The United Arab Emirates has long positioned itself as a frontrunner in artificial intelligence, but its latest ambition marks a significant escalation. The government is now moving beyond experimental AI projects toward large-scale deployment of autonomous, agentic systems that can make decisions and execute tasks without direct human intervention. This shift, while promising, raises profound questions about governance, accountability, and the frameworks needed to ensure these systems operate safely and ethically.

Across the Gulf Cooperation Council, governments have been praised for their ambitious AI strategies and rapid adoption programs. However, experts caution that the next challenge lies not in setting strategic goals but in embedding governance into daily operations. The gap between policy-level frameworks and practical implementation is becoming increasingly visible as AI moves from pilots to production.

The governance gap

Aben Pagar, head of digital risk consulting at Konexo, highlights that while GCC governments have made commendable progress in articulating AI strategies, many organizations still struggle to translate these into actionable controls. "In a fast-paced and rapidly evolving environment, governments across the GCC have made significant and commendable progress in setting ambitious AI strategies and investing in national capabilities," he says. "The region stands out globally for the clarity of its vision and the pace at which it is embracing AI as a core enabler of economic and societal transformation."

However, he notes that governance frameworks often remain stronger at the policy level than in day-to-day operational practice. "Many governance frameworks are well-articulated at a strategic level, but are still maturing in terms of how they are embedded into day-to-day operations and system design. This gap becomes more visible as governments move beyond pilots."

As AI adoption expands across public services, accountability is becoming a central concern. Experts argue that governance can no longer be treated as a periodic compliance exercise but must become a continuous operational function. "As AI becomes embedded in core public services, accountability needs to be clearly defined and operationalised from the outset," says Pagar. "Each AI system should have a clearly designated owner, responsible for its performance, risks and compliance throughout its lifecycle."

The move to agentic AI

The UAE's next step may prove even more transformative. According to Pagar, the country has set out an ambitious vision to transition a significant proportion of government services towards autonomous, agentic AI models over the next two years. "The UAE has clearly set the pace in terms of ambition, with a stated goal to transition a significant portion of government services to autonomous, agentic AI models within the next two years," he says. "This represents a fundamental shift from using AI as a support tool to positioning it as an active, decision-support and execution layer within government operations, capable of analysing data, making recommendations and carrying out actions in real time."

Agentic AI systems differ from traditional AI applications because they can autonomously perform tasks, coordinate workflows and make decisions within predefined boundaries. This capability has the potential to transform how governments deliver services, manage infrastructure and support policymaking. For example, such systems could automatically process visa applications, optimize traffic management, or even assist in regulatory enforcement.

Nasser Ali Khasawneh, global head of technology and digital sector at Eversheds Sutherland, notes that GCC countries have already laid important foundations by creating central AI authorities. "GCC countries have been amongst the first to create central AI bodies or ministries with a clearly defined remit over AI strategy," he says. These institutions will play an increasingly important role as governments seek to scale AI adoption while maintaining oversight. "As this transition unfolds, governance frameworks will need to evolve accordingly," Khasawneh adds. "The government is likely to maintain and expand on its structured, risk-based implementation models, with clearer expectations on how controls are applied in practice."

The importance of data governance

The shift towards agentic AI is also expected to elevate the importance of data governance. Pagar argues that data protection will become the foundation upon which future AI governance frameworks are built. "Data protection will increasingly form the backbone of AI governance, particularly around data quality, consent and cross-border considerations," he says. "At the same time, transparency and explainability will become more important as AI begins to play a more active role in decision-making."

Experts also point to growing concerns about cyber security, model governance and data sovereignty as key factors shaping AI adoption decisions. "Cyber risk now extends beyond infrastructure into the models themselves, including risks such as manipulation, misuse and unintended behaviour," says Pagar. "As a result, security is becoming an integral part of AI design and governance." He adds that organisations are increasingly focusing on explainability, validation and lifecycle management, while data residency requirements are influencing architecture choices, supplier selection and deployment models.

For public sector organisations looking to move AI projects from experimentation into production, governance must be embedded directly into systems and processes. "The key is to embed governance directly into the AI lifecycle rather than treating it as a separate compliance layer," says Pagar. "This starts with establishing clear visibility over where AI is being used across the organisation, followed by risk classification based on impact and sensitivity."

Looking ahead, experts believe the most significant public sector AI use cases will emerge in automated citizen services, regulatory supervision, intelligent case management and smart infrastructure operations. As governments pursue increasingly autonomous systems, the challenge will be less about identifying opportunities and more about implementing them responsibly.

"The ambition is clear," says Pagar. "However, the primary challenge is not identifying use cases, but scaling them responsibly. Integration with legacy systems, maintaining transparency in decision-making, and building public trust will all be critical."

Ultimately, success will depend on the ability to move from experimentation to disciplined, scalable execution. In this environment, effective AI governance becomes a key enabler, ensuring that innovation is delivered with confidence, accountability and long-term sustainability. The UAE's journey toward agentic AI will be closely watched as a model for how nations can balance technological advancement with the safeguards needed to protect citizens and maintain trust in digital public services.

Source: ComputerWeekly.com News