto, AI POM, Open Knowledge Format - Age-of-Product.com

The AI Delegation Lifecycle: Your Team Has AI Outputs. Where Are the Decisions?

by Stefan Wolpers | 2026-06-14 Agile and ScrumAgile Transition

TL; DR: The AI Delegation Lifecycle

Your team ships AI outputs that nobody fully trusts; you needed to be quick, and “dirty” tagged along. That ungoverned automation becomes AI debt the moment a stakeholder asks who owns it. The AI Delegation Lifecycle turns six agile skills you already practice into six explicit decisions that govern delegated AI work and produce audit-ready evidence without a separate report.

Your Team Has AI Outputs. Where Are the Decisions? How the AI Delegation Lifecycle Augments the A3 Decision Framework - Age-of-Product.com