How CFOs can manage AI costs and prove business value

Earlier this year, a bill arrived from one of 1Password’s AI vendors for 5x the value of the original contract. The initial agreement came in below a certain threshold, so it never reached the right approvers for review. By the time it did, we had a much clearer understanding of how quickly AI costs can add up. This unpleasant surprise revealed a structural gap between IT, Finance, and end users when it came to AI billing and consumption. Although Finance was accountable for the budget, it had no way to see what was being spent on AI until it was already spent. Other CFOs are seeing the same pattern of a bill arriving that no one can explain. Now, as leaders grapple with soaring and unpredictable token costs, what was considered a budget line item just a few months ago has become a board-level topic. Managing AI costs requires Finance and IT to share visibility into consumption before the invoice arrives. Organizations need a way to track usage, forecast budget risk, assign ownership, and connect AI investments to measurable business outcomes. Why AI costs are harder for Finance to forecast Effective Finance and IT are built on predictability: per-seat SaaS contracts, annual budget cycles, and predictable renewal dates. Contracts with AI vendors are fundamentally different. They're based on consumption pricing, which scales with usage, not headcount. As AI usage grows across a team or department, the bill can literally grow overnight. The closest comparison is cloud, which also uses consumption pricing. Cloud sprawl took years to bring under control, but Finance eventually learned to model it. With AI, there is no time for a learning curve. Pricing tiers change constantly, new models ship overnight, and AI adoption continues to accelerate. Why Finance sees AI overspend too late While Finance is responsible for AI spend management, the tools Finance relies on weren't designed to provide real-time visibility. Getting a complete picture requires going through the IT team, which aggregates data from multiple vendor dashboards and provides insight days after the fact. By the time Finance receives the data, usage is out of date. Without a responsive feedback loop, some companies simply pull the only lever available to them and set a hard cap on AI spending. Caps may give some immediate control but they can also restrict the AI adoption that leadership is actively pushing. Finance leaders need insight that connects spending to productive, useful activity, so they can do what they do best: plan for the future. How visibility improves AI spend governance Visibility is the foundation of AI spend governance. Without it, Finance can't forecast, govern, or manage AI costs effectively. At 1Password, we use AI Spend and Consumption Management in SaaS Manager to get real-time visibility across our AI vendors. That changed the conversation immediately. We stopped reconstructing what had already happened and started anticipating what was coming, when we might fall short, and where spend would do the most good. Based on that visibility, we worked with IT and procurement to redesign our vendor approval process, giving us a mechanism to review consumption-based contracts before they were signed. It also gave us the opportunity to start tying AI spend directly to business outcomes. Visibility also drives efficiency by showing which teams are using which models, and at what cost. There's a 300x cost difference between the most and least expensive models, and most organizations have zero visibility into which models their employees are using or how they're using them. We’ve found that employees often default to the most expensive frontier model, but for most tasks, a far less expensive model produces the same result. Closing that gap reduces costs without touching adoption or having to automatically put caps in place. 5 steps CFOs can take to manage AI costs Putting a governance framework in place helps lay the foundation for controlled AI spend. #1 Audit your current AI exposure Before building a governance framework, Finance needs to know what it's working with. Map every AI tool in the organization, not just the largest contracts. Identify which agreements have consumption elements, which vendors are billing by token or usage, and which contracts currently sit below approval thresholds. This exercise will surface spending that Finance has not reviewed and commitments that look fixed but have flexibility. #2 Model usage before approving consumption-based contracts This is a procurement requirement Finance can put in place immediately. Any team requesting an AI tool with a consumption element should be required to model expected usage before the contract is approved. What will adoption look like in 30 days? At 90 days of full use across the department? That modeling exercise surfaces the real financial commitment, not just the number stated on the agreement. A contract that starts at $250,000 can represent a $1 million obli

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