Business AI is unusually hard to budget when charges rise and fall with usage, teams buy tools independently, or pilots become production workflows. But “almost impossible” is not inevitable: companies can improve forecasts by making usage visible, assigning an owner, modeling more than one scenario, and measuring costs against outcomes.
Why is business AI spending difficult to forecast?
Traditional software budgets often start with a known number of seats or a contract. AI can add consumption-based charges that change with prompts, model choice, workflow volume, and how often a system retries or delegates work. Meanwhile, AI features may be bundled into existing software, and separate teams may acquire tools or cloud capacity without a shared view of the total.
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The challenge grows as AI moves from experiments into routine operations. More users and automated workflows can increase demand; changing models, data requirements, review procedures, and infrastructure can also change the cost of delivering the same business function. The result is not that every company is overspending, but that a single annual estimate can quickly become unreliable unless it is revisited.
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Market growth is not an individual company forecast
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. That forecast signals market growth; it is not an estimate of what any particular business will spend, and it does not cover every cost of adopting AI. Gartner said in July 2026 that model spending is increasingly usage-driven and that buyers are focusing more on efficiency, cost control, and measurable outcomes. Gartner’s market forecast
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Budget plans and reported spending can diverge
In EY’s fifth US AI Pulse Survey, 23% of respondents said their organization spent at least $10 million on AI, compared with 35% who had expected to reach that level a year earlier. Three percent said they had committed at least 50% of their total budget to AI, compared with 18% who had previously expected to do so. EY surveyed 534 senior US business leaders in selected industries; these results are not a census of businesses or a universal spending pattern. EY’s 2026 survey
Separate evidence points to rising investment and uneven returns, but the measures should not be conflated. Gartner reported that 85% of surveyed functional leaders planned to increase AI spending in 2026, after allocating an average of 12% of their functional budgets to AI in 2025. Its survey included 1,303 respondents at organizations with at least $50 million in fiscal-year 2025 revenue. IBM, citing research by the IBM Institute for Business Value with Oxford Economics, said 37% of AI initiatives had delivered the business value senior leaders expected by the end of 2025. These figures come from different samples and questions, so they do not establish a direct comparison between spending growth and returns. Gartner’s organizational survey; IBM’s discussion of AI costs and value
What belongs in an AI budget?
A model or API invoice is only one part of the cost of putting AI into a business workflow. Map the full portfolio so a low-looking usage bill does not conceal costs elsewhere.
| Cost area | What to include |
|---|---|
| Access and software | Subscriptions and seats, model licenses, API fees, and AI features embedded in existing software. |
| Model use and development | Token consumption, experimentation, training, and fine-tuning. |
| Compute and supporting systems | Cloud or GPU capacity, orchestration, vector databases, and related infrastructure. |
| Data and operations | Data pipeline development and maintenance, integration, governance, and security work. |
| People and review | Staff training, time spent operating workflows, and human review of AI output. |
IBM’s overview identifies model, infrastructure, and data costs; Kiplinger also calls out training, governance, and human output review. The exact mix depends on the workflow and deployment, so a vendor’s token or seat price alone is not a total-cost estimate. IBM’s AI cost overview; Kiplinger’s guide to budgeting for AI at work
How can a business build a more reliable AI forecast?
- Map where AI is already being bought and used. Gather subscriptions, seats, model and API consumption, cloud and GPU capacity, embedded software features, experiments, data work, and staff effort. Include business units, not just centrally approved projects.
- Name an accountable owner. Give one person or function responsibility for consolidating the view, with finance, IT, procurement, and business leaders contributing. Decentralized purchasing and unclear ownership make it harder to see commitments and anticipate demand.
- Build low, expected, and high-use cases. For each workflow, estimate adoption, volume, model choices or routing, and the human review burden. State assumptions rather than treating one estimate as certain. Kiplinger recommends a floor-and-ceiling range; McKinsey recommends scenario planning as adoption and workflow scope shift.
- Update the forecast against actual usage. Compare estimates with provider, model, user, or workload consumption where those details are available. Revisit assumptions regularly—Kiplinger recommends rolling quarterly forecasts—rather than waiting for an annual budget cycle to expose a mismatch.
- Judge spend against an agreed result. Connect a workflow’s cost to an outcome such as productivity, revenue, risk reduction, or customer experience. If results do not justify the ongoing cost, consider changing, pausing, or reallocating the work.
McKinsey says that 20–30% of AI spend is often unaccounted for in its experience, citing fragmentation across providers, model vendors, software, experimentation, and business units. That is McKinsey’s reported experience, not a universal audited rate. Its survey also found that 20–25% of companies reported mature AI FinOps practices. Together, these findings illustrate why visibility and ownership matter; they do not establish a fixed amount every company will miss. McKinsey’s analysis of AI costs and FinOps
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams compare AI options?
Do not choose a model or deployment solely because its listed token price is lower. Compare the cost of completing the task to an acceptable standard, taking account of output quality, latency, reliability, risk, and operational burden. A cheaper option that needs more retries or human correction may not reduce the total cost. Conversely, a premium model may be unnecessary for a simpler task if a less costly option meets the required standard.
Consumption can change quickly, especially when AI is used in automated or reasoning-heavy workflows. 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 describe OpenAI’s customer base, not an industry-wide growth rate or a forecast for another company. OpenAI’s 2025 enterprise AI report
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →McKinsey notes that teams may default to premium models when trade-offs are unclear. Making those trade-offs explicit—and tracking cost and performance by task—helps turn model selection into a budget decision rather than a blanket assumption.
What do the survey findings say about budget control?
Survey results offer context, not a universal benchmark. SpendHound, a software vendor, reported in its 2026 vendor-produced research that 46% of surveyed finance and procurement leaders exceeded AI budgets in 2025, 81% expected budgets to rise in 2026, and 22% said no single person owned the AI budget. The report combines a stated survey with proprietary spend data, so its figures should be read in that context. SpendHound’s 2026 AI spend report
Gartner analyst Arunasree Cheparthi summarized the pressure on buyers: “Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes.” The practical implication is not to freeze AI spending, but to make decisions with a clearer view of what is being spent, what demand is driving it, and what the business is getting in return.
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