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Food for Agile Thought #536: POM Starter Pack, Product Leadership Guide, What People Want from AI, Claude Skills Playbook

by Stefan Wolpers | 2026-03-20 News

TL; DR: POM Starter Pack — Food for Agile Thought #536

Welcome to the 536th edition of the Food for Agile Thought newsletter, shared with 35,661 peers. This week, Anthropic’s 81,000-person study reveals that hope and alarm about AI coexist within the same individual. Alberto Romero channels that tension into eight practical strategies for AI career anxiety, while Allan Kelly warns that today’s AI hype mirrors the 1990s BPR failures. On the product side, Teresa Torres walks teams through measuring real customer impact rather than shipping features, Janna Bastow proposes that fixing bugs and technical debt is the strategy, and the Dotwork team provides a POM starter pack to operationalize Marty Cagan’s Product Operating Model.

Next, David Pereira suggests that product leadership means creating space for product managers to thrive, not being the smartest person in the room. Steve Blank warns that startups older than 2 years are likely running obsolete playbooks in a world reshaped by AI agents and vibe coding. Also, Ruben Dominguez highlights Claude’s 14x revenue jump and proposes that the real productivity gap lies in learning to co-work with AI. Cedric Chin recommends ignoring AI predictions and studying actual field reports instead, while Dave Snowden reminds us that Boyd’s OODA loop was never meant to be a safe iteration cycle.

Then, Jeff Gothelf proposes that storytelling is now the product manager’s key competitive advantage as AI commoditizes standard PM artifacts. Tristan Kromer addresses the lack of memory in AI agents. He proposes building a RAG-based experiment knowledge base to compound learning rather than repeat it. Martin Eriksson adds that AI agents need the same strategic clarity as human teams or organizations will scale confusion at machine speed. Finally, Sharyph explains how Claude Code Skills 2.0 turns Claude into a personalized, testable workflow system, while Deloitte’s 2026 State of AI report finds that only 34% of organizations truly reimagine their business with AI despite rising access.

Food for Agile Thought #536: POM Starter Pack, Product Leadership Guide, AI: What People Want, Claude Skills Playbook - Age-of-Product.com

Three AI Skills to Sharpen Judgment

by Stefan Wolpers | 2026-03-15 Agile 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.

Three Thinking AI Skills to Sharpen Judgment: Socratic Explorer, Brutal Critic, Pre-Mortem — Age-of-Product.com

Download the Claude Skills Pack

by Stefan Wolpers | Downloads

Welcome to the Download Page of the ‘Claude Skills Pack’

Most agile practitioners use AI to produce outputs more quickly. Few use it to think better. This free download gives you four Claude Skills (Socratic Explorer, Brutal Critic, Pre-Mortem, and Commander’s Intent) that turn AI into a structured thinking partner for diagnosing problems, stress-testing plans, and anticipating failures before they happen.

Download your Claude Skills Pack now:

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Why Agile Practitioners Should Be Optimistic for 2026 (Part 1): You Have Already Survived This

Why Agile Practitioners Should Be Optimistic for 2026 (Part 2): AI for Agile Practitioners

Assist, Automate, Avoid: How Agile Practitioners Stay Irreplaceable with the A3 Framework

Agile Is Dead, Long Live Agility

The Reformation That Became the Church

The Immunity Response: How Organizations Neutralize Change

AI Transformation Déjà Vu: Why Today’s Failures Look Uncannily Like Yesterday’s “Agile Transformations”

The Scrum Anti-Patterns Guide — Up Your Agile Game!

Hands-on Agile: Stefan Wolpers: The Scrum Anti-Patterns Guide: Challenges Every Scrum Team Faces and How to Overcome Them

👆 Stefan Wolpers: The Scrum Anti-Patterns Guide (Amazon advertisement.)

Food for Agile Thought #535: AI’s Labor Market Impact, Killing Your Darlings, Discovery Failures, Learned Helplessness

by Stefan Wolpers | 2026-03-13 News

TL; DR: AI’s Labor Market Impact — Food for Agile Thought #535

Welcome to the 535th edition of the Food for Agile Thought newsletter, shared with 35,669 peers. This week, Ethan Mollick explores AI’s shift from co-intelligence to managing autonomous agents, urging organizations to experiment now. Jing Hu counters the “AI is bigger than Covid” panic by exposing the gap between theoretical and actual AI adoption, while Massenkoff and McCrory back this up with data on AI’s labor market impact showing no systematic rise in unemployment yet. Teresa Torres and Petra Wille warn that mediocre product success traps teams, Johanna Rothman offers team-based approaches to shaping unclear backlogs, and Joost Minnaar shows why removing hierarchy fails without investing in human capability.

