AI Adoption Challenges for Organizations in the GCC

GCC governments and businesses are investing heavily in AI, but investment, tool adoption and business value are different measures. A McKinsey survey published in November 2025 of 139 senior executives and board directors across the six GCC countries found that only 31% of respondents considered their organizations to have reached scaled or fully deployed AI maturity. The challenge is not whether the region is interested in AI; it is how individual organizations turn promising use cases into governed, useful work.

The Gap Between AI Ambition and AI Results

Regional AI adoption is advancing, but the depth of implementation varies. In the McKinsey GCC executive and board survey published in 2025, 31% of respondents described AI as scaled or fully deployed, and 11% met the survey’s more demanding definition of an AI value realizer. These are responses from a specific survey sample, not a census of all Gulf organizations.

The practical gap can span use-case selection, data access, governance, workforce skills, operating processes and leadership decisions. PwC’s 2026 Middle East CEO survey reports strong AI uptake alongside uneven access to relevant data. A robust AI program needs both executive sponsorship and operating foundations; neither alone guarantees measurable outcomes.

Successful scaling requires several capabilities working together: business-led prioritization, suitable data and technology, responsible risk oversight, workforce adoption and evidence of value. These are useful assessment dimensions rather than proof that one leadership model determines performance across the GCC.

Challenge 1: Leadership AI Literacy Is Too Low

AI literacy is an important leadership responsibility, but it is not the only barrier to effective adoption. Senior teams need enough understanding of AI limitations, data requirements and failure modes to decide where it belongs, evaluate investment proposals and question unsupported performance claims. Technology, data and talent constraints also deserve separate assessment.

A delegation risk arises when executive sponsors leave the definition of business value entirely to a vendor or technology team. Technical specialists should lead on technical design, testing and feasibility, while business owners should define the decision or workflow being improved, the acceptable risk and the evidence required to justify a wider rollout.

Executive AI literacy matters because business leaders need to own investment priorities, decide what evidence of value is sufficient, and set accountability for risk. A technology team can provide technical expertise, but the business sponsor still needs to challenge use-case assumptions, confirm success measures and oversee adoption. Without these capabilities, even a technically sound pilot can fail to change operating decisions.

What to do about it: Invest in structured AI literacy for your entire senior leadership team – not one-day briefings or vendor presentations, but genuine programs that build the strategic understanding, governance capability, and decision-making frameworks that effective AI leadership requires. This is exactly what AI for Business Leaders (AIB-01) is designed to deliver.

Challenge 2: Data Readiness Is Overestimated

An AI use case depends on relevant, accessible and appropriately governed data. This remains a material constraint in GCC organizations: in PwC’s 2026 CEO survey, only 16% of GCC CEO respondents agreed that their most-used AI tools had access to all relevant documents and data. That finding concerns those surveyed CEOs and their AI tools; it should not be interpreted as a measurement of all regional databases.

Organizations can discover data problems during pilots: access controls, missing records, conflicting definitions, weak documentation or poor integration may make outputs unreliable. Where users cannot verify outputs or fit them into an established workflow, adoption can stall even when the underlying model appears technically impressive.

For multilingual operations, teams may need to evaluate Arabic-language input quality, document handling and translation fidelity alongside ordinary data-governance requirements. Government and regulated organizations should also assess information classifications, retention rules, cross-border transfer restrictions and permitted vendor access under their own applicable policies and laws.

What to do about it: Conduct an honest data readiness assessment before committing to AI deployment. Map where your data lives, in what form, at what quality level, and what would be required to make it AI-ready. This assessment often reveals that a data infrastructure investment needs to precede or accompany AI investment – not follow it.

Challenge 3: The Workforce Is Not Prepared

AI adoption changes workflows, judgment and supervision, not only software. Even technically sound tools may not produce value if employees are unclear about when to use them, how to verify answers, what to escalate and who remains accountable for decisions. Workforce training should be tied to actual tasks and role-specific safeguards.

Employees need clear guidance, practical opportunities to test tools and reassurance grounded in genuine policies rather than predictions about jobs. For GCC employers operating across languages and job families, a single generic AI workshop may be insufficient for executives, specialists and frontline roles whose data access and risks differ.

An effective capability plan separates executive oversight, manager-level application, specialist development and frontline operating guidance. Organizations should select the sequence from their use cases, legal requirements and workforce readiness rather than assume every employee needs identical tools or training hours.

What to do about it: Develop a layered AI workforce capability program that addresses different needs at different organizational levels. AI Tools for Managers and Professionals (AIB-02) is designed for the middle layer – the managers and professionals who need practical AI tool skills to work more effectively right now.

Challenge 4: Governance Frameworks Are Absent or Ineffective

AI governance concerns include privacy, security, intellectual property, accuracy, bias and appropriate human oversight. Not every risk applies equally to every use case, and voluntary guidelines should not be confused with binding legal obligations. Risk assessment should reflect the organization, sector, country and actual data involved.

Across the GCC, organizations should distinguish national AI strategies and ethics guidance from enforceable sector-specific, privacy, procurement and cybersecurity requirements. Frameworks published by public authorities can guide responsible design, but whether a particular requirement is legally binding depends on the jurisdiction, regulated activity and applicable rule. Obtain qualified local advice for legal interpretations.

Boards and executive committees overseeing material AI investments should be able to ask who owns a system’s outcomes, how risks are evaluated, where human intervention is required and what evidence supports claims of value. The appropriate board role depends on the organization’s governance structure; it should not replace management’s implementation responsibilities.

What to do about it: Establish an AI governance framework before scaling AI deployment. This means defining AI risk appetite, establishing an AI review process for significant deployments, creating clear accountability for AI outcomes, and building AI literacy at board level sufficient to enable meaningful oversight.

Challenge 5: Build vs Buy Decisions Are Made Poorly

The build-versus-buy choice should begin with the organization’s problem, data rights, security needs, integration requirements and capacity to maintain a solution. Some routine tasks can be served by existing commercial tools; differentiated, sensitive or highly integrated cases may justify custom work. Neither option is automatically cheaper, faster or safer for every organization.

Decision-makers should compare at least the total cost of ownership, vendor dependence, data portability, model performance on relevant tasks, localization needs, regulatory constraints, maintenance skills and the cost of switching later. A short controlled evaluation often reveals more than a broad claim that custom AI or off-the-shelf AI is always superior.

Vendors and internal technology teams contribute essential expertise, but their proposals should be evaluated against independently defined business outcomes. Sponsors can reduce decision bias by requesting alternatives, a pilot design, a realistic implementation budget and a clearly named owner for ongoing assurance.

What to do about it: Build sufficient AI literacy at leadership level to evaluate build vs buy decisions independently of technology team or vendor recommendations. Establish clear criteria for when custom AI development is genuinely justified versus when commercial tools should be the default.

The Path Forward: AI as a Leadership Priority

Neither a large AI budget nor a technically sophisticated model guarantees organizational value. The stronger decision is to prioritize use cases with credible benefits, fit-for-purpose data, clear risk ownership, trained users and a realistic path from limited testing to ordinary operations.

Leadership capability is one necessary ingredient, alongside technology, data quality, implementation discipline and workforce development. A practical programme turns AI ambition into a portfolio of accountable decisions: which problems to solve, what evidence to collect, which risks to accept, when to pause and whether a solution warrants wider deployment.