Stefan Wolpers
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Articles by Stefan Wolpers
Avoiding AI Debt as a Team
TL; DR: Reusing Agile Artifacts to Avoid Accumulating AI Debt In this video from the 76th Hands-on Agile Meetup, I walk you through the A3 Delegation System and show how it helps avoid AI debt by borrowing artifacts and practices from Agile, such as the Definition of Done and Retrospectives.
Food for Agile Thought 566: Building AI Products, Feature Factory at 10, PM Skills Report 2026, Team Dynamics Suffer from AI
TL; DR: Building AI Products — Food for Agile Thought #566 Welcome to the 566th edition of the Food for Agile Thought newsletter, shared with 35,248 peers. This week, Ian Vanagas suspects user conversations will outlast half his lessons in building AI products, whereas John Cutler sees AI amplifying feature-factory habits. With features cheap, Aakash Gupta and Tim Herbig warn that wrong choices get expensive.
The Jobs Matrix for AI: Four Boxes Instead of 40 Tools
Join the DZone community and get the full member experience. Join For Free TL;DR: A 2×2 Matrix on AI for Agile Practitioners You probably think making sense of AI means keeping up with every new tool, agent, and pricing tier. Steve Jobs faced a similar mess at Apple in 1997, with a dozen versions of the Macintosh, and he fixed it with a two-by-two grid on a whiteboard.
To Switch, or Not to Switch the AI Model
TL;DR: The Hidden Cost of Switching AI Models A new AI model arrives that is cheaper per token, scores better on benchmarks, and seems to require nothing more than a setting change. However, the token bill shows only part of the cost of switching AI models. There is also the effort to check whether your workflows, for example, the one to create status reports for stakeholders, still produce outputs in line with your AI Definition of Done.
AI on Top of a Dysfunctional System
Join the DZone community and get the full member experience. Join For Free TL;DR: Polished Artifacts, Unchanged Decisions, Or Ten Backlog Anti-Patterns AI Makes Worse Add AI to a Product Backlog process that already struggles, and everything seems to improve within an afternoon.
The Jobs Matrix for AI: Four Boxes Instead of Forty Tools
TL;DR: A 2×2 Matrix on AI for Agile Practitioners You probably think making sense of AI means keeping up with every new tool, agent, and pricing tier. Steve Jobs faced a similar mess at Apple in 1997, with a dozen versions of the Macintosh, and he fixed it with a two-by-two grid on a whiteboard. Almost 30 years later, the “Jobs Matrix for AI” sorts the AI tool market and, more usefully, the AI use cases of Scrum Masters, Product Owners, Agile Coaches, and anyone else in agile product development.
Can Your Team Name the Work It Already Runs With AI?
Join the DZone community and get the full member experience. Join For Free TL;DR: The AI Workflow Inventory Finalizes the A3 Delegation System You probably know your own AI shortcuts: the colleague who drafts the stakeholder update, the nightly job somebody set up before they left, the interview notes that go through a model on demand.
AI on Top of a Dysfunctional System: The Product Backlog
TL;DR: Polished Artifacts, Unchanged Decisions, Or Ten Backlog Anti-Patterns AI Makes Worse Add AI to a Product Backlog process that already struggles, and everything seems to improve within an afternoon. The problem is that polishing artifacts with AI doesn’t fix the root cause: the basis for the team’s decisions doesn’t change; AI only removes the visible discomfort that used to signal something was broken, along with some of the pressure to fix it.
AI Transformations and Agile Transformations Rhyme
TL;DR: A Déjà-Vu? AI adoption seems to be scaling: 37% of respondents in McKinsey’s 2026 survey report an EBIT effect from AI, and Gartner finds that 22% of organizations have scaled it across business units. Now, Agile practitioners have seen this combination before, as AI transformations and Agile transformations rhyme.
The AI Workflow Inventory: Can Your Team Name the Work It Already Runs With AI?
