UAE organizations lifted AI spending 105 per cent in a year but scored only 48 out of 100 on ServiceNow's Enterprise AI Maturity Index, a sign that the constraint is no longer budget or ambition but execution. The pattern mirrors a global one, echoed by separate worldwide studies from MIT and Alteryx, and the companies pulling ahead are defined by how well they operationalize AI, not how much they spend on it.

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Over the past decade, the United Arab Emirates has been positioning itself as one of the most AI-forward nations across the globe. It appointed the world's first minister for artificial intelligence in 2017, built a national strategy pointed at 2031, and founded the world's first university dedicated entirely to AI. Nor their ambition to succeed nor capital was seen as a barrier. What new findings did highlight, however, was that the hardest part of the AI era in the UAE came down to actual implementation. Organizations may have access to AI but their use of it is still minimal.

UAE-specific data from ServiceNow's Enterprise AI Maturity Index, released this week found that while organizations within the emirates increased spending by 105 per cent year on year, the index’s measure of AI maturity was a 48 out of 100. The score may be moving in the right direction, 13 points up from the previous year, but the gap between what companies are investing and what they are able to execute remains wide.

Constraints Beyond Money

The central argument in the report remains that this is not a spending problem. UAE organizations expect AI to account for close to a fifth of their total IT budgets by 2027. While companies have made real progress in areas of AI adoption pertaining to boardrooms, namely vision, strategy, and leadership, the real work remains in the fragmented and disconnected areas of the organization which still work in silos on top of which AI is being layered onto.

"The UAE remains one of the world's most ambitious AI markets. The government's long-term strategy and regulatory leadership have given organisations a genuine head start," said Saif Mashat, VP for Middle East and Africa at ServiceNow. "While UAE organisations have built the financial and strategic commitment to AI, the ones pulling ahead are moving from AI pilots to AI orchestration, connecting legacy systems, data, governance, and AI agents in one control tower. That's where enterprise-wide execution begins."

It is worth being clear about the source. This is vendor research, conducted by ThoughtLab on ServiceNow's behalf, and its conclusion points squarely at the category of product ServiceNow sells, an orchestration layer that connects systems, data and AI agents. The UAE sample is also small, 100 executives out of 4,500 surveyed across 19 countries. Those are reasons to read the prescription with care. They are not reasons to dismiss the diagnosis, because independent research has landed in almost exactly the same place.

A Global Divide, Not Just a Local One

The most cited data point in enterprise AI this year came from MIT, whose 2025 study of corporate AI found that roughly 95 percent of generative AI pilots delivered no measurable impact on profit and loss, despite an estimated $30 to $40 billion in enterprise spending. The researchers called it the GenAI Divide, and their explanation was not model quality or regulation. Similar to many other reports of the same nature, the problem was found to be integration. Generic tools impress in demos and stall in workflows because they do not connect to how businesses run.

The UAE numbers look less like a Gulf-specific weakness and more like a local instance of a worldwide condition. American enterprises, which dominate global AI spending, are wrestling with the same divide between adoption and transformation, and the pressure to show a measurable return has intensified as budgets have grown. This is also why the finding matters to readers well beyond the Emirates. ServiceNow is a US company, the maturity problem it is describing is one every large enterprise faces, and the UAE simply offers an unusually clean case study of what happens when abundant capital meets operational friction.

The convergence does not stop with MIT. A separate global survey of 1,400 IT leaders, published this month by the analytics company Alteryx and conducted by the research firm Coleman Parkes, reached strikingly similar conclusions from a different direction. It found that 80 percent of organizations expect AI spending to rise over the next two years, and that 69 percent already report moderate or significant returns, yet 53 percent say they struggle to translate business context, the rules, definitions and operational knowledge that govern how a company actually runs, into the systems and workflows AI depends on. Only 18 percent said business users have fully self-service access to the cloud data those systems need. Alteryx sells tooling aimed squarely at that problem, so its framing is no more disinterested than ServiceNow's. What is harder to wave away is the repetition. Three separate studies, one academic and two commercial, drawn from different samples and methods, keep describing the same gap between what companies spend on AI and what they can actually put to work.

“Our research highlights a growing gap between AI ambition and enterprise-scale execution,” said Andy MacMillan, CEO of Alteryx. “Organizations have proven they're willing to invest in AI, and many are already seeing returns. But scaling AI requires more than better models. It requires making the business knowledge people use every day available to the systems making decisions.”

The Agentic AI Paradox

Nowhere is that friction clearer than in the technology everyone spent the year talking about. Agentic AI, systems that can take actions on their own rather than just generate text, became the defining enterprise trend of 2026. The report finds that more than half of UAE organizations, 57 percent, have implemented it in some form. But only 7 percent have used it to build genuinely autonomous workflows. In the overwhelming majority of cases, AI is still making individual employees a little faster rather than changing how the business itself operates.

That gap between deploying agents and trusting them to run a process unsupervised is not necessarily a failure of nerve. Autonomous systems demand governance, auditability, and operational foundations that most organizations have not yet built, and moving cautiously while those foundations are laid is a defensible stance.

The Unglamorous Blockers

The report identifies three foundations that separate the leaders from the rest, and none of them are exciting. The first is legacy technology where only 14 percent of UAE organizations have replaced legacy systems with integrated platforms, which means AI is most often deployed across a patchwork rather than a single operational backbone.

The second is data. More than three-quarters of executives, 77 percent, cite inadequate data accuracy, access, and management as a major barrier to adoption, a reminder that AI ambition tends to collide with the unresolved data problems a company has been deferring for years. The third is governance. Just 16 percent have put AI testing, auditing and risk management processes in place, a thin foundation on which to scale autonomous systems safely.

These are not the topics that fill keynote stages, but they are the ones that decide whether AI produces a return. The organizations that treat data modernization, integration, and governance as prerequisites rather than afterthoughts are the ones crossing from experimentation into execution.

From Spending to Operationalizing

Organizations at the highest levels of AI maturity report an average return on their AI investment of 160 percent, rising to a projected 194 percent within two years. The report finds they are also 5.6 times more productive, 2.7 times more successful at scaling AI and 2.6 times more effective at managing risk. Whatever discount one applies for the source, the direction is consistent with the independent MIT findings: the winners are defined less by how much they spend than by how completely they rebuild around the technology.

"The organisations pulling ahead are no longer distinguished by how much they spend on AI, but by how effectively they operationalise it. This requires moving from point solutions to unified, orchestrated platforms," said Mashat. "Strong governance, connected data and orchestrated workflows are what translate investment into business outcomes. Together, these give organisations the confidence to scale AI, manage risk and generate measurable returns. The UAE government has already created many of the conditions for AI leadership. The challenge for enterprises now is to bring that same discipline and consistency into their own organisations."

That last point captures the real shape of the story. The UAE's national push gave its companies a head start that few markets can match, in infrastructure, regulation, and political will. The next phase of that leadership will not be won with bigger budgets or bolder strategies, both of which the Emirates already has in abundance. It will be won in the far less visible work of connecting systems, cleaning data, and governing AI well enough to let it run. The country has proved it can commit to AI. The open question, the same one facing enterprises from Dubai to Dallas, is whether it can operationalize it.

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