Governments worldwide are grappling with how to move artificial intelligence from scattered experiments into reliable public systems, and a recent study highlights the United Arab Emirates as a useful reference point in that shift. The research, conducted by INSEAD in collaboration with Yango Group, looks at how the UAE has begun treating AI less as a set of isolated tools and more as part of the basic operating fabric of public services.
Rather than focusing on flashy model capabilities, the paper emphasises institutional design: sustained leadership, process redesign at the departmental level, and procurement strategies that serve long-term goals. This approach stands in contrast to the common pattern seen elsewhere, where governments accumulate pilot projects that fail to scale because of fragmented ownership, unclear accountability, and governance structures that lag behind technical deployment.
The UAE’s experience over the past decade offers a practical lens. The National AI Strategy 2031 functions primarily as a coordination mechanism, helping align efforts across entities rather than dictating specific technologies. Platforms like TAMM have grown into AI-supported gateways for over a thousand government services, simplifying citizen interactions. In Dubai, an acceleration programme reviewed 183 potential applications and narrowed them to 15 targeted deployments in areas such as mobility, healthcare, logistics, and urban planning. Abu Dhabi, meanwhile, has prioritised foundational infrastructure, including sovereign cloud capacity, in pursuit of what officials describe as an “AI-native” government model.
These steps reflect a recognition that success depends less on access to the latest algorithms and more on addressing data silos, bridging policy and technical talent gaps, and building evaluation frameworks that keep pace with real-world use. The study compares the UAE with approaches in the UK, Singapore, the United States, the EU, and China, noting that similar technological resources produce uneven results depending on how initiatives are organised and overseen. Execution shortfalls, the research finds, rarely trace back to model accuracy; they more often stem from institutional friction.
Of course, questions remain about long-term sustainability. Concentrated leadership can accelerate decisions, yet it also risks over-centralisation if checks and balances are insufficient. Talent shortages at the intersection of technology and public policy persist globally, including in the Gulf. Procurement partnerships, while pragmatic, introduce dependencies that require careful management to preserve data sovereignty and public trust. The UAE’s progress, built amid rapid national development and significant investment, may not translate neatly to larger or less resourced administrations. Still, the emphasis on embedding AI into core operations rather than bolting it on offers a reminder that technology adoption is ultimately an organisational challenge.
For policymakers elsewhere, the takeaway is straightforward: treating AI as public infrastructure demands deliberate redesign of how governments work, not just what tools they buy. This includes rethinking procurement as a strategic function, clarifying accountability across agencies, and investing in the human and data foundations that determine whether AI delivers consistent public value or simply adds another layer of complexity. As more countries confront these realities, the UAE’s evolving model provides one set of tested choices worth examining closely, even if the full outcomes will take years to measure.
