걱정을 멈추고 애자일 LLM을 사랑하는 법을 배웠습니다

TL;DR: The LLM in Agile Most agile practitioners are still debating whether AI matters. I stopped debating and started using it. Over two-plus years, AI went from proofreading my book manuscript to designing Retrospectives based on team data, to running an entire product development process for a new course, to working with autonomous AI agents. Each phase revealed what the previous one could not teach. Finally, I went Kubrick and started loving the LLM in Agile. The window of opportunity to build this competence is open now, but it will not remain open indefinitely. Start acting. 🎓 🇬🇧 🤖 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 400+ Peers and Save $100—But Only Until March 9: AI4Agile Online Course v2 at $149. 🇩🇪 Zur deutschsprachigen Version des Artikels: Wie ich lernte, das LLM in Agile zu lieben. 🗞 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. A Personal Journey to the LLM in Agile You have heard it all by now: AI will take your job, AI is overhyped, get another certification, or wait it out. The advice contradicts itself weekly, and none of it tells you what to do on Monday morning. Meanwhile, you have not opened an LLM this week for anything related to your actual work. Or you opened one, got a generic Sprint Retrospective agenda, and closed the tab. Neither reaction is wrong, yet both are incomplete. I want to share something different: what happened when I stopped debating AI and started using it. Not as a party trick or a thought experiment, but as a working tool, applied to real problems I face as an agile practitioner and trainer. The progression surprised me, and it will surprise you, too. Phase One: A Proofreading Buddy Who Talked Back My first serious use of AI had nothing to do with Scrum events or team facilitation. It was my book. I was in the middle of the editing process for the Scrum Anti-Patterns Guide, and anyone who has written a book knows the problem: after the tenth pass through your own manuscript, you stop seeing your own mistakes. Your brain auto-corrects what is on the page to match what you intended to write. Editors help, but the back-and-forth takes weeks per round. Then, ChatGPT 3.5 appeared. For the first time, I had a collaborator who could check my writing before I passed it to the human editor. Not a spell checker. A thinking partner who could flag where an argument was unclear, where a paragraph lost its thread, where a sentence tried to do too much. That was the moment AI stopped being a curiosity and became a tool I actively integrated into my work. Proofreading is not exciting. But having a collaborator who catches what your exhausted brain no longer sees changes how you approach a 400-page manuscript. Phase Two: Pattern Recognition I Could Not Do Alone The next step happened when I started experimenting with Retrospective design. I had years of facilitation experience and a mental library of formats and exercises. What I did not have was the ability to cross-reference team performance data, identify emerging patterns, and match those patterns against known formats at speed. AI is good at this, though not for the reason people assume. AI is not creative. It matches: it analyzes data, identifies a pattern, and connects it to something in its training data that fits. That is not original thinking. But it is exactly what a practitioner needs when deciding which Retrospective format will surface the conversation that the team needs most. The insight was not “AI can design Retrospectives.” The insight was that AI is an analytical partner for problems I already knew how to solve, but solved more slowly and with more blind spots on my own. Phase Three: The LLM in Agile as a Process Collaborator The real shift came when I was building the Advanced Product Backlog Management Course from scratch. For the first time, I used AI across an entire product development process, not just for a single task. I started by feeding it student feedback from in-person workshops, interviews, and surveys. Then I added the curriculum’s “product strategy and the roadmap.” I asked it to find patterns in the feedback: what themes kept appearin