About 84 per cent of organisations in the Gulf Cooperation Council use AI in at least one business function, up from 62 per cent in 2023, according to a McKinsey survey conducted with the GCC Board Directors Institute. Yet only 11 per cent qualify as “value realisers”, meaning they have scaled AI and can attribute at least 5 per cent of earnings to the technology.
The findings show that adoption alone is proving a poor measure of AI maturity. Only 31 per cent of surveyed organisations said they had reached the stage where AI was being scaled or fully deployed across the enterprise, leaving more than two-thirds still short of broad operational use.
That gap is drawing attention to the data, cloud and governance foundations supporting AI systems. Accelera Digital Group, a technology company operating across Africa, the Middle East and Europe, says many organisations are attempting to build AI services on fragmented technology estates, where information remains trapped in legacy systems and governed inconsistently.
ADG describes the resulting burden as a “silent AI tax”: additional cost and complexity created when businesses have to reconcile disconnected datasets, duplicate information, repair poor-quality records or repeatedly integrate systems before AI tools can work reliably at scale.
Google Cloud research published this year found that 55 per cent of executives globally said their existing data systems actively prevented them from scaling agentic AI enterprise-wide. The research also found that AI systems could access, on average, only 45 per cent of enterprise data, highlighting operational limits created by fragmented information.
Google Cloud has separately said that trusted business context, rather than model capability alone, is becoming a key bottleneck for companies seeking to move AI from pilots into production. Its infrastructure research found that 83 per cent of organisations expected to require upgrades to support production-grade autonomous systems, while security, governance and operational controls remained leading obstacles.
For Gulf companies, those constraints are becoming more important as spending rises. McKinsey’s GCC study found that about three-quarters of respondents said senior executives were committed to scaling AI, while a large majority expected artificial intelligence budgets to increase. The challenge is shifting from securing executive support to proving that investment can generate dependable commercial outcomes at scale.
The McKinsey research also points to uneven capability inside organisations. Companies that capture value tend to combine clear business objectives with stronger technology architecture, data management, talent and change-management practices, rather than treating AI as isolated experiments.
Data quality is particularly important as businesses adopt generative and agentic systems that can retrieve information, make recommendations and execute tasks across applications. Weak controls can cause errors to spread more quickly because automated systems may draw from inconsistent records or act on information that has not been properly classified, validated or updated.
ADG argues that businesses should address those weaknesses before expanding deployments, establishing stronger governance standards and improving data quality and reducing fragmentation across cloud and legacy environments. Its position reflects a broader industry shift towards treating data infrastructure and governance as part of the AI investment itself rather than separate back-office technology work.
A 2025 GCC Board Directors Institute review found that only 5 per cent of respondents had a fully implemented AI adoption plan, despite growing board-level attention to the technology. The institute said limited expertise and skills gaps remained obstacles to effective oversight.
The latest Gulf adoption figures were drawn from McKinsey’s 2025 survey of senior executives and board members across the six GCC states. The survey defines value realisers more narrowly than companies that simply report productivity improvements, requiring scaled deployment and a measurable earnings contribution.
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