Part of the AI in finance series.
The core problem with AI costs in the 2027 group budget is that the dominant billing model has moved from fixed seat licences to consumption-based token charges, creating a variable cost line with no natural volume driver in a standard budget model. Deloitte's Q3 2026 CFO Signals survey of 200 North American CFOs at organisations with revenue of at least $1bn found that 53% of CFOs primarily review AI usage costs retrospectively - after the money has been spent. The same survey found that 80% use less advanced cost controls for AI than for other cost categories, and that 74% take several business days to several weeks to pause or shut down overrunning usage. On the r/CFO forum, the question is already live: 'How are you handling token budgets with AI initiatives? Seem like a shadow cost functional leaders overlook.' It is. The 2027 budget cycle is the point to fix it.
Why does the licence-fee assumption break for AI?
The licence-fee assumption breaks because token-based billing scales with usage volume, not headcount, so a budget set as a per-user annual charge will almost certainly misprice the actual spend. Deloitte's Q3 2026 CFO Signals survey found that barriers to controlling AI costs include no AI cost governance or policy (40% of respondents), limited timely insight into token usage and unit costs (38%), and limited standards for models, prompts, and workflow design (36%). Gartner, as reported by CFO Dive on 13 October 2026, described AI costs as a 'material margin risk' and noted that 83% of CFOs report fewer than half of their AI initiatives have produced measurable gains. A Datarails-commissioned survey - research funded by a finance software vendor, so read with that context - found that 32% of US finance leaders exceeded AI budgets by at least 10% in the past 12 months. The pattern is consistent: costs arrive faster than the governance framework that was meant to contain them.
How should the 2027 AI cost line be structured?
Split the AI cost line into two distinct categories: fixed costs covering licences and platform subscriptions, and variable costs covering consumption usage. The fixed line behaves like any software commitment and belongs in the technology budget with a named contract owner. The variable line is different in kind. It should be built bottom-up from named workflows and their expected transaction volumes. Each workflow owner provides an estimate of volume and approximate usage intensity per task. Finance then aggregates those estimates into a usage budget with a clear driver logic. Any IT-submitted AI forecast that arrives as a lump sum should be challenged with three questions: which workflows does this cover, what are the expected volumes, and what is the estimated usage per task? Without those drivers, the number cannot be validated and cannot be monitored during the year.
How does the group layer work - where does the AI cost sit?
Group finance must make an explicit structural decision about where AI costs are owned and how they flow through the group. Centrally purchased AI capacity can be held at the centre as a shared service cost, or recharged to subsidiaries on a usage basis. Each approach has different visibility and accountability implications. The practical minimum is that every business unit has a named cost owner for its AI usage and an agreed ceiling. Without a ceiling, consumption-based costs have no natural brake at the business unit level. Where central AI is recharged across borders, there are transfer-pricing consequences: take legal and tax advice before settling the recharge methodology. For the mechanics of building that multi-entity budget layer, see group budget consolidation across multiple entities.
Should pilots be in the same budget line as production?
Pilots should sit in a separate budget line from production AI spend, each with a named decision date. Mixing pilot and production costs in a single AI line makes it impossible to evaluate whether a pilot has earned the right to scale. A pilot budget is an option: it expires at the decision date as either a go or a stop. Production spend is a committed operating cost with a workflow owner and a volume driver. The governance and audit trail for that transition is covered at AI governance and the finance audit trail. If pilots are consistently failing to reach production, the causes are usually upstream of cost - see why AI finance pilots stall for the common reasons.
What should the monthly AI cost review cover?
Monthly AI cost review should compare actual consumption against the workflow volume drivers used to build the budget - not simply against the budget number itself. Gartner, via CFO Dive, found in July 2026 that 45% of finance AI investment targets productivity, with only one in five targeting decision quality. A review that only checks spend misses whether the spend is producing the outcome it was approved for. The short list for each monthly review: actual spend by workflow against budgeted volume driver; variance explanation from the workflow owner; status of any pilot with a decision date in the period; any workflow where consumption has reached its ceiling; and one line on whether the approved outcome - the productivity or quality target - is being tracked. The incentive structure around that outcome tracking matters: how finance function incentives shape AI adoption addresses that directly. KPMG's Q2 pulse, as reported by CFO Dive, found that 29% of finance leaders struggle to understand and control AI operating costs as systems scale - a monthly driver-based review is the structural answer to that problem.
What is the link between AI costs and the headcount budget?
The AI cost line and the headcount line are not independent: where AI drives productivity in named workflows, the workforce plan for those functions should reflect it, and the 2027 finance team headcount plan sets out how to make that connection explicit.
Common questions
Why are AI costs harder to budget than traditional software?
AI costs based on token consumption scale with usage volume, not user count, so a per-seat budget assumption will misprice actual spend. Deloitte's Q3 2026 CFO Signals survey of 200 North American CFOs found that 53% review AI usage costs retrospectively, after the money is spent. The same survey found that 74% of CFOs take several business days to several weeks to pause overrunning AI usage once it is identified.
What is the right way to build an AI usage budget bottom-up?
Budget AI usage from named workflows and their expected transaction volumes, with each workflow owner providing volume and usage-intensity estimates. Any AI forecast submitted as an IT lump sum should be challenged by asking which workflows it covers, what the expected volumes are, and what the estimated usage per task is. Without those drivers, the number cannot be validated or monitored during the year.
Should AI pilot costs be in the same budget line as production spend?
No. Pilots should sit in a separate budget line, each with a named decision date. Mixing pilot and production spend makes it impossible to evaluate whether a pilot has earned the right to scale. A pilot budget is a time-limited option; production spend is a committed operating cost with a volume driver and a workflow owner.
What are the transfer-pricing implications of recharging AI costs across borders?
Where a group centrally purchases AI capacity and recharges it to subsidiaries in other jurisdictions, the recharge methodology has transfer-pricing consequences under local and international tax rules. Finance should take legal and tax advice before settling the recharge basis - this is not a standard intercompany cost allocation.
What should a monthly AI cost review include?
A monthly review should compare actual AI consumption against the workflow volume drivers used to build the budget, not just against the budget total. It should include variance explanations from workflow owners, pilot status against decision dates, any workflow that has reached its usage ceiling, and a line on whether each approved workflow is delivering its stated outcome. Gartner, via CFO Dive in July 2026, found only one in five finance AI investments targets decision quality - outcome tracking closes that gap.