Enterprise AI Strategy

AI value is not an inference metric. Start with product outcome, cycle time, total cost, and cost of delay, then overlay inference investment.

Per-engineer token caps optimize the most visible cost while hiding labor, delay, and business value. Budget AI production by accepted outcome, not individual contributor.

Price the outcome before its tokens. A rational token budget is a function of total delivery economics, and even 150% of labor cost can make sense when it buys enough time.

Your CFO should not be asking what you spent tokens on as if the model invoice is a minibar tab. Your CFO should be asking why the highest-ROI production input in the software portfolio is underfunded.

Your token burn is not the problem. It is the first invoice honest enough to show that your engineering work has no value discipline.

Before you optimize token spend, measure the completed work, human attention, cycle time, and risk reduction those tokens bought.

Before finance audits token spend, audit the expenses that somehow escaped fiscal responsibility: offshore teams, staff augmentation, systems integrators, consulting partners, Scrum layers, agile coaches, planning ceremonies, and delivery management. How much did they actually cost, and what success rate did they produce?

Three times a week now an engineering leader I respect tells me their CFO wants them to justify the ROI on AI tokens. The question itself is the part I am puzzled by. A token is a raw material, the way steel is a raw material at a foundry. Nobody audits the flour invoice at the bakery. The question is not a token question. It is a measurement question, and it is being asked on the wrong floor of the building.