Beyond AI Adoption

It is becoming difficult to describe the GCC simply as an emerging adopter of artificial intelligence. In the UAE, AI use is already approaching the characteristics of a general-purpose business technology.

Microsoft estimates that 70.1% of the UAE's working-age population was using AI by March 2026 — the highest measured rate in its national leaderboard. Once access becomes widespread, competitive advantage begins to move away from mere possession of the technology.

PwC reports that 70% of Middle East CEOs have a clearly defined AI roadmap, well above the 51% global benchmark. But only 22% say their most-used AI tools can access all relevant company documents and data.

Avanti defines the difference between those two reported indicators as the AI Execution Gap. It is deliberately narrow: it is not an AI maturity score and it does not mean that 48% of AI projects fail. It is a way of describing the distance between strategic readiness and information accessibility.

Sixteen per cent of GCC CEOs say their most-used AI tools have access to all relevant company documents and data, against 29% globally.

The finding is striking because it sits beside strong evidence of regional AI deployment. PwC reports extensive AI use in demand-generation functions — sales, marketing and customer service — among 43% of GCC CEOs, compared with 22% globally.

The most plausible reading is not that the GCC lacks appetite for AI. It is that rapid adoption is exposing the harder problems that appear when AI moves from isolated tasks toward enterprise operations: data silos, permissions, legacy systems, integration, governance and security.

Saudi Arabia provides the cleanest country-level illustration. Sixty-one per cent of CEOs report a clear AI roadmap, while only 14% say their most-used AI tools can access all relevant company data and documents. That 47-point difference should not be interpreted as a failure rate; it is better understood as evidence that strategic intent and enterprise information architecture are moving at different speeds.

Oman shows the same broad pattern, though the source wording requires caution. Seventy-two per cent of CEOs report a clear AI roadmap, while 34% report that AI tools can access company data. PwC's Oman page describes that 34% as AI tools being able to access company data, which is not identical to the regional measure of all relevant documents and data. The 38-point difference is therefore best treated as an indicative comparison, not a directly interchangeable country ranking.

Avanti uses the term wide-but-shallow AI to describe a stage of enterprise adoption in which AI appears across many functions but remains incompletely connected to the underlying systems, data and workflows of the organisation.

A business may use AI in customer communications, recruitment, marketing, document preparation and management reporting while those tools remain separated from CRM records, ERP data, contracts, procurement history, operating procedures and institutional knowledge.

This is not necessarily a weak stage. It can produce real productivity gains. But it becomes limiting when the organisation expects AI to perform multi-step work that depends on authoritative internal information.

In IBM research involving 500 senior UAE executives, 77% said their organisations had achieved significant operational productivity improvements using AI. Forty-four per cent expected returns on AI investment in under a year.

This matters because it prevents a simplistic conclusion. The execution gap is not evidence that AI has failed. Businesses can create value before their architecture is complete.

The obstacles identified by UAE executives are practical rather than futuristic: 67% cited inadequate data infrastructure or fragmentation, 65% security, privacy and ethical concerns, 64% IT complexity and 63% high upfront costs or reluctance to invest.

The lesson is important. Inside an enterprise, the smartest model is not automatically the most useful model. A sufficiently capable system that is securely connected to authoritative data and workflows can be more valuable than a frontier model operating without context.

Avanti defines the Enterprise AI Last Mile as the transition between possessing AI capabilities and embedding them into the operational fabric of a business.

IBM's 2026 UAE findings introduce a different problem: dependency. Eighty-eight per cent of surveyed UAE executives said switching their primary AI vendor or model would be difficult if required today. Nearly all — 96% — reported not fully understanding their AI dependencies across vendors, models and infrastructure.

These findings do not prove that vendor dependence has caused the productivity gains reported in IBM's separate 2025 study. But together they reveal a strategic tension: AI can become valuable before an organisation has complete visibility over what that value depends upon.

A conventional AI assistant may answer, summarise, draft or recommend. An agentic system may increasingly be expected to retrieve, decide, update, schedule, contact, route, purchase, escalate or execute.

That difference changes the architecture required. An agent cannot meaningfully automate procurement if it cannot access supplier information. It cannot manage customer interactions without customer history. It cannot operate safely if permissions are ambiguous.

The closer AI moves to execution, the more important identity, permissions, auditability, resilience, data sovereignty and model portability become.

The Gulf has spent several years proving that it is willing to adopt artificial intelligence. The evidence increasingly suggests that basic adoption is no longer the most interesting constraint.

What follows is harder: connecting AI to decades of organisational information; giving it enough access to be useful without giving it too much; modernising fragmented systems; defining permissions; and controlling dependency as AI moves closer to execution.

The next phase of GCC AI advantage is likely to be built through depth: infrastructure, integration, governance and control.

This publication is a synthesis. Avanti AI Labs did not conduct an original survey for it. It draws on credible, publicly available third-party research and applies its own analysis to the relationship between adoption, enterprise data access, integration and control.

Figures are reported against the population the original dataset describes. Middle East, Gulf Cooperation Council, Saudi Arabia, Oman and United Arab Emirates figures are not interchangeable and are labelled separately throughout. Where a number is produced by Avanti AI Labs rather than drawn directly from a source, it is identified as Avanti AI Labs analysis and the calculation is stated.

Source wording differs slightly between PwC country pages. Where a country measure is not identical to the regional measure, the comparison is marked indicative rather than presented as a ranking.