You bought the AI tools. Now prove they're working.

Engineering leaders measure AI coding tool ROI by tracking AI-assisted pull requests as the primary metric, measuring how often AI contributed at the code generation, review, or agentic authorship stage. Then correlate that contribution to delivery outcomes like cycle time, defect rates, and planning accuracy. Seat-based adoption dashboards show who has a license. PR-level tracking shows whether AI is actually improving delivery.

An AI-assisted pull request is a PR where AI contributed at any stage of development, generating the code, reviewing the pull request, or autonomously authoring it as an AI agent. Unlike acceptance rate metrics, which capture a single interaction, AI-assisted PR tracking measures the full scope of AI's role in production workflows. Engineering leaders use AI-assisted PR rate as their north star metric for proving whether AI investments improve throughput.

GitHub Copilot's native analytics track suggestion acceptance rates and seat usage, but don't connect AI activity to downstream delivery outcomes. A team can maintain a high acceptance rate while cycle time stays flat, defect rates rise, and sprint predictability worsens. Proving AI ROI to a CEO or board requires correlating AI contribution to PR-level delivery metrics. This data lives at the Git level, not inside a single tool's dashboard.

Tracking AI productivity across tools like Copilot, Cursor, and others requires a tool-agnostic measurement layer that connects to your Git provider and AI tool APIs directly. This automatically tags every AI-assisted pull request regardless of which tool contributed, giving engineering leaders a unified view of adoption, acceptance rates, and quality impact by team and tool. No agents, proxies, or developer machine installs required.

Engineering leaders should be able to answer four questions: What percentage of our pull requests have AI contribution, by team and tool? Are AI-assisted PRs moving faster through our pipeline than human-only PRs? What is the quality impact of AI-generated code, measured by defect rates and rework? Which AI tools are delivering the most measurable improvement in delivery? If your current tooling can't answer these, it measures adoption rather than impact.