Beyond Token Costs: Measuring the Cost of a Verified AI Outcome

A low inference bill does not establish that an AI workflow is economical. The relevant expense includes the work required to check, correct, and complete a business task. This paper develops a conceptual accounting framework from research on agent evaluation and guidance on technology unit economics. It connects a fixed cohort of requests to execution records, human effort, shared costs, and independently checked outcomes. The proposed measure, cost per verified outcome, retains expenditure on unsuccessful and unresolved cases while counting each accepted business result once. An illustrative invoice-processing comparison shows how a workflow with higher model expenditure can have lower total cost because it requires less correction. A reporting architecture preserves the evidence behind both the numerator and denominator. Procurement and deployment decisions should consider verified completion, quality, elapsed time, and cost together. The framework offers an auditable comparison method, not evidence that a particular model or workflow is cheaper. Its usefulness depends on credible verification and consistent accounting boundaries.

Artificial Intelligence