The KPIs that keep your AI honest, fair, and reliable
Business KPIs measure outcomes. System KPIs measure the engine underneath. Is the model accurate? Is it fair? Does it stay up? Do people actually trust it? These 20 metrics answer those questions before a production failure answers them for you.
Is the model getting the right answers?
Performance metrics are the foundation. If accuracy is low, nothing else matters — your AI is making wrong decisions at scale. But accuracy alone isn't enough. You need to know if the model is stable over time (drift), resilient to adversarial inputs (robustness), and can recover quickly when things go wrong.
The question to ask yourself
If model accuracy dropped 5% overnight, how long would it take you to notice?
Overall correctness of model predictions
Target
>95%
Frequency
Trade-off between precision and false positives
Target
F1 Score >0.90
Frequency
Performance stability under adversarial inputs
Target
<5% degradation
Frequency
Identification of performance decay over time
Target
<2% monthly drift
Frequency
Time to restore performance after drift
Target
<24 hours
Frequency
Implementation playbook
How to operationalize these metrics — not just track them
Start with baselines, not targets
Run your AI system for 2–4 weeks before setting hard targets. Arbitrary thresholds create false alarms or false confidence.
Automate collection from day one
Manual metric collection doesn't scale and introduces lag. Instrument your pipeline to emit metrics as a side effect of every prediction.
Build dashboards with thresholds
A metric without a threshold is decoration. Set green/amber/red bands and wire alerts for amber. Don't wait for red.
Correlate across categories
Fairness problems often surface as user satisfaction drops. Performance drift shows up as incident spikes. Connect the dots.
Review and recalibrate quarterly
Business needs change, data distributions shift, user expectations evolve. A target set 6 months ago may no longer make sense.
Make metrics visible to stakeholders
System KPIs shouldn't live in an engineering wiki. Surface them alongside business KPIs so leadership understands the full picture.
The metrics you don't track will hurt you
Most teams track accuracy and call it done. Then a fairness audit reveals demographic bias. Or an operational blind spot means a model has been serving stale predictions for weeks. System KPIs aren't about perfection — they're about catching problems before your users, regulators, or the press catch them for you.
See system KPIs in action
The AI Ops Dashboard shows live metrics — MTTR, accuracy, adoption rates, and incident tracking — in the format these system KPIs are designed to feed.