The metrics that prove AI is working
Most AI projects fail not because the technology doesn't work — but because no one defined what “working” looks like. This framework connects AI implementation to the 7 business outcomes your stakeholders actually care about.
“Without clear metrics, AI projects become expensive experiments with no feedback loop.”
Companies that establish KPIs before deployment achieve 3x higher ROI — because they know what to optimize, when to pivot, and how to communicate value to the board.
7
Business Outcomes
Revenue to Innovation
35
Measurable KPIs
Quantified targets
3x
Higher ROI
With clear KPIs
20
AI System Metrics
Fairness & performance
How AIOps delivers value
Observe. Engage. Act. Continuously Optimize.
Observe
Ingest data from logs, metrics, traces, and business systems. Correlate signals. See the full picture.
Engage
Surface AI/ML insights and recommendations. Connect patterns to root causes with contextual intelligence.
Act
Automate remediation with runbooks and orchestration. Close the loop from detection to resolution.
Optimize
Learn from every outcome. Refine models, reduce noise, and continuously improve accuracy and speed.
Mapped to what matters
7 Business Outcomes, 35 KPIs
Each category answers a specific question your leadership is already asking. Click any category to see the full dashboard with charts and definitions.
Ready to measure what matters?
Start with the KPIs most relevant to your AI initiative. You don't need all 35 on day one — pick the 5 that answer your board's top questions.
Browse all 35 KPIs