Glossary entry · Governance
Layer 1 of AI value management: knowing what the organisation spends on AI, where demand is appearing, and who is consuming AI capability.
Every later layer rests on it. Until AI cost can be normalised across SaaS, APIs, cloud platforms, private infrastructure and shared services, nobody can allocate, forecast, charge back, cap or investigate it, or separate pilots from run-rate demand.
Starting measures in the KPI library include AI Spend Allocation Rate, Shadow AI Spend Ratio, AI TCO Completeness Score and Unallocated Shared AI Capacity Cost. Owners are usually FinOps, ITFM and procurement.
Usage transparency is the first of the five layers of AI value management. The layers are cumulative: a leadership team cannot make credible portfolio choices if it cannot see usage.
It is close to what FinOps for AI delivers, and FinOps is usually the primary owner. The layer asks a little more than a billing view: spend in seats and embedded SaaS tiers counts, and so does spend that sits outside formal IT budgets.
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. An outline model for reporting or charging AI cost to teams: cost pools, allocation drivers, the monthly split, an unallocated line and a readiness test for moving to chargeback.
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.
Excel template. One register of initiatives, tools and agents with owners, and one cost register in the seven layers of the AI TCO Framework.
