Gartner published its worldwide AI spending forecast on May 19, 2026, and the numbers are striking. Global AI investment is on track to reach $2.59 trillion this year — a 47% increase over 2025 — making it one of the fastest periods of technology spending growth in recorded history.

But buried inside those headline numbers is a more interesting story for business owners: the money has mostly been flowing from tech companies and hyperscalers. 2026 is when mainstream enterprises finally start opening their wallets.

What the Numbers Actually Say

The $2.59 trillion total breaks down in ways that matter:

AI infrastructure dominates. More than 45% of all AI spending goes toward infrastructure — the servers, chips, and compute that power AI models. Spending on AI-optimized servers alone is expected to triple over the next five years. The data center build-out is not slowing down.

AI agent software is the growth segment to watch. Gartner forecasts AI agent software spending will hit $206.5 billion in 2026 and jump to $376.3 billion in 2027. That 82% single-year jump signals that businesses are moving from experimenting with AI to actually deploying agents that do work.

Enterprise adoption is still early. Only 17% of organisations have deployed AI agents to date. But more than 60% say they expect to within the next two years. That gap — between intent and deployment — is where the real story lives.

Enterprise apps are getting AI fast. Gartner predicts 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% at the start of the year. If that plays out, every major software vendor your team uses will embed some form of AI agent before the year is out.

The Risk Nobody’s Talking About

There is a cautionary note in the same Gartner data that deserves equal attention: over 40% of agentic AI projects will be canceled by the end of 2027.

The reasons cited are escalating costs, unclear business value, and inadequate risk controls. In other words, companies are spending the money, but many are not getting the returns. They are deploying AI without the data foundations, governance structures, or workforce readiness to make it stick.

Gartner’s own research has shown that organisations with successful AI initiatives invest up to four times more in data and analytics foundations than their peers. You cannot bolt AI onto a broken data operation and expect results.

Why 2026 Is Different

For the past two years, AI spending was largely a story about OpenAI, Microsoft, Google, Amazon, and the infrastructure companies supplying them. The enterprise world watched, ran pilots, and waited.

2026 is the year Gartner calls the “inflection point” — the moment when real businesses, not just tech companies, start committing serious capital to AI transformation. The drivers include:

• Agentic AI moving from experimental to production-ready
• Clearer ROI benchmarks emerging from early adopters
• Competitive pressure intensifying as peers deploy and report gains
• AI being embedded directly into the software already in use (CRMs, ERPs, analytics platforms)

For business owners who have been watching from the sideline, this data is both validation and a warning. The wave is here. But spending on AI is not the same as succeeding with AI.

What This Means for Business

The Gartner forecast tells you the market is moving fast. What it does not tell you is how to be in the 60% that succeeds rather than the 40% that cancels.

A few patterns separate the organisations getting returns:

They start with clear business problems, not technology. Successful deployments begin with a specific operational pain point — not with “we need to adopt AI.”

They invest in data before agents. AI agents are only as reliable as the data they can access. Organisations skipping this step find their agents producing wrong outputs or requiring constant human correction.

They build internal capability alongside external deployment. Buying an AI solution and hiring a vendor is not enough. Teams need to understand what AI can and cannot do, how to evaluate outputs, and how to adapt processes.

They treat AI agents like new employees, not new software. Agents need onboarding, guardrails, evaluation criteria, and someone accountable for their outputs. The companies treating AI as a plug-and-play software upgrade are the ones failing.

At Enterprise DNA, both pillars of what we do — education through EDNA Learn and deployment through Omni services — are responses to exactly this gap. The spending is coming. The question is whether your organisation will be ready to make it count.

If you are deciding how to allocate AI investment this year, the $2.59 trillion market backdrop is useful context. But the more important number might be 40% — the share of projects that will not survive to 2028.

Build on solid foundations. Deploy where the business case is clear. Upskill the people who need to work alongside these systems. That is how you end up on the right side of that statistic.

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Gartner’s worldwide AI spending forecast was published May 19, 2026. The figures cited cover total AI-related spending globally, including infrastructure, software, and services.