Acceptance criteria
Report licenses, tokens, provider cost, model efficiency, redundant tools and cost per task or outcome, and produce the evidence behind a decision to scale, pause, optimize or retire a tool.
Why it matters
License waste and duplicate tools are often the fastest way to fund the control.
How to verify
Licensed versus active use for one enterprise AI tool, and the cost method explained.
OWASP adoption context
Portfolio cost, overlap and retirement decisions can compare AI services across adoption tiers rather than treating a tier as a measure of value.
Framework mappings
Cost and outcome evidence can inform a decision to retire an AI tool. The relationship is an input to decommissioning decisions; financial optimization does not establish safe retirement or treatment of residual risks.
Token use and model efficiency provide resource-consumption information for identified in-scope AI services. Cost or ROI alone is insufficient, and these metrics establish neither capacity adequacy nor responsible-AI supplier alignment.