KPMG survey: only 26% of companies have a comprehensive view of their AI costs, while 50% have some visibility and 22% have none or only see costs after billing
Only 26% of companies say they have a comprehensive view of their AI costs, according to a new survey
Context & Ripple Effects
This survey lands after related CEO surveys showed AI spending plans rising even as fewer than half of projects were reported to return more than they cost, and only a minority had delivered expected ROI or scaled enterprise-wide.
The cost-visibility finding adds an operational explanation for that gap: many companies are trying to assess AI’s business value without a complete view of the inputs behind it.
First-order effects
- Companies without comprehensive AI cost visibility have an immediate budgeting and ROI-measurement problem: they cannot reliably compare AI projects, providers, or deployments on total cost.
- Finance, procurement, and technology teams face pressure to assign and monitor AI spending before charges arrive, rather than treating it as an opaque shared technology expense.
Second-order effects
- AI vendors and internal platform teams will face more scrutiny over usage reporting, cost allocation, and the ability to distinguish experimentation from production workloads.
- As AI investment continues to rise, incomplete cost data can delay scale-up decisions and redirect spending toward projects whose costs and returns can be measured more clearly.
Third-order effects
- If cost governance remains behind adoption, enterprise AI competition may shift from access to models toward the operational tooling needed to control, attribute, and justify AI spend.
- The pattern suggests that broad AI experimentation will not automatically become enterprise-wide deployment; repeatable financial controls may become a prerequisite for scaling.
The trend: Enterprise AI is moving from an experimentation phase toward a cost-accountability phase, where spending visibility and measurable returns determine which deployments scale.