TL;DR: The AI Workflow Inventory Finalizes the A3 Delegation System You probably know your own AI shortcuts: the colleague who drafts the stakeholder update, the nightly job somebody set up before they left, the interview notes that go through a model on demand. However, I want to challenge you: that perceived knowledge can create false confidence, because it feels like knowing the team’s way of working with AI, when it is still only a diffuse understanding of the practice.
The Folly of Tokenmaxxing or Reinventing the Wheel
TL; DR: Tokenmaxxing or Reinventing the Wheel Your organization counts AI tokens, seats, and pilots, but can anyone name a single decision those numbers actually changed? Tokenmaxxing is only the symptom; five old Agile Laws explain the cause, and each one comes with a test you can run this week. There is no need to reinvent the wheel with AI transformations and learn the hard way what the veterans of other transformations already figured out.
Product Owner Interview Questions for the Age of AI
TL; DR: 10 New Product Owner Interview Questions on AI “How do you use AI in your work?” If that is still one of your Product Owner interview questions, you are screening for tooling fluency in a role that lives or dies on product judgment. Every candidate has an answer; LLMs make sure of that. None of those answers tell you whether the person can decide what is worth building when building is no longer the hard part. These ten new Product Owner interview questions are designed to expose the gap.
Food for Agile Thought 558: Transformation Failure, POM Before AI, Velocity vs. Opportunity Costs, Influence and Rank
TL; DR: Transformation Failure — Food for Agile Thought #558 Welcome to the 558th edition of the Food for Agile Thought newsletter, shared with 35,366 peers. This week, Nigel Thurlow and Stephanie Leue tackle leadership: Thurlow analyzes patterns of transformation failure, showing that leaders seek change only when they hold power, while Stephanie Leue proposes that steady presence, not control, builds authority.
From Agile to the Product Operating Model
TL; DR: The “Agile to the Product Operating Model” Survey Results Between August 2 and August 10, 2026, 48 practitioners participated in my Agile to Product Operating Model (POM) survey, which tries to shed light on what is actually changing. Let me summarize the answers for you: the reported transformations change decision-making less than the Cagan framework suggests. Where respondents report improvements, they appear in delivery and collaboration rather than in business results.
The Folly of Tokenmaxxing, or What AI Learns About Legacy Organizations That Agile Already Knows
TL; DR: Tokenmaxxing Or Reinventing the Wheel Your organization counts AI tokens, seats, and pilots, but can anyone name a single decision those numbers actually changed? Tokenmaxxing is only the symptom; five old Agile Laws explain the cause, and each one comes with a test you can run this week. There is no need to reinvent the wheel with AI transformations and learn the hard way what the veterans of other transformations already figured out.
Food for Agile Thought 557: AI Watermarks, A New Product Role, Skill Loss and GenAI, Do We Prioritize Value?
TL; DR: AI Watermarks — Food for Agile Thought #557 Welcome to the 557th edition of the Food for Agile Thought newsletter, shared with 35,378 peers. This week, Anthropic details the AI watermarks and C2PA metadata Claude now attaches under the EU AI Act, while Dror Poleg shows machines spot each other through word statistics that every writer carries.
Survey: From Agile to a Product Operating Model: What Is Actually Changing?
Product Operating Model or Product Washing? The debate over the product operating model (POM) has a data problem. Most of us, including me, argue from the organizations we know.
Food for Agile Thought 555: Rogue AI Agents, New Product Roles, Product Sense, Team Cohesion
TL; DR: Rogue AI Agents — Food for Agile Thought #555 Welcome to the 555th edition of the Food for Agile Thought newsletter, shared with 35,412 peers. This week, Hugo Larcher and colleagues, along with Anthropic’s Frontier Red Team, demonstrate how weak containment enables rogue AI agents to turn tests into real breaches. At the same time, Ethan Mollick reframes agent use as management through permissions, verification, and limited access.
How the A3 Delegation System Helps to Avoid AI Debt Borrowing from Agile Artifacts
TL; DR: Reusing Agile Artifacts to Avoid Accumulating AI Debt In this video from the 76th Hands-on Agile Meetup, I walk you through the A3 Delegation System and show how it helps avoid AI debt by borrowing artifacts and practices from Agile, such as the Definition of Done and Retrospectives. If you’d like to download the corresponding canvases (the artifacts of the A3 Delegation System), you can do so below.
