Monitoring & Performance involves continuously tracking how AI systems operate after deployment. This includes measuring accuracy and effectiveness (is the AI still performing as expected?), detecting model drift (has performance degraded as real-world conditions change?), identifying bias in outputs, tracking usage patterns and costs, monitoring system health, and reporting these metrics to leadership. Effective monitoring includes automated alerts when systems deviate from expected behaviour and processes for investigating and remediating issues.
AI systems are not “set and forget”—they can degrade over time as data distributions change, produce biased outcomes that weren’t apparent during testing, or consume unexpected resources. This dimension evaluates whether you track usage, detect bias or drift, and report performance to leadership.
Why It Matters
Unmonitored AI systems can degrade over time, produce biased outcomes, or incur unexpected costs.
Maturity Levels
| Basic | Standard | Advanced | Leading |
|---|---|---|---|
| No monitoring in place; AI systems operate without oversight. | Usage and cost tracking for AI systems. | Bias and model drift monitoring, with alerting and remediation processes. | Continuous benchmarking, performance reviews with leadership, and industry comparisons. |
📥 Related Resources & Templates
Downloadable templates, examples, and frameworks to help you implement this dimension.
Bias & Drift Monitoring Framework
PremiumFramework and templates for monitoring AI model bias and drift, including measurement methodologies and reporting.
AI Monitoring Dashboard
Dashboard templates and data structures for visualizing AI system performance, usage, and health metrics.
AI Usage Log
Template for logging AI system usage, requests, and outcomes for compliance and performance tracking.