AI token spend is emerging as a new enterprise resource—one that finance must learn to forecast, allocate, and optimize against the value it delivers.
Generative AI is moving quickly from experimentation to essential infrastructure. Employees have woven it into routine daily tasks, and teams and applications are leaning on it more heavily every quarter. That growth comes with a new cost category finance wasn’t designed to handle: AI token consumption.
When AI is writing code and powering agents, token consumption can outpace traditional planning processes. Finance leaders suddenly find themselves in unfamiliar territory, needing to bring discipline to AI spending without becoming the function that kills adoption. Getting the balance right means treating tokens as an enterprise resource that must generate returns commensurate with its cost.
At SAP, we have been working through these questions firsthand. Here’s what we’ve learned from building, testing, and adjusting that framework.
You can’t manage what you can’t see
