AI Product Metrics
Not model metrics (accuracy, F1) — business metrics. The question isn’t “how good is the model?” but “is the AI feature making the product more valuable?”
Primary Metrics
| Metric | What It Measures | Target Range | How to Measure |
|---|---|---|---|
| Task completion rate | Did the AI solve the user’s problem end-to-end? | 60-80% (support), higher (code) | User feedback + automated detection |
| Deflection rate | Queries resolved without human escalation | 20-40% in first 90 days | Track handoff to human |
| Cost per conversation | Total LLM + infra cost per interaction | Track trend, not absolute | Observability pipeline |
| CSAT for AI | User satisfaction post-AI interaction | Compare to human baseline | Post-interaction survey |
| Adoption rate | % of eligible users engaging with AI | 30%+ within 60 days | Feature analytics |
| Time-to-value | Time from first interaction to outcome | Seconds/minutes, not hours | Event timing |
| Hallucination rate | Factual errors (user-reported + automated) | <5% for production | Feedback + eval pipeline |
| Retention impact | Cohort retention: AI users vs non-AI users | Positive delta | A/B cohort analysis |
How Leading Companies Measure
Intercom Fin (Customer Support AI)
- Resolution rate: Fully resolved without human handoff
- Handoff rate: When AI transfers to human (lower = better)
- CSAT per AI conversation: Compared against human agent CSAT
- Time to resolution: AI vs human baseline
GitHub Copilot (Code AI)
- Acceptance rate: % of suggestions accepted by developer
- Persistence rate: Lines of code still present after 30 seconds (not immediately deleted)
- Developer productivity surveys: Self-reported impact
Notion AI (Productivity AI)
- Feature adoption %: How many users try AI features
- Task completion speed improvement: Before/after AI
- Retention lift: AI users vs non-AI users
Setting AI Product OKRs
Structure: “Increase [quality metric] from X to Y, while maintaining [cost metric] below Z.”
Always pair quality with cost — optimizing one without the other leads to either expensive perfection or cheap garbage.
Examples:
- “Increase task completion rate from 45% to 65% while keeping cost per conversation under $0.15”
- “Achieve 30% deflection rate within 90 days with CSAT ≥ 4.0/5.0”
- “Reach 40% AI feature adoption with <5% hallucination rate”
Metrics by Product Stage
| Stage | Focus Metrics | Why |
|---|---|---|
| MVP | Adoption rate, task completion, qualitative feedback | Does anyone use it? Does it work? |
| Growth | Deflection rate, CSAT, cost per conversation | Is it providing business value? Is it sustainable? |
| Scale | Retention impact, revenue attribution, cost optimization | Is it a competitive advantage? |
Anti-Patterns
| Anti-Pattern | Why It’s Bad | Do This Instead |
|---|---|---|
| Only measuring accuracy/F1 | Model metrics ≠ product metrics | Measure task completion and user satisfaction |
| No cost tracking | AI costs can spike unpredictably | Track cost per interaction from day 1 |
| Comparing AI to perfection | No system is 100% — compare to human baseline | Benchmark against human agents / manual process |
| Measuring adoption without quality | High adoption + low quality = user frustration | Always pair adoption with satisfaction |