Saudi Arabia has made substantial investments in artificial intelligence (AI) infrastructure, cloud capacity and national digital transformation programmes, but the next phase of growth will depend on how effectively organisations translate those investments into operational capability.
According to Andrew Chen, vice-president and head of platform and products at Magna AI, the challenge facing organisations is no longer simply deploying compute resources, but creating an integrated environment where infrastructure, platforms, applications, security and governance operate as a cohesive system.
“Saudi Arabia has already made significant progress in building the infrastructure and compute capacity required for AI,” says Chen. “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 says many organisations remain focused on individual technology components, despite the fact that long-term success depends on connecting those components to real business and government processes.
“Compute provides the capacity, but platforms must make that capacity accessible and manageable across different workloads,” he says. “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.”
According to Chen, organisations often spend too much time managing multiple technologies and vendors rather than scaling AI capabilities across the enterprise.
“The challenge is less about adding another technology component and more about creating a common architecture and operating model across the full AI lifecycle. When these layers are fragmented, organisations spend significant time managing integrations and vendors instead of scaling intelligence,” he says.
From pilots to production
While enterprises across the Middle East have launched numerous AI pilot projects over the past two years, relatively few have successfully scaled those initiatives into production environments.
“A pilot proves that an AI use case can work in a controlled environment. Production is much more complex because organisations need clear ownership of outcomes, seamless integration with existing processes, strong data governance, robust security and compliance, and a clear framework for measuring performance over time,” says Chen.
“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”
Andrew Chen, Magna AI
“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.”
He adds that organisations must establish measurable business outcomes before deployment begins. “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.”
Sovereign AI moves beyond data residency
As governments across the Gulf place greater emphasis on sovereign AI strategies, Chen says the concept should be viewed more broadly than simply storing data within national borders.
“A production-ready sovereign AI environment goes well beyond keeping data or infrastructure within national borders,” he says. “Location matters, but sovereignty ultimately comes down to meaningful control across the AI lifecycle, including data, models, compute, applications, agents and operational decision-making.”
Organisations increasingly require the flexibility to deploy AI workloads across multiple environments, including sovereign clouds, private clouds, public clouds and on-premise infrastructure, depending on regulatory and operational requirements.
“In practice, organisations need clear control over workload placement, access to data and models, system security and governance, and ongoing compliance and auditability,” says Chen.
He also highlights the growing importance of maintaining control over the intelligence generated by AI systems. “Every decision, insight and model improvement contributes to an organisation’s long-term value and should remain under its control,” he says.
“Infrastructure can be acquired, and models can be licensed, but the expertise to operate, govern, secure and continuously improve AI has to be developed,” he adds. “That is what turns sovereign infrastructure into genuine sovereign capability.”
Governance becomes central to AI deployment
As organisations move from AI assistants and copilots towards increasingly autonomous AI agents, security and governance are becoming critical considerations.
“AI introduces risks that traditional controls were not designed to address, including model manipulation, training data poisoning, unauthorised inference access and supply chain vulnerabilities,” he says.
According to Chen, organisations must secure not only infrastructure and applications, but also models, data pipelines, application programming interfaces and inference environments. “This requires an AI-native approach that treats security and governance as part of architecture rather than as functions added after deployment,” he says.
Runtime guardrails, continuous monitoring, policy enforcement and appropriate human oversight are essential to keep AI agents within defined boundaries and prevent unsafe or unauthorised actions Andrew Chen, Magna AI
The need for governance becomes even more important as AI systems gain greater autonomy. “The more independently a system can act, the stronger the requirements for visibility, accountability and control,” says Chen. “Runtime guardrails, continuous monitoring, policy enforcement and appropriate human oversight are essential to keep agents within defined boundaries and prevent unsafe or unauthorised actions.”
Supporting Vision 2030 ambitions
Chen says Saudi Arabia’s importance as an AI market reflects the Kingdom’s ambition to build long-term national capability rather than pursue isolated AI projects.
“Saudi Arabia is moving beyond AI experimentation towards building AI as a long-term national capability,” he says. “Vision 2030 has created a clear direction around economic diversification, digital transformation and innovation, supported by significant investment in infrastructure, regulatory frameworks and sectors where AI can generate meaningful economic and operational impact.”
Looking ahead, Chen believes the next stage of AI development in the Kingdom will focus on helping organisations convert infrastructure investments into measurable outcomes. “Ultimately, the goal is to translate investments in AI infrastructure into locally operated intelligence, measurable outcomes and sustainable capability,” he says.
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