AI can silently erode your product operating model by replacing empirical validation with pattern-matching shortcuts and algorithmic decision-making. This article on product development AI risks, along with its corresponding video, identifies three consolidated risk categories and practical boundaries to maintain customer-centric judgment while leveraging AI effectively.
Peter Yang, a renowned product leader, argues that AI will split product roles into two groups: Generalists who can prototype end-to-end with AI, and specialists in the top 5% of their fields. Everyone else in the dangerous middle risks being squeezed.
How does this apply to agile practitioners: Scrum Masters, Product Owners, Agile Coaches, and transformation leads? It does, with important nuances.
Help me QA the questions for the AI 4 Agile Certificate by taking three short practice quizzes on the intersection of AI and Agile. In return, pass 2 out of 3 and get a free shot at the real AI 4 Agile Certification (40 questions, 45 minutes). The top performer of this small competition also receives the full AI 4 Agile online course.
Your feedback on the questions matters tremendously and helps shape a better curriculum for all practitioners.
The Mastering AI with the AI 4 Agile Online Course launches this week, and I am proud that I avoided another delay. Scope creep happened despite my supposed expertise in preventing exactly that. The course expanded from a simple prompt collection to over 8 hours of video, custom GPTs, and materials that I’ll apparently continue to update indefinitely, as I’m still not satisfied that it’s comprehensive enough. (Also, the field is advancing so rapidly.)
At least the $129 lifetime access means you will benefit from my urge to fight my imposter syndrome with perfectionism and from my inability to call a project “done.” I guess we are in for the long term. 🙂
🎓 🛒 The AI 4 Agile Online Course Is Available — Join Now at $ 129 until October 20, 2025: AI 4 Agile — Master AI Integration for Agile Practitioners.
“Good Enough Agile” is ending as AI automates mere ceremonial tasks and Product Operating Models demand outcome-focused teams. Agile professionals must evolve from process facilitators to strategic product thinkers or risk obsolescence as organizations adopt AI-native approaches that embody Agile values without ritual overhead.
Recent research shows AI isn’t just another tool—it’s a “cybernetic teammate” that enhances agile work. A Harvard Business School study of 776 professionals found individuals using AI matched the performance of human teams, broke down expertise silos, and experienced more positive emotions during work. For agile practitioners, the choice isn’t between humans or AI but between being AI-augmented or falling behind those who are. The cost of experimentation is low; the potential career advantage, on the other hand, is substantial. A reason to embrace generative AI in Agile?
Agile teams have always sought ways to work smarter without compromising their principles. Many have begun experimenting with new technologies, frameworks, or practices to enhance their way of working. Still, they often struggle to get relevant, actionable results that address their specific challenges. Regarding generative AI, there is a better way for agile practitioners than reinventing the wheel team by team—the Agile Prompt Engineering Framework.
Learn why it solves the challenge: a structured approach to prompting AI models designed specifically for agile practitioners who want to leverage this technology as a powerful ally in their journey.
ChatGPT can be an excellent tool for those who know how to create prompts. The simplest form of prompting ChatGPT is to feed it the task and ask for results. However, this approach is unlikely to trigger the best response from the model.
Instead, invest more time in prompt engineering, and provide ChatGPT with a better context of the situation, desired outcomes, data, constraints, etc. The following article offers a primer to creating ChatGPT prompts for Scrum practitioners to get you started running. You will learn:
In this comprehensive Scrum Master Interview guide, we delve into 83 critical questions that can help distinguish genuine Scrum Masters from pretenders during interviews. We designed this selection to evaluate the candidates’ theoretical knowledge, practical experience, and ability to apply general Scrum and “Agile “principles effectively in real-world scenarios—as outlined in the Scrum Guide or the Agile Manifesto. Ideal for hiring managers, HR professionals, and future Scrum teammates, this guide provides a toolkit to ensure that your next Scrum Master hire is truly qualified, enhancing your team’s agility and productivity.
If you are a Scrum Master currently looking for a new position, please check out the “Preparing for Your Scrum Master Interview as a Candidate” section below.
So far, this Scrum Master interview guide has been downloaded more than 25,000 times.
If you are looking to fill a position for a Product Owner in your organization, you may find the following 82 interview questions useful to identify the right candidate. They are derived from my sixteen years of practical experience with XP and Scrum, serving both as Product Owner and Scrum Master and interviewing dozens of Product Owner candidates on behalf of my clients.
So far, this Product Owner interview guide has been downloaded more than 10,000 times.
Age-of-Product.com’s parent company — Berlin Product People GmbH — offers Scrum training classes authorized by Scrum.org, Liberating Structures workshops, and hybrid training of Professional Scrum and Liberating Structures. The training classes are offered both in English and German.
Check out the upcoming timetable of training classes, workshops, meetups, and other events below and join your peers.
