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IDC forecasts that Global 1,000 companies will underestimate AI infrastructure costs by 30% through 2027, according to CIO. That is a forecast for a defined group and time horizon—not a finding that every CIO will miss a budget by exactly 30%. The practical warning is that AI costs can grow in less predictable ways than conventional IT budgets anticipate, especially when projects expand beyond pilots.
How much will AI infrastructure really cost?
There is no single reliable figure for every AI deployment. The cost depends on the workload, how often people use it, the system architecture, where it runs, and the supporting services it needs. IDC’s 30% estimate, reported by CIO, is a warning about underestimation among Global 1,000 companies through 2027; the forecast methodology is not detailed in the reporting, so it should not be treated as a universal budget multiplier.
The uncertainty is partly about consumption. IDC vice president of infrastructure and operations research Jevin Jensen described the shift this way: “AI has moved technology spending from predictable consumption to probabilistic behavior. That means financial visibility must become continuous, not periodic.” A pilot’s usage assumptions may not hold once employees across more teams use a model, or when a system starts processing larger volumes of data.
A useful budget therefore estimates not just the cost to acquire or access a model, but the full operating environment around it—and updates those estimates as real usage emerges.
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Which costs are easy to miss?
Compute is only one part of the bill. IDC’s Jensen identifies GPUs, inference, networking, and tokens among the harder costs to predict. Security, governance, and employee training also belong in the plan. Depending on the workload and architecture, a project may incur costs in some categories far more heavily than others.
- Compute and inference: GPU capacity and the processing required to answer requests or run other AI workloads. Actual demand depends on usage and the work being performed.
- Tokens and model use: For services billed around model consumption, activity can change as adoption and the volume or complexity of requests change.
- Networking and data movement: Moving data between users, models, storage, and systems can add infrastructure requirements.
- Security and governance: Controls for protecting information and managing how AI is used need to be accounted for, not assumed to come free with model access.
- Training and workforce support: Employees and technical teams may need preparation and ongoing support as AI use broadens.
- Monitoring and validation: Logging, drift detection, monitoring, and validation take compute and operational effort. Cisco’s Nik Kale says that, in some environments, supporting systems can cost as much as—or more than—model inference; that is an attributed observation, not a universal ratio.
These costs can compound when a deployment moves from a limited test to a business service. The initial team may not be the only group that adopts the tool, and a production system may need more monitoring and controls than a pilot. Budget owners should make those assumptions explicit rather than extrapolating directly from a small trial.
What do current budget expectations suggest?
A Deloitte survey offers context for the scale of expected investment, but it measures expectations rather than actual future spending. Fielded in November and December 2025, the survey covered 515 US business and technology decision-makers at director level or above, across five industries and organizations with at least US$500 million in revenue. Deloitte published the results in March 2026.
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| Survey finding | What it means |
|---|---|
| 86% expected AI infrastructure budgets to increase over the following three years. | This describes respondents’ expectations, not a realized increase across all companies. |
| Respondents expected budgets to grow to more than three times their current level on average. | This is an average expectation from the surveyed group, not a spending forecast for an individual organization. |
| Large enterprises expected budgets to reach almost four times their current level. | This is a reported expectation for large enterprises, not a measured outcome. |
The findings reinforce the need to plan for expansion, but they do not tell an organization what its own AI program will cost. Its workload, adoption, architecture, and operating requirements determine the relevant budget.
How should you compare cloud, on-premises, and hybrid deployments?
There is no universally cheapest deployment model established by these sources. Cloud and on-premises arrangements have different cost patterns, and a hybrid design is an option rather than an automatic saving. Compare alternatives against the workload and its utilization pattern, including supporting systems and organizational requirements.
| Option | Cost pattern to examine | Questions for planning |
|---|---|---|
| Public cloud | Operating spend tied to consumption and the services used. | How will usage be measured and allocated? What happens to costs if adoption or processing demand grows? |
| On-premises | Capital and operating spend for hardware and the environment needed to run it. | What are the workload’s capacity needs, and what staffing, power, cooling, and supporting systems will be required? |
| Hybrid | A combination of cloud and on-premises costs, depending on which workloads run where. | Which workload belongs in each environment, and how will data movement, governance, and costs be managed across both? |
Some AI work may run on infrastructure an organization already has, while other workloads may require new capacity. A hardware purchase does not by itself solve budget uncertainty, and neither cloud nor on-premises should be presumed cheaper without workload-specific analysis.
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How do power and capacity affect the wider cost picture?
Enterprise budgets sit within a broader infrastructure environment. The International Energy Agency reported that data-center electricity demand rose 17% in 2025, and described supply-chain and grid-connection bottlenecks. The agency also noted that efficiency per AI task is improving even as overall use rises. These sector-level conditions do not quantify the effect on a particular company’s budget, but they make power, cooling, and capacity reasonable planning considerations—especially for organizations operating their own facilities or pursuing large deployments.
Deloitte also points to memory component costs, longer procurement times, potential wafer-cost increases, power and grid interconnections, and choices such as air versus liquid cooling. These are factors to assess for the relevant deployment, not fixed charges that apply to every AI project.
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- Set a workload-specific baseline. Define what the system will do, who is expected to use it, the data it will process, and the operating environment. Separate pilot assumptions from production expectations.
- Budget the whole service. Include compute, inference, networking, model consumption, security, governance, training, monitoring, logging, validation, and any required staffing or supporting systems.
- Track real consumption continuously. Measure usage and costs by workload or team, and make allocation visible to technology and finance leaders. Deloitte advises organizations to track and audit AI consumption; continuous visibility is also the budgeting approach Jensen advocates.
- Reforecast as adoption changes. Compare actual activity with the assumptions behind the budget. Update the forecast when new teams adopt the system, workloads change, or capacity and pricing conditions shift.
- Connect spend to business value. Review whether the workload’s expected return justifies its infrastructure and operating costs. Revisit the design if usage rises without a corresponding business case.
- Plan operational constraints alongside the bill. For each deployment option, account for skills, data governance, power, cooling, procurement, and capacity needs where they apply.
The point is not to add an arbitrary 30% buffer to every AI budget. It is to treat early estimates as assumptions, account for costs beyond model execution, and maintain a working forecast as deployments and usage evolve.
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