애자일 실무자가 2026년에 낙관적이어야 하는 이유(2부)

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. 🎓 🇬🇧 🤖 OUT NOW — The AI 4 Agile Online Course v2 at $149 The AI4Agile Online Course v2 teaches you to use AI in your daily agile work. Every lesson uses MegaBrain.io, a fictional startup where you apply AI to Sprint Retrospectives, stakeholder reports, product discovery, and backlog analysis. 72 video lessons. 13+ hours. 9 hands-on exercises. 155+ downloadable resources. 11 modules covering prompt engineering, Claude Skills, Claude Cowork, data analysis, and a framework for deciding where AI helps and where it does not. No coding required. You leave knowing which tools to use and where to draw the line. This course shows you how to do that precisely . The latest Version 2 is now available and comes with an introductory price of $149 instead of $249—don’t miss this opportunity to save $100! 👉 Join 500+ Peers and Save $100—But Only Until March 9: AI4Agile Online Course v2 at $149. 🇩🇪 Zur deutschsprachigen Version des Artikels: KI für agile Praktiker: Warum Sie für 2026 optimistisch sein sollten (Teil 2). 🗞 Shall I notify you about articles like this one? Awesome! You can sign up here for the ‘Food for Agile Thought’ newsletter and join 35,000-plus subscribers. The Diagnosis Was 2 Weeks Ago. This Week: The AI for Agile Practitioners Treatment In Part 1, I argued that agile practitioners are better positioned for the AI era than the doom narrative suggests, not because “people skills still matter” (the weakest defense in the industry), but because organizations adopting AI are failing for the same structural reasons they failed at Agile transformations: they cannot change how work gets done. This week, let us explore what you can do about it, not next year, but within the upcoming three months. Your Anxiety Is a Signal, Not a Verdict First, let me name what is happening to you psychologically, because pretending it is purely a skills question does not help. If you have built your career around agile practice and you are now watching AI automate tasks you used to own and organizations abandoning “Agile” for their home-made flavor of a product operating model, you are experiencing what Virginia Satir described in her change model: the chaos stage that follows when a foreign element disrupts the status quo. [2] Your old identity (“I am valuable because I know Scrum”) is cracking, and the new one has not formed yet. The problem is that most people try to escape the chaos by either denying the disruption (“AI is just hype”) or panic-buying credentials (“I need three more certifications”) — neither works. (And yes, an em dash is the right choice in this context.) What works is what you tell your teams every Sprint: inspect, adapt, run a small experiment, learn something, repeat. You already know the methodology for navigating your own career uncertainty. You just forgot to apply it to yourself. An Honest Assessment: What Transfers and What Does Not Not everything in your toolkit survives this shift. Let me be direct about both sides: What Transfers Directly Organizational change expertise: The top barrier to AI adoption is organizational, not technical. Harvard Business Review published research in November 2025 confirming that most firms struggle to capture value from AI because of “people, processes, and politics,” not because the technology fails. [1] Your years of navigating resistant organizations, sensing unwritten rules, and helping teams through uncomfortable transitions are the hard part. Data science teams will have a hard time doing so. Empirical process control: The difference between an organization that scales AI and one that stays in pilot purgatory is whether it can run honest experiments, inspect results without ego, and adapt based on evidence: what AI pilots are creating value, where should we double down? In my consulting practice, I have seen the same pattern repeatedly: the organizations that succeed with AI are those where someone insists on treating every application as a hypothesis, not a commitment; Agile’s core operating principle, applied to a new domain. Cross-functional translation: Product Managers (and Product Owners) who have spent years translating between business stakeholders and development teams hav