AI costs money in three ways. You pay to have it, you pay each time you use some of it, and people spend time learning it and checking it.
AI tools cost money through licences, through pay-as-you-go use, and through the time people spend learning, checking and fixing. If a budget only counts licences, it'll look fine while the real cost grows.
Track total cost per decision supported: licence cost allocated by active use, metered consumption, and people time for review, rework and enablement. Compare it with the cost of the decision before AI, not with the licence price alone.
1. Licences
A fixed monthly fee per person. Easy to budget, easy to see. The catch is that you pay whether or not the person uses it. A licence that sits idle for a quarter is pure cost.
Measure: cost per active user, not cost per licence. Divide total licence spend by the number of people who used the tool most weeks.
2. Consumption
Metered charges: per message, per agent action or per unit of compute. Agents built in Copilot Studio or Foundry often run on this kind of billing. It scales with use, which is fair, and can spike without warning, which isn't.
Measure: cost per decision. If an agent handles 3,000 tickets a month at a known consumption cost, you can compare that with what it cost a person to handle them.
3. People's time
Hours spent on training, prompting, checking output, correcting mistakes and dealing with the cases the model sends back. This rarely appears in any budget, and it can be the largest of the three.
Measure: sample it. Ask a handful of people to log time spent checking and fixing AI output for two weeks. Multiply up.
Putting them together
Add all three and divide by the number of decisions supported. Compare that with the cost per decision before the change. That's the honest test of whether it pays. The break-even calculator does a simpler version for licences.