AI across the Gulf
Industry deep dive: Public Sector

By Michael Caracache, Nizar Hneini and Mohamad Yamout

Public sector organizations across the Gulf Cooperation Council report high levels of AI ambition, driven by national strategies and public-sector mandates.

Investment momentum remains positive, with most organizations expecting AI budgets to increase: 91% of respondents expect AI budgets to increase in the coming year (43% significantly and 48% moderately). Scaling momentum aligns with 32% reporting AI as fully scaled, while 54% report that scaling is actively underway, stalled initiatives are reported by 12% of respondents, in line with the cross-industry average.

Where AI is creating value today

AI initiatives in government and public enterprises are prioritized across a range of drivers. Citizen and service experience is the most frequently cited value expectation, with 43% identifying it as a primary expected outcome over the next two years, consistent with national service delivery commitments across the GCC. Compliance and risk management (36%) and cost savings and productivity gains (36%) round out the near-term value picture.

End-user adoption: where scaling succeeds or stalls

36% of respondents report that business and technical team collaboration is dependent on leadership mandate. Only 19% report joint squads with shared KPIs, the collaboration model most associated with sustained adoption. When progress depends on leadership attention rather than embedded institutional structures, scaling is inherently fragile. AI initiatives that succeed under active leadership sponsorship stall when that attention shifts.

What is holding AI back from enterprise scale

Technology fragmentation is a scaling challenge for 35% of respondents. Risk aversion and cultural resistance to change are cited by 39%.

Data silos across departments are reported by 36%. This reflects the complexity of government data environments, where information ownership is often split among entities with different mandates and data governance regimes.

Leadership dependency compounds these challenges. When collaboration is driven by mandate rather than embedded in operating models, the friction from cultural resistance and data fragmentation becomes even harder to overcome.

The readiness gap is structural and not incidental

For public sector leaders, the question is no longer whether to act, but whether institutions can build the capability to deliver on commitments already made. With the Saudi Cabinet designating 2026 the Year of Artificial Intelligence, governmental entities across the region now operate under an explicit national mandate, not just a strategic aspiration.

Investment is committed, and scaling momentum is high. But high ambition scores and rising budgets cover a more uncomfortable truth: governmental entities are attempting to scale AI on foundations that were not built for it. The challenge is not a lack of commitment. It is a structural mismatch between the strategic intent and the maturity of the operational reality.

Three forces make this gap uniquely difficult to close in public sector:

First, governance complexity: AI systems deployed across ministries, agencies and public entities require layers of approval and inter-departmental coordination that few organizations have yet achieved.

Second, technology legacy: public sector IT architectures reflect decades of siloed procurement decisions that cannot be undone without modernization programs.

Third, change capacity: The agility required to embed AI at scale works against public sector structures designed for consistency and control, not agile and rapid adoption.

Without deliberately addressing all three, pilots will continue to stall before reaching the scale that national strategies demand.

The four imperatives

• 1. Resolve the technology foundation before expanding the portfolio.
  Scaling AI across fragmented government systems does not accelerate transformation; it amplifies existing dysfunction. Organizations that continue to layer new AI initiatives onto unresolved legacy architectures generate mounting technical legacy and erode confidence in AI's ability to deliver. The priority should be integrated data architecture and platform consolidation, treated as a strategic precondition rather than a background effort. Government-backed cloud ecosystems and national digital infrastructure programs across the GCC offer a practical pathway that public entities should align.

• 2. Redesign governance for cross-entity delivery.
  Current AI governance models were largely designed for single-organization oversight and are not suited to the inter-ministerial, cross-agency operating environment that defines public sector AI at scale. The result is approval complexity and unclear accountability. Leaders need joint governance frameworks, including shared standards, inter-agency coordination mechanisms and streamlined procurement pathways, that preserve accountability without sacrificing implementation speed. The sector's strong governance foundations, with only 3% reporting AI governance not systematically addressed, provide a platform to build from. The task is extending that rigor across organizational boundaries rather than within them.

• 3. Make adoption a design requirement, not a change management afterthought.
  Risk aversion and cultural resistance to change is cited by 39% of respondents, one of the most frequently reported scaling challenges in the sector. In organizations designed for consistency and control, AI adoption cannot be treated as a communications or training exercise. It requires embedding AI into workflows and decision processes from the outset, building genuine AI literacy across civil servants at all levels rather than concentrating it at leadership, and designing pilots explicitly for the organizational change they will require rather than retrofitting change management after deployment has already begun.

• 4. Close the gap to unlock sustained funding.
  A key predictors of stalled AI initiatives is the absence of a credible impact measurement framework. When government entities cannot demonstrate concrete value from pilots, budget holders revert to caution and programs lose momentum. This risk is particularly acute in the GCC, where AI budgets are often tied to national transformation agendas and subject to scrutiny against publicly committed targets.

Public sector leaders need to define performance measures before deployment begins, aligned with their context: reductions in processing times, improvement in citizen satisfaction, progress against Vision-linked KPIs, and efficiency gains in cross-agency coordination. In a region where national strategies set explicit targets, demonstrating measurable progress is essential to sustaining institutional and leadership support and funding.

Organizations that treat impact measurement as a core capability embedded in program design will be better positioned to convert pilot initiatives into sustained transformation programs.

To explore the full data and insights behind these trends, download the complete AI across the Gulf: From ambition to scalable impact report here