Food for Agile Thought 554: Toyota Production System, Pressure-Testing Product Ideas, Nokia’s Demise, Drowning in Work?
TL; DR: Toyota Production System — Food for Agile Thought #554 Welcome to the 554th edition of the Food for Agile Thought newsletter, shared with 35,428 peers. This week, Nigel Thurlow presents the Toyota Production System (TPS) as a disciplined whole, a lesson Pavel Samsonov and Ash Maurya extend to product work: faster AI delivery only magnifies incoherence without workflow thinking, customer evidence, and validation.
Food for Agile Thought 553: Dangerous Agile Myths, Produce Evidence Quality, Running Experiments, Playing Politics?
TL; DR: Dangerous Agile Myths โ Food for Agile Thought #553 Welcome to the 553rd edition of the Food for Agile Thought newsletter, shared with 35,462 peers. This week, Henrik Mรฅrtensson dismantles seven dangerous Agile myths, showing that fat-tailed cycle-time data invalidates the use of story points. Teresa Torres and Petra Wille question whether support tickets can replace story-based interviews, while Roman Pichler pushes visions beyond feature lists toward purpose.
You Already Have an AI Working Agreement. Write It Down.
TL;DR: The AI Working Agreement Your team already has rules for using AI. Some live in templates, some in habits, exceptions, and one person’s memory. The AI Working Agreement puts the decisions that matter in one place: what the team delegates to AI, what stays human, what must be reviewed, what never enters a model, who owns which workflow, and how the agreement changes.
You Already Have an AI Working Agreement. Write It Down.
TL;DR: The AI Working Agreement Your team already has rules for using AI. Some live in templates, some in habits, exceptions, and one person’s memory. The AI Working Agreement puts the decisions that matter in one place: what the team delegates to AI, what stays human, what must be reviewed, what never enters a model, who owns which workflow, and how the agreement changes.
If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy
TL;DR: The AI Routing Policy You moved your routine AI work to a cheaper model, so you think the cost question is handled; however, often, that is not the case. The decision lives in one person’s head and produces nothing that the person accountable for the invoices can read. Worse, it is an architectural choice nobody documented.
Food for Agile Thought 552: AI Creates Jobs? Product Roadmaps & Leader Anxiety, Overthinkers, Measuring ≠ Learning
TL; DR: AI Creates Jobs? — Food for Agile Thought #552 Welcome to the 552nd edition of the Food for Agile Thought newsletter, shared with 35,468 peers. This week, Ramp Economics Lab and Revelio Labs report that heavy AI adopters grew headcount by 10%, yet Charity Majors insists only honest feedback loops turn adoption into results. Alex Karp questions the economics entirely, calling token pricing fundamentally broken.
If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy
TL;DR: The AI Routing Policy You moved your routine AI work to a cheaper model, so you think the cost question is handled; however, often, that is not the case. The decision lives in one person’s head and produces nothing that the person accountable for the invoices can read. Worse, it is an architectural choice nobody documented.
Food for Agile Thought 551: AI Confidence Theater, GitHub for PMs, Product Alignments, We Tried Agile; Didn’t Work
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.
If You Can Facilitate a Retrospective, You Can Audit Your AI
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.
The AI Definition of Done
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: Human in the Loop Is Not a Quality Standard
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.
Food for Agile Thought 549: AI in Product 2026, Makers Manifesto, AI POM, Open Knowledge Format
TL; DR: AI in Product 2026 โ 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.
Your Team Has AI Outputs. Where Are the Decisions?
TL; DR: The AI Delegation Lifecycle Your team ships AI outputs nobody decided to trust fully; you needed to be quick, and “dirty” tagged along. Too bad, that that ungoverned automation becomes AI debt when a stakeholder asks who owns it. But do not despair: The AI Delegation Lifecycle turns skills you already use into six decisions you can apply this week to govern that work and prove it, audit-ready and suited for agent harnesses.
