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Learning foreign languages was never my skill. I still think back to high school, when my French teacher looked at me disparagingly during our oral exam. It came as no surprise that I failed, not because my vocab was not great, but because I was not able to really communicate with Madam McGregor. And from my observations when visiting product teams, we have a similar problem with AI adoption. Great vocab, but poor fluency.I chose the word fluency on purpose. AI Fluency works the same way it does with a language: you can know enough to order a beer and still be nowhere close to fluent. That's where most people using AI every day actually are. Open a chat window, type something short, take whatever comes back. If it sounds right, it probably is. I get the instinct. I've done it myself, on a Friday afternoon, when I just wanted the email gone and needed some data. It's also the most common failure mode I run into in product delivery right now. What AI Fluency Actually RequiresIt isn't about writing clever prompts, though a better prompt doesn't hurt. In practice, it comes down to four things:First, understanding what the tool actually gets wrong, and why a confident-sounding answer isn't the same as a correct one. Second, giving it the context it needs up front. The objective, the stage of the work, who it's for, and any other context that provides value. Third, treat the first response as a draft, never a decision. And fourth, own whatever you publish. If AI helped you write it, it's still yours to stand behind.Skip any one of those four and the rest won't save you. I've watched people write a genuinely careful, well-structured prompt and then forward the response to a stakeholder without a second read because a good prompt earns you a better draft, not permission to stop paying attention.The Truth About FluencyHere's the uncomfortable one. Anthropic ran a randomized controlled trial in 2026 and found that developers who used AI assistance on coding tasks got more done in the moment but did worse afterward on tests of what they'd actually learned. Same people. Same study. Output up, understanding down.That's not a reason to avoid AI. It's a reason to be deliberate about where you point it. On the work you already understand, AI should speed you up. On the work you're still learning to do, handing it over quietly takes the learning with it, and you often don't notice until the gap shows up somewhere it costs you.That's the paradox underneath all of this: AI fluency adds to your capability when you're deliberate about it, and it quietly subtracts from it when you're not. Most of the teams I talk to are living both sides of that at once, without having sorted out which is which yet.Introducing AI Fluency for Product Team MembersThis is why we built AI Fluency for Product Team Members™, a new self-paced course covering the foundational level of our AI Fluency Journey Map. It's built for everyone on the team, not just the people who'd call themselves technical, and it's grounded in real scenarios and case studies rather than abstract theory.OK - I am sure many of you are asking why Scrum.org. Surely AI companies are better placed to speak to AI fluency. Yes, the AI vendors know a lot about fluency, and their work has helped shape this class, but context matters. Fluency is context-dependent. I remember a friend of mine, who I consider fluent in German, being very scared to speak at a technical conference. When I pressed him, he said "technical / technology German is very different from normal German". To me, it all sounded the same, but context matters. The context for this class is agile product delivery and connects with teams using Scrum. By the end of the class, you'll be able to apply AI effectively, intentionally, and accountably in the context of the product delivery lifecycle and Scrum, spot the AI failure modes that tend to slip past people, write and iterate on prompts instead of settling for the first answer, evaluate what comes back with a genuinely skeptical eye, and take responsible ownership of AI use. You'll also come away with a clearer sense of when you've outgrown the basics, and it's time to go further.None of that makes you an AI expert. It makes you someone who can turn a generic, shallow AI output into something valuable, safe, and actually useful to your product delivery, which is, when you strip away the AI part, a fair description of good product work generally.AI Fluency for Product Team Members™ is now available on Scrum.org.Your team doesn't have an AI problem. It never did. The tools were never the hard part; fluency is, and now there's somewhere to build it.
| # | Наименование новости | Тональность | Информативность | Дата публикации |
|---|---|---|---|---|
| 1 | Does Your AI Know the Scrum Guide? Twelve Questions to Find Out | 0 | 9.71 | 21-09-2026 |
| 2 | AI on Top of a Dysfunctional System (1): The Product Backlog | 0 | 5.29 | 20-09-2026 |
| 3 | KI auf einem dysfunktionalen System (1): Das Produkt-Backlog 🇩🇪 | 0 | 8.42 | 24-09-2026 |
| 4 | AI Changed the Bottleneck. Your WIP Limits Should Change With It. | 0 | 10.22 | 08-09-2026 |
| 5 | Can Your Team Name the Work It Already Runs With AI? | 0 | 6.08 | 14-09-2026 |
| 6 | AI Tool Introduction: Promptly AI | 0 | 12.6 | 16-09-2026 |
| 7 | The New SDLC From Google Has a Harness. It Doesn't Have a Team. | 0 | 14.4 | 30-09-2026 |
| 8 | AI Transformations And Agile Transformations Rhyme | 0 | 7.34 | 06-09-2026 |
| 9 | 5 Things the Product Owner Shouldn’t Be Doing | 0 | 16.4 | 29-09-2026 |
| 10 | Your Scrum Team Might Be Excellent at Building the Wrong Thing | 0 | 13.28 | 27-09-2026 |