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. |
See This in Practice
🦺 AI Safety Monitoring
Phase 4 shows ongoing monitoring: tracking detection accuracy, measuring false positive rates, monitoring system costs, reviewing monthly performance reports with leadership, and detecting model drift as site conditions change.
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Energy⚡ Grid Optimization
Demonstrates continuous performance tracking: forecasting accuracy monitoring, real-time cost optimization metrics, grid constraint violation alerts, seasonal drift detection, and automated performance reporting to operations.
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Construction📅 Schedule Optimization
Shows business outcome monitoring: tracking project delivery improvements, measuring time savings, monitoring user adoption rates, and quarterly performance reviews demonstrating 2 additional projects per year delivered.
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📥 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.