Food for Agile Thought #548: ROT (Return on Tokens), Product Team Health, Engineers & PMs, AI Treadmill
TL; DR: ROT (Return on Tokens) — Food for Agile Thought #548 Welcome to the 548th edition of the Food for Agile Thought newsletter, shared with 35,528 peers. This week, Packy McCormick and Markie Wagner call token maxing wasteful: AI should compile processes into code, not burn tokens at runtime; ROT (return on tokens) is essential. Deb Liu warns that chasing efficiency gains only builds a faster treadmill, while Elena Verna insists companies need employees with agency, not more agents.
Stop Prompting, and Start Building Compounding AI Systems
TL;DR: Compounding Systems and Agents Go Hand-in-Hand Every AI conversation starts from zero because the model forgets who you are. The Claude Cowork Online Course teaches you to change that: build persistent Skills, connect your tools, and assemble agents for recurring work. No coding. Thesis: “A prompt disappears after one use; a Skill compounds across every session.” This closes the TL;DR as the extractable assertion. An answer engine pulling only this sentence gets the article’s central claim.
Food for Agile Thought #547: AI’s 1997 Internet Moment, Code Isn’t Product, Cognitive Surrender, Admitting Mistakes Is Not Enough
TL; DR: AI’s 1997 Internet Moment — Food for Agile Thought #547 Welcome to the 547th edition of the Food for Agile Thought newsletter, shared with 35,532 peers. This week, Benedict Evans tells Lenny Rachitsky that today marks AI’s 1997 Internet Moment and asks whether automation kills tasks or jobs, which Casey Newton’s guest, Kathryn Anne Edwards, treats as real but manageable, faulting unemployment insurance rather than fearing an idle underclass.
Eric Ries’ ‘Incorruptible’ Solved the Later Problem. AI Builders Have an Earlier One.
TL; DR: The Problem of AI Builders Eric Ries’ new book ‘Incorruptible’ solves a problem most readers will not face for years: protecting a valuable organization from capture once it succeeds. The builders that AI is creating hit an earlier issue: Building software used to force the question of whether it was worth building. That gate has largely collapsed. Eric Ries asks how mission survives success; we, the normals, how judgment survives abundance.
Food for Agile Thought #546: Choosing to Stay Human, Customer Research by LLM, AI Product-Market Fit, Enterprise Agility Today
TL; DR: Choosing to Stay Human โ Food for Agile Thought #546 Welcome to the 546th edition of the Food for Agile Thought newsletter, shared with 35,551 peers. This week, Anthropic shipped Claude Opus 4.8, which flags its uncertainty more readily, a fitting cue for Stephanie Leue, who argues no CPO embodies all nine roles a job description demands, so honest leaders name their gaps. Jeff Gothelf reframes agentic engineering as product management, since judgment outlasts typing.
Dear Micromanager: Your Distrust Has a Job; It’s Just Not the One You’re Doing
TL;DR: Why A Former Micromanager Will Make AI Adoption Work Twenty years of Agile coaching failed to fix the micromanager who meddles with every draft, every meeting, every decision. This article shows where their distrust stops damaging teams and starts producing the verification work AI adoption actually needs. Welcome the Verification Architect! What Is a Verification Architect?
No More Cheap Claude: 4 First Principles of Token Economics in 2026
TL;DR: Token Economics in the Era of Scarcity Your Claude Pro subscription hits limits faster than it did in January, as Anthropic quietly re-priced the ceiling, and every AI provider is rationing compute. If you keep working with Claude the way you did six months ago, you are in for a rude awakening. This article gives you four principles that explain how Token Economics actually works, so you can stop accepting the black box and start using your budget deliberately.
Dear Micromanager: Your Distrust Has a Job; It’s Just Not the One You’re Doing
TL;DR: Why A Former Micromanager Will Make AI Adoption Work Twenty years of agile coaching failed to fix the micromanager who meddles with every draft, every meeting, every decision. This article shows where their distrust stops damaging teams and starts producing the verification work AI adoption actually needs. Welcome the Verification Architect! What is a Verification Architect?
