AI Products Measure the Right Things
Don't treat 'we used AI' as the effect metric. Watch things that actually move results: spec-to-plan conversion, first-pass CI rate, loop-closure time.
A lot of AI projects launch and stare at "how many people used AI" and "how high the engagement is." But those are activity metrics, not result metrics. What matters is whether it actually made things better.
After reading, you should be able to answer:
- Why can't "AI adoption rate" be the effect metric?
- In AI scenarios, which metrics truly drive action?
- What happens when you pick the wrong metric?
Why "used" isn't "effective"
- "Used AI" only says the action happened; it doesn't say the result got better
- High engagement could be fresh curiosity, or could be forced form-filling
- Making adoption the metric encourages "visible flurry" instead of real effect
Its most dangerous trait: it's too easy to game. A few pretty AI buttons and it goes up, while users may not be getting anything better vibe-report.
Metrics that truly drive action
AI products should pick metrics that measure results, not activity:
| Metric | What action it drives |
|---|---|
| Spec-to-plan conversion | is the requirement actually caught and moving forward |
| First-pass CI rate | does AI-generated code pass on the first try |
| Autonomous-fix loop-closure time | how quickly it fixes itself when broken |
| Task completion (not call volume) | did the user reach their goal |
Pick one primary metric that decides "pass or fail," watch the rest separately to locate weaknesses.
What picking wrong does
- You/your team push where it's wrong: optimizing "looks very AI" instead of "actually better"
- Fake optimization: changes made to game the metric sacrifice real value
- Metric drift: the goal changes, the metric doesn't follow copilot-cost
How to pick
- Ask first "what does the user need to achieve," then back into what to measure from there
- One primary metric decides; the rest are diagnostics
- Re-review it periodically by a human; don't let the metric be the sole authority
Read the right metrics, and after launch you know what to do next. Once the launch data is fine, make failure the next round's fuel: feed failures back into the next iteration.