Measuring AI's Impact on Engineering Teams: The Metrics Already Exist

There's a question that keeps coming up in engineering leadership circles: how do we measure whether AI is actually helping? It's a fair question. Most of us are investing real time and money into AI tooling for our teams and at some point we need to understand what we're getting for it. The challenge is that the most visible metrics...PR counts, commits, code suggestions accepted...are measuring activity, not outcomes. We've been down this road before. There was a time when lines of code was the go-to measure of engineer productivity, and it was a terrible idea. People padded their stats and quality suffered. Same thing happened with test coverage. Once it became the goal instead of a signal, teams chased the number instead of what we actually wanted which was confidence in our code. The 2025 DORA Report puts some real numbers to this. AI helps developers complete 21% more tasks and merge 98% more pull requests...but organizational delivery metrics stayed flat. More activity, same out