Stop treating AI as a team member to “onboard.” Instead, give it just enough context for specific tasks, connect it to your existing artifacts, and create clear boundaries through team agreements. This lightweight, modular approach of contextual AI integration delivers immediate value without unrealistic expectations, letting AI enhance your team’s capabilities without pretending it’s human.
When you step into a new role as Scrum Master or agile coach for a team under pressure, you’re immediately confronted with a challenging reality: you need to understand the complex dynamics at play, but have limited time to process all the available information. This article explores how AI interview analysis can be a powerful sensemaking tool for agile practitioners who need to synthesize unstructured qualitative data quickly, particularly when joining a team mid-crisis.
In this experiment, OpenAI’s new stealthy LLM Optimus Alpha demonstrated exceptional performance in team data analysis, quickly identifying key patterns in complex agile metrics and synthesizing insights about technical debt, value creation, and team dynamics. The model provided a tailored Retrospective format based on real team data.
Its ability to analyze performance metrics and translate them into solid, actionable Retrospective designs represents a significant advancement for agile practitioners.
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.
As AI tools become increasingly integrated into our work, agile practitioners face the million-dollar question:
Will AI make my career obsolete? 🤔
I believe the answer is a resounding no—if we choose to understand and adapt to AI’s real impact on our field today and in the coming months.
👉 So, please invest 10 minutes and join the AI for Agile Practitioners Survey now!
Are you considering a new Scrum Master or Agile Coach job? However, you are not sure that it is the right organization? Don’t worry; there are four steps of proactive research to identify suitable employers or clients for getting hired as a Scrum Master and avoid disappointment later.
I have used those four steps for years to identify organizations I would like to work with, and they never failed me. Read on and learn how to employ search engines, LinkedIn’s people search, reach out to peers in the agile community, and analyze the event markets in the quest for your next Scrum Master job.
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 490th edition of the Food for Agile Thought newsletter, shared with 42,688 peers. This week, Tobias Mayer reintroduces Scrum as a minimalist, team-empowering practice rooted in empiricism with a simplified Scrum Guide, and David Pereira challenges the feature factory’s enduring grip with seven sharp tactics for reclaiming product purpose. Aakash Gupta and Paweł Huryn offer an AI PM crash course, while Simon Willison finds AI search finally usable. Also, Dwarkesh Patel urges humility over certainty when navigating uncertain AGI trajectories.
Next, Roni Ben Aharon reflects on the power of sunsetting features to sharpen product focus, and Jenny Wanger champions AI for productivity without losing human connection. Ethan Mollick introduces “Jagged AGI,” while Zvi Mowshowitz warns of its risks, testing o3. Moreover, John Cutler reframes team conversations as core infrastructure shaping alignment, performance, and adaptive capacity.
Lastly, Philippe Bonneton and Mike Cottmeyer explain why AI stalls beyond the pilot phase, while Fredrik Delin calls for funding persistent teams over projects. Itamar Gilad critiques outdated prioritization, Scott Sehlhorst warns of diminishing returns, and Johanna Rothman shows how frequent, collaborative deliveries replace blame with trust. Together, they spotlight the structural shifts needed for real agility and sustainable impact.
Welcome to the 489th edition of the Food for Agile Thought newsletter, shared with 42,673 peers. In this week’s edition, John Cutler dissects the fallacy of linear product development models, advocating for messy, fractal approaches that mirror real-world complexity. Marty Cagan sees generative AI as a lever for genuine team autonomy beyond legacy constraints. Dan Shipper champions OpenAI’s o3 for complex, agentic workflows, while Gary Marcus rebuts AGI hype Tyler Cowen advocates. Moreover, Maarten Dalmijn warns against Big Bang rewrite failures, urging incremental progress, better morale, and reduced risk through visible, sustainable change.
Next, John Cutler clarifies the four distinct jobs of prioritization, warning against their conflation, and Hamel Husain shares AI product lessons centered on iteration and domain expertise. Ant Murphy offers a path out of OKR theatre through focused, product-driven goals. Additionally, Tina Huang reveals how AI and fundamentals combine for intuitive, context-aware app development.
Lastly, Aakash Gupta and Anthony Maggio share how they built a no-code AI PM agent in under 90 minutes. David Burkus explains how trust is rebuilt through consistent leadership action. Also, Gregor Ojstersek urges culture-first AI adoption in engineering, avoiding its imposition. Finally, Kevin O’Sullivan demystifies product analytics, while Paweł Huryn shares a no-code guide to building voice agents with n8n and MCP servers.
Welcome to the 488th edition of the Food for Agile Thought newsletter, shared with 42,671 peers. This week, Tobi Lütke calls for reflexive AI usage as a baseline at Shopify, positioning it as a creative and productivity multiplier. Jeremy Brown offers human-centered principles for product–engineering ownership that avoid RACI wars. Aakash Gupta shows how OpenAI and Notion treat experimentation as strategic infrastructure, and Roman Pichler explores AI’s role in product strategy, emphasizing its benefits while reinforcing the irreplaceable role of human judgment. Meanwhile, the 2025 AI Index Report reveals explosive AI progress and investment and flags persistent global gaps in regulation, education, and reasoning performance.
Next, John Cutler unpacks how flawed models and stale dashboards hinder creating shared understanding at scale. Mark Graban explains why psychological safety is key to Lean success, not a nice-to-have. Mike Cottmeyer and Eric Flecher connect AI readiness to the same structural shifts required for real agility. Also, Pim de Morree makes a strong case for replacing hierarchy with well-supported self-management.
Lastly, Wes Kao shares actionable tactics with Lenny Rachitsky for clearer, more persuasive communication—especially when managing up or handling objections. Abby Covert reminds us that stakeholder misalignment, not poor structure, derails excellent information architecture. Hyunsun Park and Subra Tangirala reveal why ambiguity silences employees; they call for cultures where sensing risk isn’t just leadership’s job.
