Glossary entry · Governance
Layer 3 of AI value management: whether AI is improving the workflow or creating new work around it, comparing usage with throughput, rework, cycle time, quality and the human effort needed to manage AI.
Business cases often claim this layer before the layers below can support it. It separates capacity actually released and put to use from activity growth, which is what a CFO needs before treating time saved as value.
Starting measures in the KPI library include AI Baseline Definition Rate, AI Adoption Rate, Capacity Redeployment Rate, Cost per Business Outcome and AI Benefit Realisation Accuracy. Owners are usually the CIO, COO and product leaders.
Productivity value is the third of the five layers of AI value management. Without adoption and operating change, productivity gains remain theoretical, and the layer depends on clean evidence from the two below it: a productivity claim measured before usage transparency cannot show whether it covers the full cost of the capability producing it.
Realised productivity gain and absorption capacity describe the difference between time released and value booked.
Put it to work
Browser tool. Twenty questions across the five layers of AI value management, scored against concrete behaviours, ending in a maturity level and the two layers to work on first.
Excel template. A starter scorecard that takes KPIs from the library and maps them to the five layers of AI value management, with baseline, target and owner for each.
Word template. A nine-part case for one AI use case: problem, baseline, value by layer, full cost, attribution plan, risk, exit criteria and the decision asked for.
Excel template. Track each benefit by type, owner and booking record, so only value that has reached an accepted record counts as realised.
