Ilya Savin

Synopsis: How we rolled out AI across a 60+ Android team at Qonto - strategy, hands-on experiments, what worked (and what didn’t), and how we made it part of our actual dev flow without slowing things down Abstract: The trend, the industry, or maybe even your CTO - all saying “Use AI now.” Cool. But how do you actually make that work across a large Android team without messing with the dev experience? At Qonto, I manage part of a 60-person Android org working in a large, modular codebase with tight delivery timelines. We knew AI had potential, but things only started to click once we made it part of our existing flow This talk is about how we introduced AI into our Android work in a way that was practical, measurable, and actually helpful. No vendor pitches, no sci-fi promises - just things we tried, what worked, what didn’t, and what we’re doing next. You’ll hear: - How we approached AI as a series of small experiments, not a top-down mandate - Android-specific use cases that showed real value (test generation, CI/CD, reviews, refactoring, screen generation in Compose, and more) - Tactics we used to get buy-in from engineers (and what they ignored) - Metrics we used to measure adoption and actual time savings - What we’d do differently if we started today Whether you’re just starting with AI or already deep in it, this talk shows how to make it work in a big Android team without slowing things down. Takeaways: - A rollout strategy for introducing AI to a large Android team without disruption - Real examples of where AI actually helped in Android workflows - and where it didn’t - How to drive adoption with engineers who are focused on delivery, not tooling - Lessons from experiments that failed, and why they failed - How to think about measurement beyond just “Did they use the tool?”

Everyone’s talking about AI. But how do you actually introduce it into a large mobile engineering team without derailing delivery, bloating workflows, or creating tool fatigue? This roundtable brings together Android and Flutter tech leads, managers, and senior developers to swap lessons on rolling out AI in real-world dev environments. Inspired by a case study from Qonto’s 60+ Android team, we’ll dive into what it takes to move past the hype and make AI adoption stick—across CI, test generation, reviews, UI scaffolding, and more. We’ll talk practical rollout strategies, developer resistance, failed experiments, and measurable wins. Whether you're early in your AI journey or managing adoption at scale, this is your chance to learn what’s working (and what isn’t) from peers doing the same. How are you currently introducing AI tools to your dev team—top-down, bottom-up, or somewhere in between? What mobile-specific use cases (e.g. test generation, Compose/UI code, linting, PR reviews) have actually shown value with AI? What friction have you encountered from engineers? How are you driving engagement without mandating adoption? How are you measuring success—developer happiness, time saved, code quality, or something else? What AI experiments failed for you—and what did you learn from them?