Saudi Arabia’s next AI challenge is turning infrastructure into capability

Saudi Arabia has already invested heavily in AI infrastructure, cloud capacity and national digital transformation programmes, but turning that infrastructure into operational capability will determine whether organisations can scale artificial intelligence beyond pilots, says Andrew Chen, vice-president and head of platform and products at Magna AI. Chen argues the technical capacity exists, but the next phase requires integrated operating models, clear ownership and sovereign control across the AI lifecycle to move projects from proof-of-concept into production.

“Saudi Arabia has already made significant progress in building the infrastructure and compute capacity required for AI,” Chen said. “The next phase is about turning that foundation into an integrated operating environment where compute, platforms, applications, security and governance work together rather than as separate layers.”

Chen warns many organisations remain focused on individual technology components instead of connecting those components to real business and government processes. He emphasises that compute capacity must be made accessible and manageable through platforms, and that applications must link AI to measurable outcomes rather than isolated use cases. “Compute provides the capacity, but platforms must make that capacity accessible and manageable across different workloads,” he said. “Applications then need to connect AI to real business and government processes where it can deliver measurable outcomes, rather than being restricted to isolated use cases.”

Organisations across the Middle East have launched numerous AI pilots over the past two years, but Chen says relatively few initiatives have reached production. He describes the shift from pilot to production as primarily an operating-model challenge: clear outcome ownership, seamless integration with existing processes, strong data governance, robust security and compliance, and frameworks for measuring performance over time.

• “A pilot proves that an AI use case can work in a controlled environment. Production is much more complex,” Chen said.
• “The biggest barrier is often not the technology itself, but the operating model around it. Data readiness, integration, governance, skills and security all matter, but they cannot be solved independently.”
• “AI should ultimately be measured by improvements such as productivity gained, costs reduced, decisions improved, or risks lowered, rather than by adoption or usage alone.”

On sovereign AI, Chen advocates a broader interpretation than simple data residency. He says sovereignty means “meaningful control across the AI lifecycle, including data, models, compute, applications, agents and operational decision-making.” Organisations will need flexibility to place workloads across sovereign clouds, private clouds, public clouds and on-premise infrastructure while maintaining control over workload placement, access to data and models, system security and auditability.

Security and governance must evolve to match AI’s growing autonomy. Chen points out risks such as model manipulation, training data poisoning, unauthorised inference access and supply chain vulnerabilities, and urges an AI-native approach that builds security and governance into architecture rather than treating them as afterthoughts. “Runtime guardrails, continuous monitoring, policy enforcement and appropriate human oversight are essential to keep agents within defined boundaries and prevent unsafe or unauthorised actions,” he said.

Outlook

Chen frames Saudi Arabia’s AI trajectory as a move from experimentation to sustained national capability aligned with Vision 2030. “Saudi Arabia is moving beyond AI experimentation towards building AI as a long-term national capability,” he said, noting that Vision 2030 provides direction on economic diversification and digital transformation backed by infrastructure and regulatory investment. The near-term challenge is practical: convert infrastructure spend into locally operated intelligence, measurable outcomes and sustainable capability by aligning platforms, applications, governance and operational ownership across the full AI lifecycle.