Finance executives are counting every token because AI usage can become a variable operating cost—and activity alone does not show whether that cost is paying off. The useful measure is cost per successful outcome, assessed alongside quality, human review, latency and risk.
Why token usage has become a finance concern
Tokens are units of text processed by an AI model. A workflow can consume them in its prompt, retrieved documents and other context, tool interactions, and generated response. As a result, spend may change with how a workflow is designed and used, not just with the number of employees or software licenses.
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Deloitte notes that token prices vary by model, infrastructure, configuration and market conditions. A token total therefore needs context: which workflow produced it, which model handled it, and what useful result came out. A high total may reflect valuable work—or repeated context and activity that deliver little business value. Deloitte’s guide to AI token economics for CFOs discusses the cost drivers.
What executives say about AI token usage and related costs
In its fifth US AI Pulse Survey, announced July 28, 2026, EY reported that 82% of senior leaders at organizations investing in AI were concerned about token usage and related costs. The survey also found that 64% said their organization actively monitored usage and had clear budget guardrails. EY described its sample as at least 500 US-employed decision-makers at senior vice president level and above. These are survey responses from senior leaders, not a census of CFOs or finance executives. EY’s July 2026 survey announcement provides the findings and sample description.
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EY Global AI Consulting Leader Dan Diasio said, “’AI saves time’ is no longer a sufficient business case when the costs are mounting and difficult to ascertain over the long run.” The point for finance teams is not to discourage adoption, but to ask whether a particular use of AI produces a measurable result at an acceptable total cost.
Measure cost per successful outcome, not tokens alone
A token count describes consumption, not value. For a finance team, the relevant unit might be the cost to produce an accurate report, resolve a payment exception, or complete another defined task at the required service level. The denominator should count successful outcomes—not all attempts—while the cost should include both AI and the human work needed to check, correct or approve results.
Boston Consulting Group (BCG) expresses return on AI as “RoAI = Economic return / Cost of human intelligence + Cost of tokens”. In practical terms, the denominator is the sum of human-intelligence cost and token cost. A workflow that uses fewer tokens but requires extensive review may not be more economical; a costlier model could be justified if it materially improves success, risk or turnaround time. BCG recommends identifying where usage occurs, shaping cost, then proving value or stopping or minimizing activity that does not justify its expense. BCG’s framework for controlling AI token costs describes this workflow-level approach.
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For each material AI workflow, connect usage and spend to an owner and an outcome. Track the factors that can change the cost or result:
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- Prompt length and the size of retrieved context or documents.
- Output quantity, model choice and reasoning effort.
- Tool calls, cache behavior and repeated agent loops.
- Success rate, quality, human review time, latency and risk.
Compare workflows on cost per successful outcome at the required quality and risk level. Include the cost of failed attempts and human correction where they occur; a cheap first response is not a useful saving if the task must be redone.
How token costs can show up in the P&L
BCG offers a management framework that separates reusable AI capability investment, token use for internal work as operating expense, and customer-facing inference as cost of goods sold. This can help finance and product teams ask where spend belongs and who should own it. It is not accounting advice: the precise treatment depends on an organization’s circumstances and accounting policies.
That distinction also affects accountability. Internal teams may own workflows that consume tokens for analysis or administration, while product teams may incur token costs each time a customer uses an AI feature. Assigning costs to the workflow owner makes it easier to evaluate whether usage supports a business result and whether its budget is appropriate.
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Controls that can shape usage
Gartner recommends managing AI costs through model choice, context, thresholds and oversight. These are governance methods, not guaranteed savings; the right configuration depends on the task and its quality, latency and risk requirements.
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Route work to a model suited to its complexity
Consider whether routine tasks can use a smaller or less costly model, while reserving more capable models for work that needs them. Evaluate routing on task performance and total workflow cost rather than token price alone.
Keep context and agent loops purposeful
Review prompts and retrieved material for unnecessary history or documents. Check whether an agent repeats the same context across loops or makes avoidable tool calls. Reducing context or interactions may change cost, but should not remove information needed for a reliable answer.
Set thresholds and escalation rules
Define which usage patterns trigger an alert, review or escalation before spend exceeds plan. Set a review cadence for high-consuming workflows, and make clear who can change a limit or approve an exception. Monitor projected spend separately from observed usage and from realized business value.
Questions finance teams should ask workflow owners
- Which workflows consume the most tokens, and what outcome does each produce?
- What is the cost per successful outcome at the required quality and risk level, including human review?
- Who owns each workflow and budget, and where does its cost belong in the P&L?
- What share of work is routed to the most capable models, and can a smaller model meet the requirement for routine tasks?
- Are prompts, retrieved documents, or agent loops carrying unnecessary context or repeating work?
- Which thresholds, escalation rules and review cadence will flag usage before it exceeds plan?
These questions turn usage visibility into a decision process: preserve workflows that deliver value at acceptable cost and quality, improve those with avoidable consumption, and reconsider those whose outcomes do not justify their expense.
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Keep forecasts and industry claims in scope
In a June 24, 2026 release, Gartner forecast that AI coding costs would exceed an average developer’s salary by 2028, citing token growth and consumption-based licensing. That is a forecast about AI coding workloads, not an observed result or a prediction for all enterprise AI spending. Gartner’s release states the scope and horizon.
Accenture’s September 2026 AI Tokenomics Guide for CIOs describes a survey of 750 senior global executives across 17 countries and reports that less than one dollar in five of enterprise token spend can be traced to a quantified financial outcome. The finding is Accenture’s report framing, not a universal measurement of every enterprise. Its practical implication is to link spend with attributable outcomes rather than assume that usage proves value. Accenture’s AI Tokenomics Guide for CIOs provides its survey context.
What this means for finance AI projects
AI agents in finance can touch planning, forecasting, reporting, procurement, payments, treasury, tax and accounting-close work. In a May 4, 2026 announcement, OpenAI said it and PwC were building agents around these workflows; the announcement described PwC’s role in finance transformation, controls and implementation. It also highlighted the need for CFOs to see usage, token consumption and projected spend as agentic workflows scale. This is a vendor announcement, not evidence that every listed workflow is deployed or produces a particular return. OpenAI’s announcement sets out the collaboration.
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