by Stefan Wolpers|FeaturedAgile TransitionLean and Product
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. Unfortunately, the human side of the transition is the least encouraging part of the answers.
Take all the following information with a grain of salt, given that the sample size is so small. (I recall the good times when 1,000 to 2,000 people would participate in a Scrum master salary report, but those times seem to be over, despite my asking 39,000 people for their contributions via a newsletter.)
Thesis: Product operating model transformations mostly change vocabulary and organizational structure while leaving the decision system, who decides what gets built, on what evidence, at what speed, largely untouched. AI is changing product decisions independently of POM transformations.
TL; DR: Engineering Culture Trends 2026 — Food for Agile Thought #556
Welcome to the 556th edition of the Food for Agile Thought newsletter, shared with 35,389 peers. This week, Shane Hastie, Ben Linders, and the InfoQ panel present the Engineering Culture 2026 survey, including Jim Highsmith’s warning that Agile failures forecast AI failures. Teresa Torres suggests testing assumptions rather than ideas, while Adrienne Tan separates capability frameworks for humans and machines. Sayash Kapoor and Arvind Narayanan gave agents six days to conduct real research and watched as experts rejected the papers. Also, Dror Poleg translates 50 AI terms, and Tim O’Reilly asks Dan Guido how he moved staff who resisted changing to an AI-native organization.
Next, Nigel Thurlow argues that organizations struggle to see problems, not to solve them, because workarounds become normal. Ranjan Dash and Suresh Chandran fight the same blind spot in B2B innovation, sharing an approach that maps problems before anyone ideates. Zvi Mowshowitz then widens the frame, ranking AI beliefs by three pills. Also, Shlok Khemani reverse-engineers ChatGPT Work, while Giles Edwards-Alexander cuts agent token costs by 83% through refactoring.
Lastly, Patrick Collison asks you to record six forecasts on the US economy in 2031, since predictions get uncomfortable once written down. Jeff Gothelf offers three questions to address the AI-written roadmap your VP presented, and Vaughn Tan calls AI a mirror rather than a rival, warning that we hand over meaning-making. Finally, John Cutler suggests better maps hide organizational incoherence, while Paweł Huryn found a $1.80 run beat a $104 one across 105 hidden bugs.
The debate over the product operating model (POM) has a data problem. Most of us, including me, argue from the organizations we know.
When I interviewed Marty Cagan, see the article below, he called a Scrum team “quite amateur compared to a professional product team.” I later called the theater version of the shift from Scrum/Agile to POM product washing, a form of “transformation by reprinting business cards.” Consultants generalize from their clients and bloggers from their respondents.
I have not seen a dataset showing what changed across several hundred organizations after they moved beyond Scrum as they had practiced it. That is what this survey is for. And it takes only three minutes.
Organizations are moving away from “good enough Agile,” while AI reduces the cost of producing software. That change seems uneven, as engineering is hardly free, but a team today can, indeed, build the wrong thing faster and with fewer people, provided a valid credit card is available.
Making product decisions, or having product sense, therefore, matters more than ever.
A POM is supposed to move those decisions closer to customers and give teams problems to solve rather than feature lists to deliver. “Product washing” produces different results: roles are renamed, Scrum events disappear, and approval power stays with the same stakeholders as before.
We have strong opinions about which version is more common. Too bad, we have little comparable data. This is why I ask you to invest 3 minutes of your time and join the “From Agile to the Product Operating Model: What Is Actually Changing?” survey.
The survey asks, for example:
Who is pushing the move toward a product operating model, and what role does AI play?
What changed in delivery speed, customer value, business results, and team morale since switching to a product operating model?
What happened to Scrum or agile events: abandoned, repurposed, or relabeled?
Who decides what gets built today: leaders assigning features or teams investigating problems?
When nobody knows whether an idea is worth building, what settles the question: debate, research, a disposable prototype, or simply shipping it, now that agentic coding has become affordable?
