How DevEx and AI Shape Team Performance, with Laura Tacho, CTO at DX
In this episode, Laura Tacho joins Katerina to explore what really drives developer team performance. They dig into how to balance qualitative and quantitative metrics, why most companies misuse data, and how to start measuring DevEx even without a big budget.
They also unpack the reality behind AI-assisted development — how to track its true impact, set realistic expectations, and avoid falling for the hype.
If you care about building high-performing engineering teams, this conversation is a must.
What You’ll Learn
How DX helps teams boost performance through developer intelligence
Why self-reported data can surface quick, actionable insights
What makes a developer survey actually useful — and how to get teams to respond
How to use system data to save time and streamline engineering workflows
Why developers already know where the friction is — and how to tap into it
How starting simple with metrics early on can build better habits as you grow
The risks of using metrics to judge individual performance — and how to avoid them
Where AI tools really stand today — more autocomplete than autonomous
How to let data guide your decisions when introducing AI tools
Timestamps:
[0:00] Introduction to CTO Insights
[1:11] The Role of Data in Developer Experience
[4:19] Balancing Quantitative and Qualitative Data
[7:15] Common Pitfalls in Using Metrics
[8:33] Adapting Surveys to Specific Teams
[10:39] Common Developer Friction Points
[12:28] Starting Metrics in Early-Stage Startups
[15:29] AI's Impact on Developer Workflows
[18:10] Setting Realistic AI Expectations
[20:31] Final Advice for Engineering Leaders