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
Most finance leaders wouldn’t manage a major cost category from a single line item, yet that’s exactly where companies start with AI. Total spend matters, but it won’t tell you which teams, workloads, or usage patterns are driving that spend, or whether the consumption is actually producing useful outcomes.
Getting that visibility requires real collaboration across commercial, engineering, finance, and product teams. Finance brings the forecasting questions and accountability framework. Other functions bring the operational context that makes the numbers meaningful.
In our experience, repeated forecasting cycles generally improved our financial models as we layered in more operational detail around usage. The broader lesson: when a cost category is new and fast-moving, don’t wait for precision before acting. Start with enough transparency to make better decisions, then sharpen the model as patterns emerge.
Someone has to own it
Our most consequential insight was philosophical rather than financial. We learned that visibility alone isn’t enough and that consumption needs an owner.
A centralized AI budget makes early experimentation easy, but it also disconnects the people spending tokens from any financial accountability for them. As AI becomes more deeply embedded in business processes, that model breaks down.
This isn’t an argument for immediately charging back every LLM call with forensic accuracy. It’s an argument for managing AI consumption the same way companies manage other enterprise resources such as software, external services, and labor. Give decision-makers a clear picture of what their teams are consuming and what outcomes that consumption is expected to produce.
At SAP, allocating token costs to business areas has shifted the conversation from “How much are we spending?” to “What are we getting for this?” and “Is this the right place to invest more?” That’s a healthier conversation.
Token spend is not a technology line item. It is a strategic and operational investment decision. The goal is to make AI spending intentional, not just cheap.
Cost per token is the wrong scorecard
A large AI bill draws attention, but optimizing purely on cost can lead to exactly the wrong decisions.
The more useful question is the relationship between consumption and business impact. An AI tool that meaningfully accelerates software development, reduces repetitive work, or improves customer service will carry real token costs. Cutting that usage simply because the line item is visible could destroy more value than it saves.
We saw this firsthand. After rolling out AI developer tools at SAP, we recorded a mid-double-digit percentage increase in pull-request merge rates, a clear signal that development work was moving faster. The consumption was worth it.
Finance needs a paired view: cost metrics alongside value metrics. Governance without that view risks optimizing for cost at the expense of value creation.
Guardrails should target waste, not adoption
As usage scales, controls become necessary, but the right controls are surgical, not sweeping. When we examined consumption patterns in detail, three root causes of disproportionate spend emerged: power-user and automated-agent concentration, model misalignment, and tool proliferation.
Those are the areas where guardrails earn their keep. Controls matter because they focus on the sources of avoidable spend rather than putting a blanket brake on usage. At SAP, our response centered on three levers: token capping to prevent runaway consumption, model routing to better match capability and cost to the task, and tool rationalization to eliminate redundancy and concentrate investment where utilization justified it. That work helped contain a triple-digit-million-dollar financial risk while keeping adoption moving forward.
The principle is simple: remove waste, preserve productive demand.
From cost control to value governance
AI isn’t going to pause for the next planning cycle. Its capabilities, usage patterns, and economics will keep shifting, and the governance model around it needs to keep pace.
The work is not complete. The next step is to embed these practices into regular planning and reporting, assign clearer ownership, improve allocation, and build forecasting capabilities that can anticipate where costs are heading before they arrive.
Companies that do this well will still have an AI bill to pay. But they will have the transparency and accountability to tell the difference between consumption that is creating value and consumption that isn’t and direct investment accordingly.
Lukas Deutsch is chief controlling officer at SAP.
David Imbert is chief marketing officer for SAP Financial Management.
