How would you approach measuring the success of an AI-powered feature?
Model answer
Model answerAI features have two layers of success: did the AI do the right thing, and did the user get value? I'd track model quality metrics separately from product metrics. Model: accuracy, hallucination rate, latency. Product: task completion rate with vs without AI, time saved, user satisfaction. The most important metric: did users override or ignore the AI's output? High override rates mean the model is untrustworthy or users don't understand it.
Framework · Dual Metrics Model
- UUser value metrics
Task completion rate, time-to-completion, satisfaction — did the AI actually help? - BBusiness metrics
Cost per AI call, deflection rate, support ticket reduction - GGuardrails
Override rate and hallucination rate — if either is high, the feature is net negative - TTrust metrics
Long-term: are users relying on AI more or less over time? Increasing trust = success
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