{"id":"VAL-003","section":"AI Usage, Spend & Value","requirement":"Measure spend, waste and portfolio decisions","acceptance":"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.","tier":"Tier-independent","whyItMatters":"License waste and duplicate tools are often the fastest way to fund the control.","verificationMethod":"POV + W","verificationTest":"Licensed versus active use for one enterprise AI tool, and the cost method explained.","formerly":null,"alsoIn":[],"sort":47,"adoptionContext":"Portfolio cost, overlap and retirement decisions can compare AI services across adoption tiers rather than treating a tier as a measure of value.","mappings":[{"frameworks":["rmf"],"explanation":"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.","kind":"framework","references":{"rmf":["GOVERN 1.7"]},"relation":null,"conditions":[]},{"frameworks":["iso"],"explanation":"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.","kind":"framework","references":{"iso":["A.4.5"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-003","frameworkRefs":{"ISO42001":["A.4.5"],"NIST-AI-RMF":["GOVERN 1.7"]}}