3.3 Metrics That Matter: OKRs for AI Products
What gets measured gets managed—and in AI, measuring the wrong things leads to products that perform well on benchmarks but fail in production, or optimize for accuracy while causing real-world harm. AI Innovations use a balanced scorecard approach that measures business value, model performance, and governance health together, preventing the single-metric myopia that plagues many AI initiatives.
Many AI teams measure only what's easy: model accuracy on test sets. But a model with 99% accuracy can still cause harm if it fails systematically for certain groups, or if that 1% error manifests in high-stakes situations. Effective AI measurement requires a comprehensive view across business, technical, and ethical dimensions.
The Three Metric Categories
AI Innovations track metrics in three categories, all of which must be healthy for the AI product to be considered successful:
Business Metrics
Do we deliver value? Measures whether the AI product achieves its intended business outcomes and justifies its investment.
Owner: STO
Model Metrics
Does it work well? Measures technical performance including accuracy, latency, reliability, and performance across subgroups.
Owner: ML Engineer lead
Governance Metrics
Is it responsible? Measures fairness, compliance, documentation currency, and risk management effectiveness.
Owner: AI Ethics Liaison
Business Metrics
Value Realization
Business metrics connect AI capabilities to organizational outcomes:
| Metric Type | Examples | Measurement Approach |
|---|---|---|
| Revenue Impact | Conversion lift, upsell rate, customer lifetime value | A/B testing, cohort analysis |
| Cost Reduction | Automation rate, processing time, error reduction | Before/after comparison, process metrics |
| Risk Reduction | Fraud prevented, compliance violations avoided | Incident tracking, audit results |
| User Adoption | Active users, feature usage, satisfaction scores | Product analytics, surveys |
| Decision Quality | Recommendation acceptance rate, override frequency | User behavior tracking |
ROI Calculation
Every AI product should have a clear ROI thesis that is tracked over time:
Value Delivered: Quantified business impact (revenue, cost savings, risk reduction)
Total Investment: Pod costs + infrastructure + data + opportunity cost
ROI: (Value - Investment) / Investment
Payback Period: Time to recover initial investment
Model Performance Metrics
Core Performance
Standard ML metrics appropriate to the model type:
| Model Type | Primary Metrics | Secondary Metrics |
|---|---|---|
| Classification | Precision, Recall, F1, AUC-ROC | Confusion matrix, PR curve |
| Regression | MAE, RMSE, R-squared | Residual analysis, prediction intervals |
| Ranking | NDCG, MAP, MRR | Position bias metrics |
| Generation (LLM) | Task-specific accuracy, human evaluation | Toxicity, factuality, coherence |
| Computer Vision | mAP, IoU, accuracy | Per-class performance, edge cases |
Operational Performance
Production-focused metrics that matter for real-world deployment:
Drift Detection
Metrics that detect when the model is degrading or the world is changing:
- Data Drift: Statistical distance between training and production distributions
- Concept Drift: Change in the relationship between inputs and outcomes
- Prediction Drift: Shifts in model output distribution
- Performance Drift: Degradation in accuracy metrics over time
Governance Metrics
Fairness Metrics
Metrics that measure equitable treatment across groups:
| Metric | Definition | When to Use |
|---|---|---|
| Demographic Parity | Equal positive prediction rates across groups | When equal outcomes are desired |
| Equalized Odds | Equal TPR and FPR across groups | When accuracy should be consistent |
| Calibration | Predicted probabilities match actual rates per group | When predictions inform decisions |
| Individual Fairness | Similar individuals receive similar predictions | When individual treatment matters |
Different fairness metrics can conflict with each other and with accuracy. There is no single "fair" metric—the right choice depends on context, stakeholders, and values. The Model Card should document which fairness metrics are prioritized and why.
Compliance Metrics
Metrics that track governance health:
- Documentation Currency: % of Model Card sections updated within policy timeframe
- Review Completion: % of required reviews completed on schedule
- Incident Response Time: Time from detection to resolution for governance issues
- Audit Readiness: Score from periodic audit readiness assessments
- Training Completion: % of pod members current on required training
Risk Metrics
Metrics that track risk exposure and mitigation:
- Open Risks: Count of identified risks without approved mitigations
- Risk Trend: Direction of aggregate risk score over time
- Mitigation Effectiveness: % of mitigations working as intended
- Near Misses: Incidents that could have caused harm but were caught
OKR Framework for AI Products
Objective and Key Results Structure
AI Innovations use OKRs to set quarterly goals that span all three metric categories:
Objective: Deliver trustworthy AI recommendations that drive customer value
Key Results:
- Business: Increase recommendation conversion rate from 3% to 5%
- Model: Maintain precision >90% while improving recall from 75% to 85%
- Governance: Reduce demographic parity gap from 8% to <3%
- Operational: Achieve 99.9% availability with P99 latency <200ms
OKR Design Principles for AI
Balance All Three Categories
Every OKR set should include key results from business, model, and governance categories. Succeeding on two while failing on one is not success.
Measure Outcomes, Not Activities
"Improve model accuracy" is better than "Train 5 new models." Focus on results, not effort.
Set Ambitious but Achievable Targets
OKRs should be stretch goals (aim for 70% achievement) but not impossible. Consistently missing OKRs demoralizes teams.
Include Leading and Lagging Indicators
Lagging indicators (revenue impact) matter most but move slowly. Leading indicators (user engagement) provide earlier signal.
Make Governance Non-Negotiable
Business success cannot justify governance failures. A product that hits business targets but fails fairness thresholds is not successful.
Quarterly Review Process
| Timing | Activity | Participants |
|---|---|---|
| Q-1 Week | STO drafts proposed OKRs | STO, with pod input |
| Q-Start Week 1 | Pod review and refinement | Full pod workshop |
| Q-Start Week 2 | AI Council review (for high-risk products) | STO presents |
| Mid-Quarter | Progress check and adjustment | Pod + stakeholders |
| Q-End Week | Results review and scoring | Pod + AI Council |
Metric Dashboard
Every AI Innovation should maintain a real-time dashboard showing:
- Current OKR Progress: Visual tracker for each key result
- Key Performance Metrics: Business, model, and governance health
- Alerts: Any metrics outside acceptable thresholds
- Trends: Direction of change for key metrics
- Incidents: Recent issues and their resolution status
The Metric Paradox
Metrics are essential for managing AI products, but they can also distort behavior. When teams optimize narrowly for measured targets, they may neglect unmeasured aspects that matter. The best defense is comprehensive measurement across business, model, and governance dimensions—combined with qualitative judgment about whether the metrics are telling the full story.