The instinct after launching an AI feature is to check the standard dashboard: DAU, session length, retention, revenue impact. These metrics matter. They are also insufficient — and sometimes actively misleading — for understanding AI product performance.
Why Standard Metrics Lie
A user who gets a bad AI output and learns to ignore it will show up in your retention numbers as an engaged user. A user who copies and pastes an AI suggestion without reading it looks identical to a user who carefully reviewed it and found it valuable. Standard engagement metrics cannot distinguish between these states.
The Metric Layers AI Products Need
Layer 1: Adoption funnel. What % of users who see the AI feature try it at least once? What % use it a second time? The gap between first and second use is the trust gap — and it is almost always larger than PMs expect.
Layer 2: Engagement quality. Not “did the user interact” but “how did the interaction end?” For a writing assistant: did the user keep the suggestion, edit it, or delete it? Each outcome tells you something different about output quality.
Layer 3: Override rate. What % of AI recommendations does the user override? An override rate of 5% suggests users trust the model. An override rate of 60% suggests users have learned the model is wrong often enough that they check everything.
Layer 4: Error impact. When the AI makes a mistake, what is the downstream cost to the user? Track error recovery time, support tickets generated by AI outputs, and cases where an AI error created user-visible problems.
Layer 5: Trust trajectory. Does user engagement with the AI feature increase or decrease over time? Increasing engagement is a signal of growing trust. Decreasing engagement despite low churn often means users have found workarounds.
The One Metric to Add This Week
Ask users to rate individual AI outputs. A single thumbs up/down is enough to start. The signal density you get from 1,000 rated outputs is worth more than any amount of aggregate engagement data.