State of Enterprise AI in 2026: Where We Actually Stand (and What’s Missing)

Deloitte just dropped the 2026 edition of their “State of AI in the Enterprise” report — seventh year running. Over 3,200 business and IT leaders surveyed across 24 countries. It’s probably the most reliable barometer we have for where enterprise AI actually stands, without the keynote hype or LinkedIn FOMO.

And the numbers are telling. Not because they’re bad, but because they show exactly where the bottlenecks are.

The global picture: lots of access, little real use

Globally, 60% of workers now have access to company-approved AI tools. That’s a 50% jump from the previous year, when less than 40% had access. That’s massive progress in very little time.

But having access isn’t the same as using it. Deloitte notes significant underutilization even among those who have the tools. Among non-technical workers, only 13% are enthusiastic and active AI users. 55% are open to it, 21% prefer to avoid it, and 4% actively distrust it.

Translation: you can hand everyone tools, but if you don’t change how they work, nothing happens. We broke this down with METR and MIT data — the copilot promise falls flat without process change.

The gap between ambition and execution

Here’s the number that sums it all up: only 1 in 4 organizations has taken more than 40% of their AI initiatives to production. Most say they expect to get there soon, but the funnel is still jammed at the proof-of-concept stage.

And it shows in the results. 74% of organizations want AI to boost revenue, but only 20% have actually seen it happen. PwC reports similar findings: only 12% of CEOs have seen both lower costs and higher revenue from AI.

This doesn’t mean AI doesn’t work. It means most companies haven’t implemented it well. There’s a massive difference between “we have a ChatGPT pilot” and “we’ve integrated AI into our business processes.”

The most concerning stat: 84% of companies haven’t redesigned a single job role to integrate AI. Companies are handing out new tools to do old work the old way. That doesn’t work. Jim Rowan, Deloitte US AI lead, puts it bluntly: “Organizations succeeding with AI don’t just invest in automation and algorithms — they invest in their people.”

A closer look: Spain as a case study

The Spain-specific data from Deloitte’s report is encouraging but nuanced — and frankly, the patterns mirror what we see across Europe:

• 85% of Spanish companies plan to increase AI investment this fiscal year
• A third plan increases of over 20%
• 68% identify efficiency and productivity as the top expected benefit
• 63% expect to increase revenue through AI
• 49% cite regulation and governance as the main barrier
• 70% show high concern about proprietary data usage
• 80% trust AI more than they did two years ago

Spain’s national statistics agency (ONTSI) reported 11.4% of companies with 10+ employees were using AI in 2024. More recent data puts Madrid at 30.1% adoption — nine points above the national average — concentrating 63% of national AI spending (over €360 million). For context on how this has evolved, we wrote an analysis of AI adoption in Spain in 2025 that serves as a baseline.

Javier Echániz, Deloitte Spain’s AI and Data lead, summarizes it: “If 2024 and 2025 were the years of exploration, 2026 will be the year of industrialization and real value.”

The challenge he flags: in Spain, scaling beyond 20% of proofs of concept is still proving difficult. The goal is to follow the US, where scaling ratios are higher.

The context: infrastructure is being built at unprecedented scale

To understand why 2026 might be different, look at what the hyperscalers are doing. This week alone:

• Amazon announced $200 billion in capex for 2026 (AI, chips, robotics, satellites). 50% more than 2025.
• Google projects $175-185 billion, nearly double last year.
• Meta is going with $115-135 billion.
• Microsoft is running at around $150 billion annualized.

Just the top four hyperscalers will spend over $630 billion this year on infrastructure. For perspective: that’s more than the GDP of Singapore or Israel.

What does this mean for businesses? The cloud infrastructure running your AI models and tools is getting more powerful, more accessible, and cheaper. Providers are competing fiercely to sell you compute. The bottleneck is no longer technology — it’s knowing what to do with it. In our analysis of AI maturity, we discussed exactly this shift: the focus is moving from technology to execution.

Autonomous agents: the next frontier

Deloitte dedicates an entire section to AI agents, and the numbers are clear: 74% of companies plan to deploy autonomous agents within two years, and 85% expect to customize them for their specific needs.

Use cases are already live: financial services with agents that capture meeting action items and automate follow-ups, airlines with agents handling rebookings, manufacturing with agents optimizing product development. If you want to dig deeper into real cases and expected ROI, we covered it in AI Agents for Enterprise: From Demo to ROI.

But — and this is critical — only 21% have a mature governance model for agents. We’re in the “I want one” phase without having thought through “how do I control it.” The companies doing best are the ones starting with low-risk use cases, building governance capabilities, and scaling deliberately. Not the ones rushing to automate everything overnight.

The agentic AI market is expected to grow from $8.5 billion in 2026 to $45 billion by 2030. But market size isn’t what matters. What matters is whether your company is ready to use them well or if it’s going to be another pilot gathering dust in a drawer.

Data sovereignty: from buzzword to buying criterion

Another relevant finding: 77% of companies now consider country of origin when choosing AI providers, and nearly 3 in 5 build their stack with local providers as a foundation. Deloitte reports 83% consider sovereign AI important for strategic planning.

This isn’t tech nationalism. It’s regulatory risk management. With the EU AI Act in motion and increasing attention to where data resides, European companies are starting to make technology decisions with a geopolitical lens. And it makes sense. We analyzed this dilemma in detail with the DeepSeek case — cost vs. sovereignty is not a trivial decision.

What businesses need in 2026

If you’re a data lead, analyst, or working at an SMB trying to do something real with AI, here’s what Deloitte — and reality — says actually works:

1. Redesign the work, not just hand out tools. 84% haven’t done it. If you do, you’re already ahead of the majority. This isn’t about “give everyone ChatGPT.” It’s about thinking: which tasks in this role can AI handle? How does the person’s role change? What new skills do they need?

2. Scale the pilots that work. The pilot-to-production funnel is the biggest bottleneck. If you have a pilot delivering results, the next step isn’t another pilot — it’s scaling the one you have. Sounds obvious, but most companies prefer the shine of something new over the hard work of integrating what already works.

3. Governance before speed. Especially with autonomous agents. If you don’t know who’s accountable when an agent makes a wrong call, you’re not ready to deploy agents. Better to build the governance framework first and scale after, than to slam the brakes after an incident.

4. Invest in people, not just licenses. The AI skills gap is the number one barrier according to Deloitte. And the most common response has been training — not redesigning roles or workflows. Training is fine as a first step, but if you only train without changing how people work, they learn and then go back to doing things the old way.

5. Measure real results. Efficiency, productivity, revenue. Not “we adopted AI.” The 20% that saw real revenue gains didn’t get there by using more tools. They got there by measuring, iterating, and being honest about what worked and what didn’t.

The Deloitte takeaway that matters

The report has one line that sums it all up: “Success with AI isn’t just about increasing efficiency or revenue. It’s about achieving strategic differentiation and lasting competitive advantage.”

And you don’t get that with tools. You get it with strategy, people, and the ability to execute. The technology is here. The infrastructure is being built at an unprecedented pace. What’s missing — especially in markets like Spain, but this applies broadly — is the organizational capacity to make use of it.

2026 has all the ingredients to be the year enterprise AI leaps from experimentation to execution. But only for companies that stop collecting pilots and start building real capabilities.

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February 2026

Sources: Deloitte “State of AI in the Enterprise 2026” (3,235 leaders, 24 countries), Deloitte Spain, ONTSI, TechCrunch, Reuters, The Register. Amazon and Google investment figures from their February 5, 2026 earnings calls.