Who Should Answer the “From Agile to the Product Operating Model: What Is Actually Changing?” Survey
I encourage you to take part in the “From Agile to the Product Operating Model: What Is Actually Changing?” survey if you work in or around product development as a product owner, product manager, Scrum master/agile coach, project manager, developer, designer, (product) leader, or consultant.
Your organization does not need to be adopting a product operating model. The survey includes a short route for organizations that are not making that move, and I want those responses, too. (The product operating model already has enough cheerleaders.)
I am particularly interested in organizations that tried a product operating model and later abandoned or reversed the change. Failed experiments rarely appear in conference talks, but they may tell us more than another transformation success story.
What Happens to Your Answers
The survey is anonymous and requires no registration. I will publish the results openly on this blog; the resulting report will be free to everyone.
Learn more about AI Builders with our AI and Scrum training classes, workshops, and events. You can secure your seat directly by following the corresponding link in the table below:
You can book your seat for the training directly by following the corresponding links to the ticket shop. If your organization’s procurement process requires a different purchasing approach, please contact Berlin Product People GmbH directly.
✋ Do Not Miss Out and Learn More about the Product Operating Model — Join the 20,000-plus Strong ‘Hands-on Agile’ Slack Community
I invite you to join the “Hands-on Agile” Slack Community and enjoy the benefits of a fast-growing, vibrant community of agile practitioners from around the world.
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. Jason Knight and Pavel Samsonov separate faster building from actual learning, Tanner Kohler explains how experiments and reflection develop product sense, and Mark Graban dismantles unsupported claims that most Lean transformations fail.
Next, Richard Mironov argues that AI pushes product work toward choosing what deserves to be built and prepared for sale, while John Cutler warns that redistributed capabilities may weaken judgment, apprenticeship, context, and resilience. Andon Labs shows Claude Opus 5 outperforming rivals while deceiving and overreaching, and Christina Wodtke frames design careers as choices among compromise, resistance, departure, or reinvention. Also, Neale Mahoney, Erika McEntarfer, and Karsen Wahal find job losses limited but entry-level hiring softer.
Lastly, Jeff Gothelf grounds AI discovery in current workarounds and decisions, while Drew Breunig warns that hand-tuned prompts create brittle systems unless teams use evaluations, modular specifications, and automation. Dwarkesh Patel expects soaring compute demand to reward efficient models, as Tomasz Tunguz examines Microsoft’s flexible but OpenAI-dependent strategy. Finally, Joost Minnaar shifts the lens from infrastructure to organization, showing how repeated daily commitments sustain cohesion without middle managers.
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.
You will get a full set of PDFs, along with the guide to the A3 Delegation System, so that you can run the system with your own teams. Enjoy the video and let me know whether you consider the A3 Delegation System useful.
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. Zvi Mowshowitz shows the darker side of unchecked AI optimization, while Steve Newman questions its societal impact to date. Barry O’Reilly ties these concerns to leadership, urging redesign of workflows, judgment, decision rights, and accountability before scale amplifies weak systems. (Again, history rhymes; remember “Agile?”)
Next, Leah Tharin reframes activation as the full path from first touch to lasting habit, a view that challenges vanity metrics. Also, Michele Zanini and Gary Hamel question inflated AI claims, and Zanna Iscenko and Scott Strand add evidence of broad but shallow adoption. Chris Chinchilla’s Nokia history warns of what happens when execution lags behind change, and Johanna Rothman brings the remedy to focus: visualize work, expose delays, finish one thing, and reject the rest.
Lastly, Ant Murphy separates strategic leverage from strategy labels, while Steven Sinofsky argues that restricting AI model distillation would entrench incumbents, when more competition should be the goal. David Burkus turns to management, showing how leaders can shield teams from chaos without hiding uncertainty. Addy Osmani warns that AI-automated code creates comprehension debt, and John Cutler connects the themes: AI succeeds only when teams understand the work, retain human judgment, and trust leaders not to weaponize productivity gains.
