A few years ago, the question that separated ambitious banks from cautious ones was simply are you using artificial intelligence? Today, that question no longer means much. Across the UAE and the wider region, most banks are already experimenting with AI — in fraud detection, credit decisioning, customer service and beyond.
The harder question, and the one that will increasingly define competitive advantage, is whether a bank can deploy AI safely, responsibly and at scale. That is a governance challenge, not a technology one. The institutions that pull ahead will not be those with the most models in production, but those that can stand behind every decision their models make.
That shift begins with a definition.
Banks must decide, carefully and deliberately, what constitutes a “model” in the age of AI. The instinct to treat generative tools and machine learning systems as IT assets is understandable, but it’s also dangerous.
If an AI system influences a decision, a recommendation, a customer outcome or a risk assessment, it should be treated as a model — and governed with the same rigour banks already apply to credit risk, IFRS 9, stress testing and AML frameworks. AI is not merely a piece of technology; it is a decision-making capability, and capabilities of that weight demand oversight.
Regulators are reaching the same conclusion. The global conversation has moved quickly from how to encourage AI innovation to how to supervise it — through validation, explainability, accountability and continuous monitoring. The UAE is no exception.
The Artificial Intelligence, Digital Economy and Remote Work Applications Office recently launched its “Leading Generative AI Applications” guide, a clear signal that the national agenda is to embed these tools across sectors. But adoption guidance is only one half of a maturing ecosystem.
The other half is oversight, and banks should expect scrutiny of their AI governance to intensify. Those that prepare early will meet future expectations from a position of strength rather than scramble to catch up.
Preparation means confronting a new and unfamiliar risk landscape. Banks understand traditional risk categories well; AI introduces a different and evolving set. Hallucination, bias, weak explainability, data privacy exposure, cyber threats, third-party dependency and model drift are live risks that surface quietly and compound over time.
Identifying them is necessary but not sufficient. Each requires a clear mechanism for mitigation and ongoing monitoring, because an AI system that behaves responsibly at launch can degrade without anyone noticing — or fail in the opposite direction entirely. Where agents are nested within one another, a single fault can cascade through the chain, multiplying with exponential, domino-like speed until the system becomes uncontrollable and unmanageable.
This is where AI Model Risk Management (AI-MRM) becomes essential. Borrowing from the disciplines banks already know, AI-MRM provides a structured framework: a complete inventory of AI systems, a classification of their risk and materiality, independent validation, continuous monitoring and clear lines of oversight.
It turns ad hoc experimentation into a controlled, auditable capability. Crucially, it cannot live only within data-science teams. AI governance belongs on the board agenda and within the remit of executive management, alongside the other risks that determine an institution’s resilience.
None of this is an argument against AI. It is an argument for deserving the trust that AI-driven decisions increasingly demand.
The banks that win the next phase will not be defined by how much AI they have deployed, but by their ability to demonstrate robust governance, clear accountability and confidence in every automated decision they make.
