AI adoption metrics are meaningless without a decision-quality metric
Counting agents, use cases or automated workflows does not prove that planning decisions improved.
AI adoption is increasingly measured by the number of use cases, agents and automated workflows. Those metrics say little about whether planning decisions became better.
The measurement gap
An organization can deploy many AI capabilities while planners continue to override recommendations, distrust the data or work around the system in Excel.
What should be measured
- forecast value added;
- decision latency;
- recommendation acceptance and override quality;
- service and inventory impact;
- financial outcome;
- explainability and traceability.
Decision architecture
Each AI-supported decision needs an authoritative data source, an owner, a threshold for human approval and a way to compare the recommended action with the actual outcome.
Practical recommendation
Keep experimentation lightweight while evidence is being gathered. Integrate mature capabilities into APS, ERP or BI only when the decision, owner, controls and outcome metrics are explicit.
AI activity is not the same as decision quality.