Every AI product review I sit in starts the same way: someone pulls up a dashboard showing adoption rates, interaction volume, and session length. The numbers are up and to the right. Everyone nods.
Then I ask: “How many of those interactions ended with the user getting the right answer?” Silence.
This is the metrics gap that keeps burning teams. Usage tells you people showed up. It tells you nothing about whether they left with what they needed. An AI feature can be heavily used and actively harmful at the same time. Users try it, get a wrong answer, correct it manually, and keep coming back because they’re optimistic. Your dashboard shows engagement. Your product is eroding trust.
What to Actually Measure
Three things. That’s it.
Did the output help? Not “was it generated.” Did it contribute to the user completing their task? Define what successful completion looks like for your specific workflow, then measure whether AI-assisted completions happen more often, faster, or with fewer errors than the baseline. If you can’t tie AI output to a task outcome, you’re measuring wind.
Was it correct? Combine automated checks with periodic human review. Automated checks catch format violations, hallucinated entities, and safety issues. Human review catches the subtle stuff: answers that are technically correct but misleading, or correct for the wrong version. Sample 5% of outputs weekly. That’s enough to spot trends before they become incidents.
Do users trust it? Trust is the leading indicator everyone ignores. Track it through implicit signals: how often users edit AI output before accepting it, how often they abandon a flow after seeing the AI response, and how often they re-prompt with the same question phrased differently. Rising edit rates or re-prompt rates mean trust is declining. By the time CSAT surveys catch this, you’ve already lost months.
The Dashboard That Fits on One Screen
Your AI scorecard should answer four questions at a glance:
- Are people using it? (adoption, retention – the basics)
- Is the output good? (correctness rate, safety rate from automated + human review)
- Is it helping? (task completion rate, time to completion vs. baseline)
- Do they trust it? (edit rate, re-prompt rate, abandonment rate)
Review weekly. Tie every metric to a decision. If a number moves and nobody changes anything, delete the number. Dashboards without decisions are theater.
When a metric dips, you should be able to trace it back to a model update, a retrieval change, or a product shift within the same week. If you can’t, your instrumentation is too coarse.
The Uncomfortable Truth
Most teams avoid quality metrics because they’re harder to collect and the numbers are less flattering than engagement counts. That’s exactly why they matter. The teams that measure task success and trust alongside usage are the ones whose AI features survive past the demo phase.
Measure what the user felt. Everything else is vanity.