Ilya Savin
Mobile Engineering Manager at Qonto
Berlin, Germany
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Engineering Manager with 10 years in Android, now focused on using AI to speed up delivery and cut the busywork. I lead with a customer-first mindset and love building clean, impactful mobile experiences that actually ship
Area of Expertise
Topics
Ship > Hype: Rolling Out AI at Scale to 60 Android Engineers
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?”
Your CTO says, “Everyone should use AI.” Now what? Integrating daily AI use in a large tech team
Synopsis
This talk shares our experience at Qonto integrating AI into a large engineering team of 120 developers. It covers the strategy we took, the experiments we ran, what actually worked (and what didn’t), and how we measured real impact across the org without slowing delivery.
Abstract:
“Start using AI” sounds simple. But getting engineers to actually integrate it into their daily work, especially in a fast-paced environment with constant change, is a whole different story.
At Qonto, I lead part of a 120-person engineering org. Over the past year, we’ve worked on making AI a useful, repeatable part of how we ship software without disrupting product delivery or adding overhead.
This talk is a look behind the curtain:
- The strategy that helped us stay flexible instead of locking into one tool or vendor
- The hands-on experiments we tried and what we learned, both the wins and the dead ends
- How we kept a balance between improving developer experience and hitting delivery goals
- How we tracked adoption, measured time saved, and identified where AI actually helped
- What we’re testing next as the AI landscape continues to shift
It’s a practical, real-world perspective on bringing AI into engineering. Not from a vendor, not from a research lab, but from the ground floor of a product team under pressure.
Takeaways:
- A strategy for introducing AI into a large, fast-moving engineering org
- Examples of real use cases that drove value, and ones that didn’t
- Tips for getting buy-in from delivery-focused teams
- Lessons from failed experiments and how we adjusted
- How to think about measuring success beyond just tool usage
Ship > Hype: Rolling Out AI at Scale to 60 Android Engineers
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?”
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