AI in Agile: beyond the hype, what's actually changing for your team?

[#1]

The numbers are striking. Depending on which 2025 survey you read, between 84% and 91% of developers are now using AI tools in their workflows. Sprint planning overhead down 30–60%. Test case generation in seconds. Rovo reaching 3.5 million monthly users.

But honestly, the statistics are the easy part.

What I find far more interesting, and much less discussed, is what's happening to the people behind those numbers. Because AI isn't just changing what gets done; it's quietly reshaping who does what, how teams talk to each other, and what it means to be good at your job in an Agile team.

A few questions I'd love to hear your thoughts on:

• Roles: Is the boundary between Developer, QA, and Product Owner shifting in your team? Are some people thriving with AI while others feel displaced?
• Ceremonies: Has AI made your stand-ups, retrospectives, or planning sessions feel different — more efficient, more impersonal, or something else entirely?
• Trust: How does your team handle AI-generated outputs such as user stories, test cases, or estimations? Do people validate everything, or is trust building up?
• The unexpected: What has surprised you most, positively or negatively?

We're still early. The tooling is evolving fast, but the human and organizational side is lagging behind, and that's where I think the most valuable conversations are happening right now.

What's your experience been?

[Martin Runge]

AI is changing how our team and our customers work. We now ask developers to connect directly with customers whenever possible, so they can better understand what users need. This change has made roles less defined and has cut down on repetitive admin work for project managers and stakeholders. Although we are more efficient, it is now harder to keep a close eye on everything because things move so quickly.

To tackle these problems, we routinely review our code and processes, check AI results, and meet often to discuss project progress. This helps us carefully evaluate our work at every step.

Our following steps are to ensure teams maintain critical thinking as speed increases. We work on  AI-based efficiency with a "trust but verify" culture and human-in-the-loop processes. Managing the tension between automation, accountability, and leaving nobody behind is our focus as we advance AI adoption.