Standard KPI Catalog for AI Deployments

This compact reference groups recommended KPIs into four buckets: Adoption, Impact, Model Quality, and Operational Risk. For each metric you’ll find a short definition, a formula or measurement note, typical frequency, suggested owner, and common data sources.

Adoption

• Active users (weekly) — Count of unique users interacting with the AI feature in the chosen interval. Frequency: weekly. Owner: product analyst. Data source: usage logs.
• Task automation rate — Percentage of eligible tasks completed by the AI vs. human intervention (automated completions / eligible tasks). Frequency: weekly. Owner: operations lead. Source: system logs/workflow events.
• Feature utilization — Share of workflow steps using AI where applicable. Frequency: weekly/monthly. Owner: product manager. Source: telemetry.

Impact (tie to business outcomes)

• Time saved per task — Average minutes saved when AI is used vs. baseline manual process. Frequency: weekly/monthly. Owner: business analyst. Source: time-motion data, sampling studies.
• Conversion or completion lift — Relative change in conversion/completion when AI is active (A/B or before/after). Frequency: per experiment or monthly. Owner: analytics. Source: experiment logs, business KPIs.
• Cost per transaction — Operational cost with AI enabled vs. baseline. Frequency: monthly/quarterly. Owner: finance/ops. Source: cost reports.

Model Quality

• Primary performance metric — The model’s evaluation metric (accuracy, F1, BLEU, NDCG). Frequency: daily/weekly (pipeline dependent). Owner: ML engineer. Source: validation and production scoring.
• Prediction latency — Median/95th percentile inference time. Frequency: daily. Owner: SRE/ML engineer. Source: telemetry.
• Drift indicator — Statistical measure comparing recent input distribution to baseline (e.g., population stability index). Frequency: daily/weekly. Owner: ML engineer. Source: feature telemetry.
• Hallucination or error rate (for generative systems) — Proportion of outputs with factual errors or policy violations identified by sampling or automated detectors. Frequency: weekly. Owner: quality analyst. Source: sampled reviews or automated checks.

Operational Risk & Safety

• Incidents reported — Count of user-facing incidents attributable to AI (incorrect outputs, privacy leaks, unsafe recommendations). Frequency: weekly. Owner: ops/safety. Source: incident tracker.
• Reversal rate — Percentage of AI actions that required human reversal or correction. Frequency: weekly. Owner: ops. Source: workflow logs.
• Escalation-triggered rollbacks — Number of times the system was paused/rolled back due to risk. Frequency: monthly. Owner: product/ops. Source: change management logs.

Suggested thresholds and notes

Thresholds must be context-specific. Use three colored bands for quick triage: green (within expected variance), amber (requires investigation this cycle), red (immediate action/escalation). Document the numerical bands for each KPI during kickoff and record them with the metric owner.

Mapping to owners and sources

Every KPI entry in your dashboard should record: metric name, numerical value, measurement frequency, primary data source, and a named owner. Without that, metrics become unverifiable and meetings will stall on data debates.