Where Enterprise AI Budgets Are Going in 2026

Companies pay for AI licenses, computing capacity, data access, workflow changes, security, governance, and the staff needed to run AI. Enterprise AI budgets are likely to grow in 2026, while leaders face pressure to show better business results.

The next investment should address what is keeping the company from using AI more widely. Some companies need more computing capacity; others need better data access or controls. Spending before identifying the constraint can leave infrastructure underused or keep a promising pilot confined to one department.

AI infrastructure takes a large share

Computing capacity is the clearest area of rising AI investment. Gartner forecast worldwide AI spending of $2.59 trillion in 2026, including about $1.43 trillion for AI infrastructure. Those are market-wide figures that include spending by technology providers and cloud companies, not a tally of what enterprise customers themselves will spend.

Company surveys show a similar trend, but they report expected spending, not final spending. In Deloitte’s survey of U.S. companies with more than $500 million in annual revenue, 86% of respondents expected AI infrastructure budgets to rise over the next three years. On average, they expected those budgets to more than triple. Large companies anticipated an almost fourfold increase. These expectations point to demand for more capacity, but companies may not use all the capacity they pay for.

Infrastructure spending includes cloud computing, specialized chips, networking, and the work required to connect and operate them. Some workloads will run in public cloud services; others may run in company data centers or closer to the people and information they serve. Deloitte respondents expected to use more hybrid and distributed setups that span multiple locations. Using several locations makes costs, data access, and operations harder to manage.

Leaders need to decide which workloads require capacity set aside for them or specialized computing resources. A company with growing production traffic and a clear usage forecast has a stronger case for expanding capacity than one whose AI projects remain small pilots. Before signing a large commitment, leaders should estimate expected use, identify how they will allocate costs across business units, and account for power needs and hardware availability. Those checks help avoid paying for capacity that current workloads do not use.

Data integration makes pilots useful

Data and integration work is less visible than buying infrastructure, but it often determines whether AI reaches everyday operations. Integration means connecting an AI tool to the company’s data and software so it can support a real task, such as finding information across records or drafting a response inside an existing service workflow.

In a global survey by Publicis Sapient, enterprise AI decision-makers most often named integration across systems as a barrier to expanding AI use. Fragmented data and governance concerns also ranked highly. KPMG’s first-quarter 2026 survey likewise found that difficulty scaling use cases was a leading obstacle to demonstrating return on investment.

These findings support spending on data access and quality, software connections, and changes to how employees do their work. It does not establish a reliable dollar estimate for integration budgets. Still, the budget implication is concrete: if a pilot cannot reach the records or business system it needs, buying additional model capacity does not solve the problem.

For example, an AI tool that summarizes customer histories needs approved access to the relevant customer records and a place to deliver its summary. A company may need to resolve data permissions, connect the tool to the customer system, and decide who checks the result before acting on it. Those tasks add cost and time, but without them the tool remains separate from the work it is meant to support.

AI security protects wider deployment

Security spending covers both existing protections and controls designed for AI use. Companies need to decide what information an AI tool can access, how employees use approved services, and how to monitor AI features embedded in business software.

ETR’s Summer 2026 survey of 1,636 technology leaders found that 47% said AI spending was moderately or substantially over plan. Among organizations reallocating money to cover those overruns, 18% reported taking funds from security. ETR also found that security led software-category budget growth in its survey. Together, these findings suggest that companies continue to protect security budgets even as AI spending strains their plans.

A separate Gartner forecast estimated spending on “securing AI” at $2.835 billion worldwide in 2026, rising to $4.783 billion in 2027. That category includes AI application security, controls on AI use, governance platforms, and AI gateways that help manage traffic to AI services. It represents a defined market category, not the total cybersecurity budgets of enterprises.

The right controls depend on what the AI tool does and what information it handles. An internal tool that summarizes public documents does not pose the same risks as one that can access sensitive records or initiate actions in business systems. Leaders should involve security staff when they choose the use case and design the access controls, rather than treating security review as a final approval step. That reduces the chance that a deployment reaches a late-stage review only to require major changes.

AI governance becomes ongoing work

Governance is the set of rules, records, and oversight that help a company decide where AI is used and who is responsible for it. In practice, that work can include keeping an inventory of AI uses, documenting decisions, monitoring performance, reviewing higher-risk uses, and setting rules for human review.

