The Real Cost of Enterprise AI Is Operational

Model pricing makes AI cost appear easy to calculate: multiply tokens by requests and add licences. The resulting estimate may be precise and still materially wrong.

Enterprise cost sits around the model: connecting systems, governing access, improving sources, evaluating changes, reviewing outputs, supporting users and resolving exceptions. A cheap response that requires ten minutes of expert verification is not cheap.

Build unit economics around completed work. Include inference, retrieval, integration, human review, failure recovery and platform operations. Segment by workflow because one high-context task can cost far more than a simple classification.

Then compare with the current process, including quality and delay rather than merely labour minutes. Some systems create value through better decisions or faster response rather than headcount reduction.

Operational cost is not an argument against AI. Ignoring it is an argument for pilots whose economics collapse as soon as real users, exceptions and support arrive.