Many organizations can't answer a simple question: Is our AI working? Without clear metrics, you can't manage, improve, or justify AI investments.
Technical Metrics
Model Performance: Accuracy, precision, recall—depend on use case.
Latency: Response time matters for user experience.
Throughput: Can the system handle your volume?
Error Rate: How often does the system fail?
Business Metrics
Cost Impact: Cost reduction, revenue increase, or both?
Productivity: Time or effort saved.
Quality: Error reduction, consistency improvement.
Customer Satisfaction: How do users feel about AI interactions?
Adoption Metrics
Usage: Is the AI actually being used?
Engagement: Are users engaging deeply or bouncing quickly?
Feedback: What do users say about the AI?
Balanced Scorecard
Track metrics across dimensions:
- Technical health
- Business impact
- User experience
- Operational efficiency
Common Mistakes
Vanity metrics: Impressive but meaningless numbers.
Measuring too late: Build measurement into the design.
Ignoring qualitative feedback: Numbers don't tell the whole story.
Static targets: Expectations should evolve as systems improve.
Getting Started
Define success metrics before starting. Measure baseline before implementing. Track consistently over time.