Working With Cowork: Don’t Be Confused
TL;DR: Understand the Claude Desktop Architecture and Save Time You configured Claude in Claude Desktop, wrote instructions, uploaded reference files, and set your preferences. Then you clicked the Cowork tab. Unfortunately, Claude had no memory of what you just did. Your instructions were gone, as were your files and preferences. You assumed this was a bug, but it is a feature: You switched applications.
Working with Cowork: Claude Desktop Is Three Apps Pretending to Be One
TL;DR: Understand the Claude Desktop Architecture and Save Time You configured Claude in Claude Desktop, wrote instructions, uploaded reference files, and set your preferences. Then you clicked the Cowork tab. Unfortunately, Claude had no memory of what you just did. Your instructions were gone, as were your files and preferences. You assumed this was a bug, but it is a feature: You switched applications. You have been prompting AI for months.
Food for Agile Thought #543: AI Playbook, Product Taste, Competitor Intelligence Agent, CEO AI Psychosis
TL; DR: AI Playbook, Product Taste — Food for Agile Thought #543 Welcome to the 543rd edition of the Food for Agile Thought newsletter, shared with 35,597 peers. This week, John Cutler warns in his AI Playbook that AI accelerates broken practices just as much as good ones, while Guy Champniss adds that the psychological toll of AI adoption on employee motivation may quietly erase the productivity gains organizations expect.
The AI Spending Trap: Why Adoption Outpaces Outcomes
TL;DR: AI Adoption Issues Sound Familiar to Agile Practitioners If you have spent the last twenty years arguing that velocity is not value, that adoption is not impact, that an Agile transformation is not a Jira migration, the Stanford AI Index 2026 will read like déjà vu: The technology is new. The failure mode, the AI spending trap, is not.
The Scrum-to-POM Transition Is a Role Repositioning Event
TL; DR: Scrum-to-POM Is Not About Frameworks 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.
Food for Agile Thought #541: GPT-5.5, Product Managers in Trouble, Product on Speed, Wastes of Lean as a Chance
TL; DR: GPT-5.5 & Product on Speed — Food for Agile Thought #541 Welcome to the 541st edition of the Food for Agile Thought newsletter, shared with 35,619 peers. This week, OpenAI’s GPT-5.5 signals another meaningful capability jump, with Ethan Mollick noting that stronger models and richer tool harnesses now handle serious work, even as creative judgment still exposes AI’s limits.
No More Cheap Claude: Four First Principles of Token Economics in 2026
TL;DR: Token Economics in the Era of Scarcity Your Claude Pro subscription hits limits faster than it did in January, as Anthropic quietly re-priced the ceiling, and every AI provider is rationing compute. If you keep working with Claude the way you did six months ago, you are in for a rude awakening. This article gives you four principles that explain how Token Economics actually works, so you can stop accepting the black box and start using your budget deliberately.
Food for Agile Thought #540: Change Needs Glue People, AI Tool FOMO, Product Builders, Stanford’s 2026 AI Report
TL; DR: Change Needs Glue People — Food for Agile Thought #540 Welcome to the 540th edition of the Food for Agile Thought newsletter, shared with 35,625 peers. This week, John Cutler warns that replacing “glue people” with AI ignores the invisible judgment and political navigation that made their work valuable, while Teresa Torres and Petra Wille suggest that resisting AI tool FOMO and focusing on real problems leads to deeper learning.
Food for Agile Thought #539: Sam Altmann? Bottom-Up Roadmaps, Cycles of Disruption, The Last Four Jobs
TL; DR: Sam Altmann — Food for Agile Thought #539 Welcome to the 539th edition of the Food for Agile Thought newsletter, shared with 35,623 peers. This week, Ronan Farrow and Andrew Marantz investigate Sam Altman’s leadership at OpenAI, exposing a pattern of deception and safety trade-offs, while Ajeya Cotra predicts AI research parity could arrive by 2028, and Annie Vella reports from a retreat where nobody had AI figured out yet.
Stop Telling Professionals How to Do Their Job — Commander’s Intent at Work
TL; DR: Commander’s Intent Skill 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. 🗞 Shall I notify you about articles like this one?
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