AI spending to surge past $2.7 trillion in 2026 as infrastructure costs dominate enterprise budgets

Gartner's latest analysis, released Wednesday, pegs this year's worldwide AI spending at $2.7 trillion, marking a 49.5% increase from the prior year as infrastructure requirements continue to accelerate. Technology providers account for the largest share of this outlay at 35%, channeling capital into AI-optimized servers, infrastructure-as-a-service offerings and semiconductor purchases essential for constructing, operating and scaling AI models and agents, according to John-David Lovelock, distinguished VP analyst at Gartner.

The amount of money that is going into AI infrastructure — the chips that are being made, the servers that are being bought, the data centers that are being built, the power, utilities, cooling that are going along with it — represent the largest infrastructure project humanity has ever undertaken

John-David Lovelock, Gartner

AI costs becoming unavoidable for enterprises

As AI expenditure climbs, the technology increasingly represents a mandatory expense for chief information officers, whether intentional or not. Some organizations deliberately procure AI-enabled products and services, while others encounter the technology embedded within tools they already use or are considering.

There's a bit of rebranding of the CIO's dollars to be AI. They're not intentionally going out and buying it, they just can't get out of the way easily.

John-David Lovelock, Gartner

Technology vendors are aggressively embedding AI functionality across their product portfolios. Salesforce unveiled AIforce on Tuesday, a layer designed to enable agentic enterprise capabilities by merging model intelligence with the company's proprietary data context to integrate with key business platforms. Broadcom, meanwhile, introduced the VMware Private AI Cloud earlier this month alongside VMware AI Factory, a software-defined foundation that streamlines infrastructure deployment automation.

The infrastructure underpinning this intelligence era is being constructed at scale, and enterprises are beginning to feel the financial weight. Memory expenses are climbing as vendors purchase AI-optimized servers in volume. Simultaneously, software licensing is becoming costlier as providers layer AI capabilities into existing offerings.

It is hitting the CIO in areas that they choose — I'm going to do an AI project, I'm going to look at agents and do automation, I'm going to look at large language models to improve quality, consistency, reliability and process — but they're also getting it in areas where they don't intend to or want to

John-David Lovelock, Gartner

Incremental adoption gaining ground

Despite the infrastructure buildout underway, CIOs retain flexibility in how they approach AI initiatives. Growing challenges in quantifying and proving AI's business impact are pushing many leaders toward smaller, more targeted deployments rather than sweeping enterprise-wide transformations, Lovelock noted.

building up the muscle memory and the skills for bringing AI from a concept — bringing AI from the lowest level of expectations — to building value

John-David Lovelock, Gartner