Business AI spending is unusually hard to forecast when costs depend on how often employees use tools, purchases are spread across teams, or pilots become production workflows. But it is not impossible to manage: companies can improve control by counting the full cost of AI, assigning an owner, forecasting usage scenarios, and reviewing spending against measurable results.
Why AI budgets are harder to predict than ordinary software budgets
A fixed software subscription can often be forecast from the number of seats and the contract price. AI can add consumption-based charges: the bill may rise with the number and complexity of requests, the models selected, or how much work is routed through an AI-enabled process. When usage grows faster than expected, a pilot’s original estimate may no longer describe its production cost.
Purchasing can also be fragmented. Business units may buy separate tools, experiment with different model providers, or use AI features embedded in existing software. If finance cannot see those costs together, the company may not know its total commitment or who is accountable for it. McKinsey & Company says 20–30% of AI spending is often unaccounted for in its experience; that is a reported pattern, not a universal audited rate. McKinsey also found that only 20–25% of companies in its survey reported mature AI FinOps practices. McKinsey’s AI FinOps playbook
The difficulty is most acute when AI moves from experimentation into high-volume, agentic, or production workflows. OpenAI, for example, reported that message volume grew eightfold and API reasoning-token consumption per organization grew 320-fold year over year among its own enterprise customers. Those figures describe OpenAI’s customer base, not an industry-wide growth rate. OpenAI’s 2025 enterprise AI report
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What the spending figures do—and do not—tell you
Market forecasts and company surveys answer different questions. 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 growth in a defined market category; it is not a prediction of what any one company will spend, and it does not include every cost of adopting AI. Gartner says usage-driven model spending is sharpening attention to efficiency, cost control, and measurable outcomes. Gartner’s 2026 AI spending forecast
Survey responses show why plans can diverge from actual budgets, but samples and questions differ. EY’s fifth US AI Pulse Survey of 534 senior business leaders in selected industries found that 23% reported spending 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. These findings describe survey respondents, not all US businesses. EY’s 2026 US AI Pulse Survey
In a separate Gartner survey, 85% of 1,303 functional leaders at organizations with at least $50 million in fiscal-year 2025 revenue planned to increase AI spending in 2026, after allocating an average of 12% of their functional budgets to AI in 2025. Planned increases do not establish that every organization’s AI spending is rising or that the spending will deliver value. Gartner’s functional-leader survey
Count the full cost, not just the model bill
An API or token invoice is only one part of AI’s total cost of ownership. A more realistic budget includes the tools, infrastructure, people, and controls needed to make the system useful and safe in a real workflow.
- Access and software: subscriptions and seats, model licenses and API fees, and AI features included in other software.
- Usage and infrastructure: token consumption, cloud and GPU capacity, orchestration, and vector databases.
- Data and model work: data-pipeline development and maintenance, plus training or fine-tuning where applicable.
- People and oversight: staff time for implementation and training, human review of AI output, and governance.
IBM’s overview of AI total cost of ownership covers model, infrastructure, software, and data costs; Kiplinger also highlights staff training, output review, and governance. IBM’s AI total cost of ownership overview Kiplinger’s guide to budgeting for AI
A practical way to build an AI budget
1. Put one person in charge of the portfolio
Name an accountable owner and involve finance, IT, procurement, and the business leaders sponsoring AI work. The owner should be able to see spending across teams and distinguish approved production use from experimentation. In SpendHound’s vendor-produced 2026 benchmark, 22% of surveyed finance and procurement leaders said no single person owned the AI budget. Treat that as a finding from the vendor’s survey and proprietary data, not a universal measure. SpendHound’s 2026 AI spending report
2. Map current and planned use
Bring subscriptions, API and model usage, cloud capacity, embedded software features, experiments, data work, and staff time into one view. Where possible, tag each expense to a provider, model, team, workload, and stage of use. This makes a growing invoice easier to explain and helps prevent duplicate or hidden purchases from being mistaken for a single program’s cost.
3. Forecast a range, not a single number
Build baseline, expected, and high-use scenarios. For each, estimate how adoption, workflow volume, model choice or routing, and human review requirements could change the bill. Use observed consumption as it becomes available, then revise assumptions rather than treating a pilot estimate as a fixed annual budget. Kiplinger recommends a floor-and-ceiling range and rolling quarterly forecasts; McKinsey recommends scenario planning as adoption and model choices shift. These methods improve visibility but do not guarantee savings.
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4. Compare options by task cost and performance
Do not choose a model or deployment approach on token price alone. Compare the total cost of completing the task, output quality, latency, reliability, risk, and operational burden. A lower-cost model may require more retries or review; a premium model may be unnecessary for a simple task. McKinsey notes that teams can default to premium models when the trade-offs are unclear.
5. Connect spending to an outcome
For each use case, agree in advance what result matters—such as staff time saved, revenue, reduced risk, or better customer experience—and how it will be measured. Track that result alongside the attributable cost, then pause or redirect work when the evidence does not justify continued spending.
The value case deserves scrutiny. IBM reported that 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. This is not directly comparable with Gartner’s spending-intention survey: the organizations used different samples and measures. IBM’s report on the AI value gap
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When the “almost impossible” problem is most severe
Budgeting is not inherently impossible, but it becomes especially difficult when three conditions coincide: usage is consumption-based, buying decisions are scattered across departments, and experimental tools are becoming operational dependencies. Under those conditions, annual estimates can fall behind reality before the next budget cycle.
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Gartner analyst Arunasree Cheparthi said in July 2026: “Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes.” That scrutiny is useful: a budget should be a living forecast tied to observed use and results, rather than a one-time guess at an unfamiliar technology bill.
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