What Should You Actually Track in AI Usage
Before you can optimize AI adoption, you need to understand what you're actually measuring — and why.
At my work, we run two AI interfaces in parallel: Claude Code CLI for engineering work, and a desktop interface for the broader team. They generate different signals — so we monitor them separately. Today I want to share what I track, what the data showed me, and how I apply the learnings.
Claude Code CLI
The foundation is active engineers and active time — the baseline we rely on to understand whether the tool is really in use or just installed and idle. Below that, we monitor commits and PRs created with AI assistance as output and delivery signals, watching the trend over time rather than individual counts.
The efficiency metric we anchor on most is cost per commit. Our internal baseline is $5.13, derived from real billing data, and not telemetry...
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