TL; DR: Mastering AI 4 Agile with the Best Self-Paced Online Course
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 Agile world is splitting into two camps: Those convinced AI will automate practitioners out of existence, and those dismissing it as another crypto-level fad. Both are wrong. The evidence reveals something far more interesting and urgent: Principles written in 2001, before anyone imagined GPT-Whatever, align remarkably well with the most transformative technology of recent years. This is not a coincidence. I believe it is proof that human-centric values transcend technological disruption; it is the Agile AI Manifesto.
And coming back to the two camps, here is what both miss: The biggest threat is not that AI replaces agile practitioners. It is AI that reveals what many organizations have suspected. They never needed Agile practitioners. They needed someone to manage Jira.
If your value proposition is running ceremonies, I deliberately do not refer to them as “events,” maintaining Product Backlogs, and generating burndown charts, AI reveals you were doing work the organization could have automated a decade ago. The separation is between practitioners who do real Agile work and those who perform Agile theater. AI is an expertise detector.
AI is tremendously helpful in the hands of a skilled operator. It can accelerate research, generate insights, and support better decision-making. But here’s what the AI evangelists won’t tell you: it can be equally damaging when fundamental AI risks are ignored.
The main risk is a gradual transfer of product strategy from business leaders to technical systems—often without anyone deciding this should happen. Teams add “AI” and often report more output, not more learning. That pattern is consistent with long-standing human-factors findings: under time pressure, people over-trust automated cues and under-practice independent verification, which proves especially dangerous when the automation is probabilistic rather than deterministic (Parasuraman & Riley, 1997; see all sources listed below). That’s not a model failure first; it’s a system and decision-making failure that AI accelerates.
The article is an extension to the lessons on “AI Risks” of the Agile 4 Agile Online course; see below. The research of sources was supported by Gemini 2.5 Pro.
Organizations seem to fail their AI transformation using the same patterns that killed their Agile transformations: Performing demos instead of solving problems, buying tools before identifying needs, celebrating pilots that can’t scale, and measuring activity instead of outcomes.
These aren’t technology failures; they are organizational patterns of performing change instead of actually changing. Your advantage isn’t AI expertise; it’s pattern recognition from surviving Agile. Use it to spot theater, demand real problems before tools, insist on integration from day one, and measure actual value delivered.
TL; DR: The Agile Community Needs Your Reality Check on AI
After analyzing dozens of “AI will transform agile” articles, I’ve found a troubling pattern: They’re written by AI enthusiasts who’ve never run a Sprint, not by practitioners dealing with the messy reality of AI integration.
The result? A dangerous gap between AI hype and Agile’s reality on the ground, leading to misguided implementations across our industry.
As someone who has documented Agile anti-patterns for years, I recognize this pattern. When we let others define our practices without practitioner input, we get cargo cult implementations that miss the essence of what makes Agile work.
👉 The AI for Agile Practitioners Survey is our opportunity to establish the definitive, practitioner-driven understanding of AI’s actual impact on our field—before the consultants and tool vendors do it for us.
AI FOMO comes from seeing everyone’s polished AI achievements while you see all your own experiments, failures, and confusion.
The constant drumbeat of AI breakthroughs triggers legitimate anxiety for Scrum Masters, Product Owners, Business Analysts, and Product Managers: “Am I falling behind? Will my role be diminished?”
But here’s the truth: You are not late. Most teams are still in their early stages and uneven. There are no “AI experts” in agile yet—only pioneers and experimenters treating AI as a drafting partner that accelerates exploration while they keep judgment, ethics, and accountability.
Disclaimer: I used a Deep Research report by Gemini 2.5 Pro to research sources for this article.