Next, Aatir Abdul Rauf identifies seven headwinds AI product teams face after shipping, from margin erosion to trust gaps. At the same time, Sasha Rogelberg reports on BCG’s “AI brain fry” study, which shows that piling on AI tools hurts productivity and drives turnover. On a practical note, Ruben Hassid walks you through setting up Claude as your primary AI tool. Itamar Gilad traces product discovery failures to “must-have” features that bypass validation and to weak goals, and Tim Harford warns that quantified metrics quietly strip away context, autonomy, and genuine judgment.

Then, Olivia Moore tracks the intensifying race for the “default AI” in her sixth edition of the top 100 gen AI consumer apps. Jeff Gothelf proposes that customer relationships, not features, are the last defensible advantage, and Chris Walker identifies “context engineering” as a durable bottleneck preserving a role for local domain expertise. Justin Jackson examines how AI coding tools blur the roles of engineers, PMs, and designers, and suggests pair programming as a remedy. Lastly, David Burkus wraps things up with practical advice on leading difficult conversations with curiosity rather than accusation.

Food for Agile Thought #535: AI's Labor Market Impact, Killing Your Darlings, Discovery Failures, Learned Helplessness - Age-of-Product.com

Why Agile Practitioners Should Be Optimistic for 2026 (Part 2): AI for Agile Practitioners

by Stefan Wolpers | 2026-03-08 Agile and ScrumAgile Transition

TL; DR: What to Do About It

Your anxiety about AI is a signal, not a verdict. Here is why AI for Agile Practitioners matters and how:

What transfers: Organizational change expertise, empirical process control, and cross-functional translation. The hard parts of AI adoption are the parts you have been practicing for years.
What does not: Framework expertise as a standalone value proposition, process facilitation without outcome ownership, and tool-agnosticism as a point of pride.
What to do this week: Run one small experiment that integrates AI into your actual work. Before you prompt, categorize the task: Assist, Automate, or Avoid.

What would remain of your professional value if you removed every framework name and certification from your resume? Whatever that is: Invest there.

Why Agile Practitioners Should Be Optimistic for 2026 (Part 2): AI for Agile Practitioners - Age-of-Product.com

Food for Agile Thought #534: Stakeholder Management, Empowerment, The Last Analog Generation, Onboarding AI Agents

by Stefan Wolpers | 2026-03-06 News

TL; DR: Stakeholder Management — Food for Agile Thought #534

Welcome to the 534th edition of the Food for Agile Thought newsletter, shared with 35,693 peers. This week, Venkatesh Rao explores how AI coding clears intention debt and frees people to take on new creative work. Janna Bastow shares stakeholder management practices, and Teresa Torres pushes product teams to tie decisions to evidence, outcomes, and visible discovery. Grant Harvey reports GPT 5.4’s leap in coding and knowledge work, while Cornelia C. Walther urges human-centered AI leadership. Also, Michael Lopp names the workplace behaviors that quietly drain leaders’ attention.

Next, Chad McAllister shares Mike Hyzy’s view of Taylor Swift as a model for product strategy, and Martin Eriksson reframes empowerment as a spectrum of decision ownership. Steve Newman examines how AI agents shift work toward goals and feedback; Tom Wojcik warns that AI coding can weaken engineering judgment, and Paweł Huryn maps product frameworks into AI workflows. Moreover, Maarten Dalmijn uses Force Mapping to help teams tackle root causes instead of symptoms.

Then, Shreyas Doshi argues that as AI tools become commodities, product sense will separate strong product leaders from the rest. Peter Yang shows how AI-native companies treat agents as teammates, and Andi Roberts reminds leaders that systems shape behavior more than slogans do. Also, Mike Cohn challenges the old cost of change curve. Finally, Yuri Vonchitzki warns that poor data, not AI, is often the driver of disappointing results.