AI Tokenomics

What every executive needs to know

Your AI program’s largest financial risk isn’t the model you picked or the people you hired. It’s a unit of consumption that never made it into the budget.

Enterprise AI programs routinely blow through budget. The cause is rarely the tools or the team. It is that nobody modeled the economics of AI token consumption before launch, so the first real billing cycle arrives as a shock. Gartner’s April 2026 survey of 782 infrastructure-and-operations leaders found that only 28% of AI use cases fully met ROI expectations.

The most common reason cited was not model capability. It was expecting too much, too fast. A token is the basic unit of work an AI model charge for. Every question asked, document parsed, and line of code generated consumes tokens. On their own they cost fractions of a cent. Put hundreds of engineers, dozens of automated workflows, and millions of monthly interactions behind them, and the totals compound in ways ordinary IT budgeting can’t predict, because what drives them is how the system is built rather than how many people use it.

This brief lays out the economics in plain language: what drives cost, how to forecast it, how to control it, and what the return looks like once the numbers have been checked. No equations, no vendor jargon.

Enough to fund the program correctly the first time.

To learn more read the PoV