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Business AI spending is unusually difficult to forecast when charges depend on usage, purchasing is spread across teams, or pilots expand into production workflows. It is not impossible to budget: companies can improve control by making costs visible, assigning an owner, forecasting multiple scenarios, and checking spend against measurable outcomes.
Why AI budgets are harder to predict
Traditional software budgets often start with a known number of seats and a recurring subscription. AI can add consumption-based charges that shift with the number and complexity of requests, the model selected, and how deeply a tool is built into a workflow. A pilot with a small group may therefore provide a poor guide to the cost of broader use.
Forecasting gets harder when employees or departments buy tools independently, software includes AI features that are not itemized clearly, or usage expands from experiments into customer-facing and agentic workflows. More steps in a workflow can mean more model calls, data processing, and human review. These conditions—not every company’s AI adoption—make spending particularly difficult to control.
OpenAI reported that, among its own enterprise customers, ChatGPT message volume grew eightfold and API reasoning-token consumption per organization grew 320-fold year over year in 2025. Those figures illustrate how quickly use can change within one provider’s customer base; they are not an industry-wide growth rate. OpenAI’s 2025 enterprise AI report describes that usage.
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What the spending figures do—and do not—show
Market growth forecasts indicate the scale of the market, not what a particular company will spend. Gartner forecast worldwide end-user spending on AI models and platforms at $64.252 billion in 2026, up 63.4% from $39.311 billion in 2025. The forecast covers models and platforms, not the full cost of adopting AI inside organizations. In July 2026, Gartner analyst Arunasree Cheparthi said, “Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes.” Gartner’s forecast emphasizes that usage and cost control matter alongside market expansion.
Company surveys offer a different view: what leaders expected to spend versus what they report spending. In EY’s fifth US AI Pulse Survey, 23% of respondents said their organizations spent at least $10 million on AI, compared with 35% who had expected to reach that level a year earlier. Three percent reported committing at least half of their total budget to AI, versus 18% who had previously expected to do so. EY surveyed 534 senior US business leaders in selected industries; the results are not a census of businesses. EY’s 2026 survey results reflect that specific sample.
In a separate Gartner survey, 85% of functional leaders planned to increase AI spending in 2026 after allocating an average 12% of their functional budgets to AI in 2025. The survey covered 1,303 respondents at organizations with at least $50 million in fiscal-year 2025 revenue. These figures measure plans and past allocation among that survey’s respondents, not all companies. Gartner’s organizational survey also warns that financial visibility can be weak.
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The measures are not interchangeable: the surveys use different samples, questions, dates, and denominators. Taken together, they show why a fast-growing market should not be mistaken for a reliable company-level forecast—and why intentions and realized spending can diverge.
What belongs in an AI budget
A model or API invoice is only one part of the total cost of putting AI into a business process. Start by mapping the whole portfolio and identifying costs that may sit in different departments or vendor bills.
- Subscriptions and licenses: paid seats, model licenses, API access, and AI features bundled into other software.
- Usage and compute: tokens or other consumption charges, cloud capacity, GPUs, and costs associated with running models.
- Data and integration: data pipeline development and maintenance, orchestration, and vector databases.
- Model development: training, fine-tuning, and experimentation, including work that does not reach production.
- People and controls: staff training, governance, and human review of AI output.
IBM’s overview of AI total cost of ownership identifies model, data, and infrastructure expenses among the costs companies need to consider. Kiplinger also highlights staff training, output review, and governance. IBM’s AI total-cost-of-ownership guide and Kiplinger’s budgeting guidance explain why a vendor’s visible usage charge does not capture the whole bill.
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Why spend can disappear from view
When teams buy tools separately, experimentation is spread across providers, or software embeds AI charges in broader contracts, finance may not have a complete view of usage and commitments. McKinsey says 20–30% of AI spend is often unaccounted for in its experience, citing fragmentation across providers, model vendors, software, experimentation, and business units. Treat that as McKinsey’s reported experience, not a universal audited rate. McKinsey also reports that 20–25% of companies in its survey had mature AI FinOps practices. McKinsey’s analysis of AI FinOps describes the visibility challenge.
Ownership is one practical weakness to check. SpendHound, a software vendor, reported in its 2026 research that 22% of surveyed finance and procurement leaders said no single person owned the AI budget. Its report combines a survey with proprietary spend data, so the finding should be read as that vendor’s benchmark, not a representative estimate of all businesses. SpendHound’s 2026 report sets out its findings and methodology.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to make an AI forecast useful
1. Name an owner and bring teams together
Assign one person or function responsibility for maintaining the AI-spend picture. Finance, IT, procurement, and the business leaders funding workflows should contribute. The owner does not need to approve every experiment, but should be able to see commitments, usage, and who is accountable for each significant use case.
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2. Consolidate the portfolio before projecting it
List subscriptions, API and model usage, cloud and GPU capacity, software with embedded AI, experiments, data work, and relevant staff time. Where possible, record the provider, team, workflow, billing basis, and business purpose for each item. This gives the forecast a usable baseline instead of relying on one prominent invoice.
3. Build a range, not a single-point estimate
For each substantial workflow, estimate a baseline, expected, and high-use case. Make assumptions explicit: adoption, volume of work, workflow scope, model choice and routing, and the amount of human review. Consumption-based costs should be modeled against plausible use, not just current pilot traffic. Kiplinger recommends a floor-and-ceiling range; McKinsey recommends scenario planning as adoption and model choices change.
4. Update the forecast as usage arrives
Compare actual consumption and commitments with the scenarios, then revise the forecast regularly. Kiplinger recommends rolling quarterly forecasts rather than treating one annual estimate as fixed. A review should identify whether higher spending reflects intentional adoption, an unexpected usage pattern, duplicated tools, or a workflow that needs redesign.
5. Tie spend to an outcome and compare real alternatives
Agree on the intended result for each major use case—such as productivity, revenue, reduced risk, or customer experience—and track it alongside cost. IBM says 37% of AI initiatives delivered the business value senior leaders expected by the end of 2025, citing research by the IBM Institute for Business Value with Oxford Economics. That result is a reminder to measure outcomes, not proof that any particular project will succeed. IBM’s report on the AI value gap discusses the finding.
When comparing models or deployments, look beyond token price. Assess total task cost together with output quality, latency, reliability, risk, and operational burden. A cheaper model may require more retries or review; a premium model may be unnecessary for a simpler task. McKinsey notes that teams can default to premium models when trade-offs are unclear. If measured value does not justify a workflow’s cost, pause it or redirect resources to a better-supported use case.
Budget discipline matters more as adoption grows
Rising AI allocations do not guarantee returns. Gartner’s functional-leader survey found planned increases in spending, while IBM’s separate research found that fewer than half of initiatives delivered the business value leaders expected. The samples and measures differ, so the percentages should not be compared as if they described the same organizations. Together, they reinforce a practical rule: authorize AI spending with a forecast, but continue it based on visible use and measured results.
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