Deloitte asked 3,500 leaders what they track about AI cost. 71% said the total bill. 44% said what one unit of it costs. Eight days earlier, a separate Deloitte survey of 200 CFOs had found that when an AI cost control fails, only 7% of their organizations respond automatically or in near real time.
Read side by side, the two surveys describe a situation most finance teams will recognize. The AI bill is a number that keeps growing. Nobody can say with confidence which workflow, team or model is driving it. And when it crosses a line someone approved, it takes days or weeks to do anything about it. This post walks through what the surveys found, why the gap between the total and the unit matters more than the total itself, and what a finance leader should ask to see before the next budget cycle.
What Deloitte found, in two surveys
The State of AI in the Enterprise report, released October 29, surveyed 3,500 director to C-suite leaders across 25 countries and six industries between May and June 2026. Most of the headline is positive. 81% of respondents report faster decision-making and execution, and 28% now report revenue gains from AI, up from 20% in Deloitte’s January report, which we covered in The Enterprise AI Revenue Gap. The cost finding sits further down the page and deserves a slower read. 71% track total AI inference and API spend, 51% track token volume, and just 44% track per-unit inference cost.
Costi Perricos, Deloitte’s Global GenAI Business leader, framed it this way: “Many enterprises are getting better at watching their total AI bill, but far fewer understand the unit cost underneath it.” The next line was short: “That gap is where ROI can get lost.”
The Q3 CFO Signals Survey, published October 21, polled 200 CFOs at North American organizations with at least $1 billion in revenue. 53% said their primary practice is to review AI usage costs retrospectively, through vendor bills, periodic reports, dashboards or manual analysis, while 47% monitor in or close to real time. Only 20% have advanced AI-specific controls such as token or workload-level attribution, AI gateways or caching. 56% rely on basic AI-specific controls and 24% on general financial controls alone. The two surveys sample different people, so the numbers should not be stacked into a single statistic, but they point in the same direction.
Why a total bill is not a number you can manage
A total tells you what happened. It does not tell you why, and it does not tell you what to change. That worked well enough in a seat-based software world, where cost stayed predictable and higher usage usually meant the rollout was succeeding. Deloitte’s CFO survey notes that commercial AI providers now more commonly charge per token than per seat, and that bills can spike quickly because of large context windows or long conversation loops. Under that pricing, usage and cost stop being separate questions.
The CFOs were asked what limits their ability to oversee AI spending. 40% named a lack of AI cost governance, processes or policies. 38% named limited timely insight into token usage and unit costs. 36% named limited standards for model selection, prompts, data retrieval or workflow design. Notice that the second and third answers describe visibility and standards below the level of the invoice. The same pattern shows up in our writing on FinOps for AI, where the cloud cost playbook starts to break once the unit of billing becomes a token, and in why an AI bill can grow even as per-token prices fall.
What unit cost means for a copilot, a coding tool and an agent
“Unit cost” is not one thing, which may be part of why fewer than half of Deloitte’s respondents track it. For a copilot, a useful unit is cost per active user per month, set against how often that person actually uses the tool. For a coding assistant, it might be cost per developer or per merged pull request. For an agent, it is cost per completed workflow or per resolved case. Each one requires the same groundwork: attribute spend to a person, team, provider and project, then join it to a work outcome. Deloitte’s own advice points the same way, saying leaders need visibility into workflow-level costs and the usage that drives them.
This is the layer Olakai was built for. Olakai is a vendor-neutral Enterprise AI Intelligence Platform, and it attributes spend across tools such as Anthropic, Cursor and OpenAI by developer, provider, project and department. It also reconciles provider cost data against what was actually billed, including the day or two of lag in Google Vertex AI cost data, so the unit costs a finance team sees trace back to a real invoice. Custom KPIs then tie that usage to an outcome the business already measures.
The days between an overrun and a shutoff
The most uncomfortable finding in the CFO survey is about speed. When AI cost controls fail, 74% of respondents say their organizations typically pause or shut down the related usage within several business days to several weeks. 19% address it the same business day. 7% respond automatically or in near real time. Compare that to how an AI bill moves. A long conversation loop or a runaway agent does not wait for the next monthly review.
Closing that gap has two halves, and most organizations have neither. The first is detection early enough to matter, meaning an alert that arrives while the month can still be saved rather than an invoice that arrives after. Olakai budgets are evaluated daily against imported cost data, with threshold alerts at 50%, 80% and 100% of a budget and a forecast alert when the current run-rate projects past the limit by month end. We describe the mechanics in Inside AI Spend Governance. That is a day, not a week, and we would not call it real time. The second half is a decision about who is authorized to pause what, written down before the overrun instead of negotiated during it. No tool replaces that policy. It can only make sure the alert shows up while the policy can still be applied.
Autonomy is rising while accountability stays unclear
The State of AI report adds a reason why response speed matters beyond cost. 40% of enterprises now allow AI systems to execute decisions or take actions without human approval, at least to a moderate degree, while 23% say accountability is unclear when an AI system produces a negative outcome. Deloitte’s guidance is that governance and safety controls should scale with the level of autonomy and the potential consequence of an action, with systems that execute needing more oversight than systems that only recommend. We cover that side in our AI governance work.
An agent that can act is also an agent that can spend. It is a reasonable inference, though not a Deloitte finding, that organizations unable to name an owner for a bad outcome often cannot name one for a bad bill either. Agent IQ in Olakai addresses this at the agent level, with executions, cost, outcomes and an audit trail for each agent, so ownership and spend sit on the same record.
What a CFO scorecard should show
If the goal is to move from watching the total to managing the unit, the monthly AI review needs a different set of numbers. A workable starting scorecard has five lines:
- Unit cost for each major workflow, copilot seat or agent, not just spend by vendor.
- Token volume by tool, team and model, so a spike has a place to be traced to.
- Time to pause, meaning the median hours between a budget breach and an actual decision.
- Share of AI spend attributed to a named owner.
- Number of AI systems allowed to act without human approval, each with an accountable person.
None of these require a new finance process. They require the same attribution and ownership data that an AI system of record holds, which is why that record matters to finance, and why the CFO view in the CFO use case is built around spend, usage and return together. If you want a shorter list, our post on three token cost metrics for CFOs is a good place to begin.
Where to start
Pick the single most expensive AI tool or agent in your portfolio and answer three questions for the last month: what did it cost per unit of work, who owns it, and how long would it take to pause it. If any of those takes more than an afternoon to answer, you have found your version of the 44% and the 7%. The case for AI is getting stronger, with 28% of Deloitte’s respondents now reporting revenue gains. But the same question we raised about JPMorgan’s $700 million AI bet applies at every scale: the return is only believable when the cost side is measured as carefully as the benefit side.
Talk to an expert about measuring AI cost per unit, setting budgets that alert before the month is lost, and putting an owner on every AI system. Olakai gives finance, IT and AI leaders one view of what AI costs and what it returns.