Market estimates vary with the definition. Gartner estimated $492 million in AI governance spending worldwide for 2026 in one forecast. In a separate forecast, it estimated $275 million for the narrower category of AI governance platforms. Those figures describe different scopes and should not be combined or treated as the full cost of governance inside companies. Internal staff time and review work are not captured consistently in these market estimates.

KPMG’s first-quarter 2026 survey found that 91% of respondents said data security, privacy, and risk concerns would influence their AI strategies over the next six months. The survey also reported that most organizations required some form of human validation of agent outputs. These findings show that governance requires ongoing oversight of AI use, alongside policies and software.

Use standard controls for common, lower-risk uses and reserve more intensive review for uses with greater potential impact. For instance, a company might apply one set of access and logging rules to internal research tools, while requiring stronger review when AI supports decisions that affect customers or employees. The appropriate level depends on the use and the information involved; the available surveys do not establish one optimal governance budget or control model.

Workflows absorb more spending

As AI moves into business software and multistep processes, spending spreads beyond model access. A workflow is a sequence of tasks that people or software carry out to complete a business activity. Supporting AI in that workflow can require software licenses, data connections, security controls, monitoring, and changes to employees’ responsibilities.

Gartner forecast increased use of models embedded in software and AI agents across multiple workflows. An AI agent is software that can carry out a sequence of steps toward a task, rather than only responding to a single prompt. KPMG’s first-quarter survey reported that 54% of its respondents were deploying AI agents, including some that were coordinating multiple agents or using them across workflows. That figure applies to KPMG’s survey respondents, not all enterprises.

Putting AI into a workflow can make it more useful, while bringing more systems and decisions into play. An agent that routes information between business functions needs clear permissions, monitoring, and a way to handle errors. Budget plans that count only the model or software license leave out much of the cost of putting that agent into service.

Leaders should therefore estimate the full cost of a use case. That means including model or software fees, infrastructure, integration work, security review, governance, and the people who maintain the deployment. Without that view, a project can look inexpensive in one department while its costs accumulate across several budgets.

Budget pressure is already visible

Rising investment does not mean companies have firm control over AI costs. In ETR’s survey, nearly half of technology leaders said AI spending was moderately or substantially over plan. Organizations that covered overruns often moved money away from contractors and consultants, legacy infrastructure upgrades, or non-AI software licenses. Security was less often cut.

This is a real tradeoff: expanding AI budgets can delay other technology work that still matters. Deferring infrastructure modernization, for example, may make it harder to support new AI services later. Cutting software licenses may also displace tools employees rely on. Leaders should make those tradeoffs explicit rather than letting AI charges appear across separate budgets without a shared view.

Spending visibility is another problem. Gartner’s survey of organizations with at least $50 million in annual revenue found that 11% of respondents did not know what their function had spent on AI in 2025. A budget owner who cannot track model use, software fees, and implementation costs cannot reliably compare projects or spot overruns. Central tracking does not require every department to use the same AI tool, but it does require a consistent way to record spending and results.

Decide what to fund first

There is no universal order for infrastructure, integration, and controls. The best first investment depends on what is keeping a specific use case from working reliably again and again.

If production workloads are running short of computing capacity, infrastructure is a reasonable priority. If pilots cannot access the data and software they need, integration work is more likely to remove the immediate obstacle. If the business cannot approve wider use because it lacks clear access rules or oversight, fund those controls before adding more users. Gartner found that only 22% of surveyed organizations had successfully scaled AI across multiple business units or adopted an AI-first approach, while Publicis Sapient respondents pointed to integration and data fragmentation as common scaling barriers. Together, those findings argue for diagnosing the bottleneck before increasing capacity.

Leaders also need to decide whether to build AI internally or buy vendor products. ETR reported that sustained return on investment at scale was somewhat more common among respondents using in-house AI builds. A Morning Consult survey of U.S. business decision-makers found that large companies often prioritized output quality and security in platform decisions, and many planned to increase spending with current providers. These surveys ask different questions of different groups, so they do not establish a general winner. Build internally when the use case requires control or capabilities a vendor does not provide; buy when a product meets the need and reduces the burden of building and operating it.

Finally, tie funding to a result the business can measure. Gartner found that productivity was the leading target outcome, accounting for about 30% of functional AI spending on average. Productivity is a reasonable goal, but the share of spending directed toward it does not prove that those projects deliver the best returns. A project should have a baseline, such as time spent completing a task or the number of cases handled, and a clear owner who can decide whether to continue, adjust, or stop funding it.