Every generation of technology produces a moment when enthusiasm outpaces understanding. Generative AI has arrived at precisely that moment - and the consequences for enterprises across the Gulf are becoming visible in ways that demand the attention of every board, every legal team, and every compliance officer in the region.

By Dr. Balamurugan Balusamy, Dean and Professor at the School of Engineering and IT, Manipal Academy of Higher Education (MAHE) Dubai

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The scale of the region’s AI ambition is significant. According to Microsoft’s AI Diffusion Report for Q1 2026, the UAE leads the world in AI adoption at 70.1 per cent of the working-age population, the first economy anywhere to cross the 70 per cent threshold. Saudi Arabia’s Cabinet formally designated 2026 as the Year of Artificial Intelligence, a declaration backed by $9.1 billion in AI sector funding and the world’s largest government data centre. Meanwhile, Gartner forecasts MENA IT spending to reach $169 billion driven heavily by demand for AI infrastructure.

These figures reflect genuine regional momentum. Yet periods of rapid technology adoption have historically also been periods of elevated governance risk, because the urgency to deploy consistently outpaces the discipline required to deploy responsibly.

Among the risks that have not received sufficient attention in this region is the issue of AI hallucinations as an emerging source of legal and reputational harm. When a generative AI system produces output that appears plausible but is factually incorrect - a fabricated legal citation, an inaccurate financial summary, a misrepresented compliance position - the damage does not remain contained within the model. It travels into decisions, documents, and company-wide communications. At that point, accountability falls squarely on the organization that deployed the system without adequate oversight.

Under the UAE’s existing Federal Civil Transactions Law, liability for harm caused by negligence already extends to AI systems whose design or oversight is deemed inadequate. Even before dedicated AI legislation is fully developed, existing civil liability, data protection and regulatory frameworks may already expose organisations to accountability where AI systems are deployed without adequate oversight. Research by Damien Charlotin of HEC Paris has documented over 1,348 hallucination cases in legal proceedings worldwide, a figure that has accelerated sharply in 2026. Gulf law firms are already confronting the reality that AI adoption is bringing data security and client confidentiality to the forefront of professional risk in ways the sector has not previously encountered. In financial services, the UAE Central Bank’s February 2026 Guidance Note on responsible AI applies to every licensed institution in the country, mandating board-level accountability for all AI systems, a comprehensive model inventory, and consumer opt-out rights for high-impact decisions. Any hallucinated output embedded in a credit decision or anti-money laundering workflow now carries direct supervisory exposure.

The Dubai International Financial Centre and Abu Dhabi Global Market have also issued their own active compliance guidance this year, while Saudi Data and Artificial Intelligence Authority continues accelerating the transition from voluntary ethics principles toward enforceable obligations. Across the Gulf, what was once soft law is hardening into regulatory expectation with tangible institutional consequence.

Ambition Alone Is Not Enough

The central challenge is the widening gap between adoption ambition and governance maturity. Deloitte’s research indicates that over 80 per cent of organisations in the UAE and Saudi Arabia report intense pressure to adopt AI, while nearly half simultaneously cite governance capability gaps as their primary barrier to scaling it safely. That combination is precisely where hallucination risk escalates from a technical inconvenience into an enterprise crisis.

Responsible AI deployment is fundamentally an institutional and human challenge. This distinction is central to how MAHE Dubai approaches AI education.

Students are not trained simply to build AI systems. They are trained to interrogate them critically. This includes assessing whether models are safe to deploy, whether their outputs can be trusted, and whether those overseeing it understand the ethical and societal implications of what they are authorising.

They are taught to evaluate scalability, work through constraints around security and user adoption, and engage seriously with the consequences of failure. That critical and ethically grounded approach represents the standard to which the next generation of technology professionals must be held to. It is equally the standard that enterprise AI governance frameworks should reflect.

The future workforce will require not only technical depth, but also the judgement to recognise when AI outputs should be questioned rather than trusted automatically. This is why AI education and enterprise AI governance must evolve together. Organisations deploying AI at scale need professionals who understand not only how intelligent systems function, but also where their limitations, risks, and ethical boundaries lie. As AI adoption accelerates across sectors, the ability to critically evaluate automated outputs, apply human oversight, and manage accountability will become just as important as technical capability itself.

In practical terms, this means treating AI governance as a board-level responsibility rather than an IT function. It means maintaining a formal inventory of every model in deployment. It means building hallucination monitoring into operational processes as continuous control, not a one-time evaluation. And it means assigning clear human accountability for every AI-assisted output that carries legal or reputational consequence. The DIFC’s Commissioner of Data Protection has already issued enforcement decisions on data-related AI obligations - a clear signal that the question of who authorised an AI-generated decision is no longer hypothetical. As the UAE AI market moves toward its projected 14 per cent contribution to GDP, the most important capability an organisation can develop is the institutional discipline to continuously evaluate, challenge, and reinvent how those tools are used - rather than scaling deployment while governance matures in the background.

Hallucinations will not be engineered away. Every model, however sophisticated, operates within probabilistic boundaries - error is inherent. The determinative question is whether an organisation has built the architecture to detect that error before it becomes a liability, and the culture to take accountability when it does not. The era of deploying AI without governance is over. The era of consequence has begun.

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