Governments across the Middle East are investing heavily in artificial intelligence (AI) to transform public services and accelerate digital economies. Saudi Arabia’s National Strategy for Data and Artificial Intelligence Strategy (NSDAI), the UAE National AI Strategy 2031, and Qatar’s National AI Strategy all recognize AI as central to economic diversification and improved citizen experience. Yet, despite this momentum, the real transformation that government and businesses hope for remains elusive.
Many public sector AI projects rely on layering a large language model (LLM) on top of existing legacy data sets. This approach can deliver useful outcomes, like quickly creating summaries of hours of meeting transcripts and instantly summarize discussions or gathering key information from dense documents. These examples demonstrate AI’s potential to cut through information overload and support faster decision-making.
However, these use cases are limited and struggle when applied to large, disparate, and unstructured data sets spread across multiple departments. The underlying challenge remains —fragmented, incomplete, and siloed data. Without consistent data foundations, these proofs of concept cannot be scaled or integrated into critical services.
Challenges in data and AI deployment
The reason is straightforward: a lack of adequate preparations for the management, sharing, or securing of the data in a way that makes it ready for AI. As a result, projects are siloed, learnings fail to be shared, and systems remain too complex and inefficient to allow AI to flourish at scale. A comprehensive strategy for plans on how to unlock data from legacy systems to be used in AI is vital to address the challenge.
Data foundations are only part of the equation. The infrastructure required to process and analyze this data, from cloud platforms and high-performance computing (HPC) systems to networking and storage, must also evolve with growing demand and complexity. Then there is the skills gap, with a shortage of professionals trained to manage, interpret, and apply AI responsibly. Any data strategy must therefore be coupled with a skills strategy that builds AI literacy within the public and private sector and creates pathways to attract and retain top technology talent.
Towards a regional data strategy for AI
To unlock AI’s full potential, Middle Eastern governments need to evolve their national data eco-systems to ensure data is high-quality, secure, and accessible. This means establishing shared data standards and interoperability protocols across ministries and agencies, as well as creating governance frameworks that clarify data ownership and enable safe data sharing, while upholding sovereignty principles enshrined in national data laws.
Public sector data often involves sensitive personal or national information, making robust security guardrails non-negotiable. Data oversight should address not only how data is stored and shared, but also how it is protected against increasingly complex threats. By embedding strong data sovereignty and security principles into every stage of AI deployment, public sector organizations are afforded the confidence to embrace AI while managing risk effectively.
Governments across the region are already taking meaningful steps in this direction. Besides investing in high-performance computing (HPC) and cloud infrastructure to handle the scale and complexity of AI workloads, Saudi Arabia’s Saudi Data and AI Authority (SDAIA) and the National Data Management Office (NDMO) have introduced national data management controls and interoperability frameworks to standardize data exchange. Similarly, the UAE has set out clear parameters for personal data processing and data privacy and cross-border transfers per Federal Decree-Law on the Protection of Personal Data issued in 2021, aligning the country with international best practices. But given the recent explosion of GenAI and LLMs, ongoing updates, guidance and compliance toolkits will be vital to address real-world data flows, risk profiles, and operational constraints which are emerging with AI Proofs of Concept.
Yet technology and policy alone are not enough. The region’s long-term success will depend on people — equipping public sector professionals with the digital and analytical skills to use AI responsibly and effectively. Initiatives such as Saudi Arabia’s AI Talent Development Programme, which aims to train 1 million Saudis in AI, and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in the UAE, demonstrate how regional governments can build digital capabilities and sustain innovation. By upskilling and empowering public sector workers to recognize where AI is most effective, and where it isn’t, governments can ensure that AI becomes a practical, trusted, and integral tool for driving better outcomes for citizens.
Embedding AI into everyday public services
Embedding AI into the fabric of public services is the next critical step. From predictive healthcare systems and smart mobility to responsive government operations, AI can transform how citizens interact with public institutions. Achieving this requires more than pilot projects — it demands a regional data eco-system that unites infrastructure, governance, and human capability under a shared vision. By investing in workforce skills, modernized infrastructure, cross-department collaboration, and strong governance, the Middle East can move from isolated experiments to AI solutions that scale, deliver tangible benefits, and maintain public trust.
