The real cost of AI sits beyond the model

An inexpensive model call can become an expensive business process. Someone still has to connect the data, decide what a useful answer looks like and deal with the cases where the system fails. The invoice for the model captures only part of that work.

NIST’s AI Risk Management Framework treats assessment as an activity across the life of a system. That provides a useful discipline for commercial deployments: define the context, measure performance and decide who is responsible when results fall short.

For a small pilot, compare the time required to complete a task before and after deployment. Include checking and correction, not just generation. Then examine a sample of difficult cases, because a reassuring average can conceal the work that creates the largest cost.

Our practical reading is that teams should buy evidence before they buy scale. A narrow task, a clear owner and an agreed quality threshold create a more useful starting point than a broad claim about productivity. This is an operational interpretation of the framework, not a benchmark or a prediction of savings.