UAE’s push towards agentic AI raises stakes for governance and accountability
As the UAE accelerates plans to embed autonomous AI into government services, experts warn that governance frameworks must evolve from policy documents into operational controls that ensure transparency, accountability and trust
The UAE’s ambition to become a global leader in artificial intelligence (AI) is entering a new phase as government agencies move beyond AI experimentation towards large-scale deployment of autonomous, agentic systems.
While governments across the Gulf Cooperation Council (GCC) have been praised for their ambitious AI strategies and rapid adoption programmes, experts say the next challenge lies in operationalising governance frameworks to ensure AI systems remain accountable, transparent and secure.
According to Aben Pagar, head of digital risk consulting at Konexo, governments across the region have demonstrated exceptional commitment to AI-led transformation, but many organisations are still working to bridge the gap between strategy and execution.
“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 said. “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 noted 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,” said Pagar. “Each AI system should have a clearly designated owner, responsible for its performance, risks and compliance throughout its lifecycle.
“Decisions influenced by AI must be explainable and, where necessary, challengeable,” he said. “AI is no longer simply a tool supporting decisions – it is increasingly becoming part of the decision-making layer itself.”
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 said. “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.”
The UAE has a stated goal to transition a significant portion of government services to autonomous, agentic AI models within the next two years
Aben Pagar, Konexo
Agentic AI systems differ from traditional AI applications because they can autonomously perform tasks, coordinate workflows and make decisions within predefined boundaries, potentially transforming how governments deliver services, manage infrastructure and support policymaking.
Nasser Ali Khasawneh, global head of technology and digital sector and global co-head of AI at Eversheds Sutherland, said GCC governments have already laid important foundations by creating dedicated 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 said.
Khasawneh believes 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,” he said. “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 said. “At the same time, transparency and explainability will become more important as AI begins to play a more active role in decision-making.”
Data protection will increasingly form the backbone of AI governance, particularly around data quality, consent and cross-border considerations
Aben Pagar, Konexo
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,” said Pagar. “As a result, security is becoming an integral part of AI design and governance.”
He added 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,” said 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,” said 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.”
