AI-assisted development tools like GitHub Copilot are being adopted quickly. Many organizations have already rolled them out - or are seriously considering doing so. Adoption often looks promising: licenses are paid for, developers are using the tool, and usage metrics look encouraging.

And then the inevitable question comes up: how do we prove this actually helps?

This is not a new problem. Every major shift in how we build software - Agile, DevOps, CI/CD - has raised similar questions about productivity and effectiveness. AI-assisted development is simply the latest change in how work gets done, not a completely new measurement challenge.

In this talk, we’ll explore different ways to reason about the impact of GenAI assistants across the Software Development Life Cycle (SDLC), drawing on existing measurement approaches such as DORA and the SPACE framework, and examining how Copilot’s tool-level metrics can add context.

Rather than focusing on generated lines of code, we’ll show how Copilot metrics can be used to understand adoption and improve enablement - when interpreted correctly.

This session is for software architects, engineering leaders and senior developers who want to actively improve the adoption of AI-assisted development and apply a structured framework to measure its impact across the SDLC.