Welcome to the 517th edition of the Food for Agile Thought newsletter, shared with 40,352 peers. This week, Grant Harvey dissects the AI boom-or-bust situation, warning of inflated valuations built on shaky economics. Petra Wille urges teams to switch deliberately between product and project thinking, guided by feedback loops, and John Cutler skewers empty calls for simplification that mask vague agendas and stalled change. Len Greski declares the “Agile” brand broken but defends its principles, while David Pereira’s chat with David J. Bland highlights lessons learned and why systems thinking now takes center stage.
Next, Maarten Dalmijn highlights how delaying decisions can preserve options and reduce regret. Raghav Sethi critiques AI bloat in products, eroding trust. Richard Mironov warns of inflated AI valuations and urges sharper judgment. At the same time, Eli Pariser reports from a private AI summit where hype meets unease. Also, Johanna Rothman reminds us that truth-telling requires cultural permission and consistent leadership.
Lastly, Jeremy Korst, Stefano Puntoni, and Sonny Tambe show that generative AI delivers ROI at scale, though skills still lag. Teresa Torres explains how Claude Code lets non-technical users build reusable AI workflows, and Maik Seyfert exposes the illusion of team autonomy rooted in structural control. Also, Mark Levison targets bloated backlogs with story maps. Finally, Nadzeya Stalbouskaya urges leaders to treat architectural debt as a strategic risk rather than hide it under the label of technical debt.
Version 1 of the AI for Agile Practitioners Online Course just closed its first two weeks with 250 new students. The feedback has been direct and useful: Practitioners value the realistic MegaBrain.io scenario work, the quiz design that tests judgment rather than memorization, and the focus on ethics and responsible AI alongside practical application. So far, that’s good, but AI 4 Agile v2 seriously needs your input: What topics shall I cover?
👉 Hence, please join the AI 4 Agile v2 Survey; it won’t take more than 3 minutes.
Welcome to the 516th edition of the Food for Agile Thought newsletter, shared with 40,359 peers. This week, Jenn Spykerman addresses unknowingly sabotaging AI according to a 1944 manual, masquerading endless committees, perfectionism, and cautious delays as good governance. Leah Tharin challenges claims that LLMs can stand in for real customer insight, warning that dashboards can fuel strategy theater, while John Cutler connects prioritization to strategic leverage and power dynamics. Jing Hu reframes AI failure stats as early-stage noise, and Duncan Brown warns that AI favors the visible over the messy, human glue that makes effective teams work.
Next, Chetan Kapoor shows how eBay uses feature flags as discovery tools to validate demand early and surface usability issues. Kyle Poyar critiques popular SaaS pricing models and shares fixes that avoid complete overhauls. Ethan Mollick maps the current AI landscape with practical guidance on tools, tiers, and tactics. At the same time, Zvi Mowshowitz highlights key takeaways from Karpathy’s AGI views, and James Shore reframes engineering accountability through product bets instead of features and deadlines.
Lastly, Anthropic’s Claude receives “skills” as modular guides for specialized tasks, now open-sourced on GitHub. One author explores how great teams grow through targeted support and bold delegation, and Jeff Sauro and Jim Lewis dissect NPS claims, separating useful signals from misleading noise. Charlie Guo calls out the creeping signs of AI-generated content and its cost to authenticity. Finally, Karen Dahut presents Google Skills, a vast new learning platform for AI and tech upskilling.
Welcome to the 515th edition of the Food for Agile Thought newsletter, shared with 40,381 peers. This week, Henrik Mårtensson explores six decision-making traps managers fall into and how to overcome poor decisions with candor and deliberate practice. Janna Bastow highlights how skipping feasibility checks sabotages product delivery, offering lightweight tactics for trust and clarity in the AI era. Also, Teresa Torres and Petra Wille explore how product leaders shape their legacy through their impact, values, and reflection. Meanwhile, Jacob Poushter and team find AI anxiety outweighs optimism, and Maarten Dalmijn warns how process bloat kills team ownership.
Next, Melissa Suzuno outlines how product operating models shift focus from outputs to outcomes, scaling through pilot teams and leadership support. Roger Snyder addresses the tension between PM and PO, emphasizing the importance of alignment on purpose, ownership, and cadence, while Charlie Guo examines LLM performance drift, providing mitigation strategies. Barry O’Reilly lists 21 signs your AI project might be undead, and Martin Eriksson warns that empowerment fails without a strategic context and a deliberate shift in leadership stance.
Lastly, Pawel Brodzinski warns that autonomous AI agents lack the trust needed for broad adoption without transparency and alignment. Jens Meyer critiques veto-heavy cultures and calls for genuine accountability, where saying yes means accepting the outcome. Also, Emily Webber shares tips on selecting meaningful icebreakers that promote safety and connection, and Steve Blank defends science as the engine of innovation. Finally, Matt Kamelman stresses that smart AI starts with context, not just more data.
