Saudi Arabia became a focal point of discussion in the “Dell Technologies World 2026” in Las Vegas this week about the next phase of artificial intelligence.
The question is no longer just about the size of investment in infrastructure or national capacity building, but about the difference the Kingdom can make in a global market transitioning from AI experimentation to its operational deployment within institutions.
In exclusive remarks to Asharq Al-Awsat, Michael Dell, Chairman and CEO of Dell Technologies, stated that what the company sees in Saudi Arabia is a “deep commitment to modernizing the Kingdom,” highlighting its significant energy resources and Dell’s collaboration with Humain and other companies in the Kingdom, in addition to a regional facility through which the company works to “aggregate these capabilities and build infrastructure for customers in the region.”
He added that every country today is going through a phase of re-understanding what the transition towards AI means, and how citizens and industries can be empowered to drive the economy forward. In the same session, Dell described Saudi Vision 2030 as “highly ambitious,” and the ambition for AI under this vision as “impressive.”
The Operation Test
From this point, the real discussion about Saudi Arabia and artificial intelligence begins. The narrative is no longer solely about the volume of investments, the speed of data center construction, or the number of announced national projects.
The challenge of the next test relates to how this national capability can be transformed into operational value within government entities, banks, hospitals, energy and telecommunications companies, and smart cities. It’s about how institutions move from AI experiments to systems that operate daily, on real data, within secure environments, and at a predictable cost.
Mohammed Amin, Senior Vice President for Central Eastern Europe, Middle East, Türkiye and Africa at Dell Technologies, places this transformation in a clear context.
In remarks to Asharq Al-Awsat on the sidelines of the conference, he states that the biggest barrier for institutions in Saudi Arabia and the Gulf as they transition from AI experimentation to production is not a single isolated factor, but an interconnected system encompassing infrastructure, governance, skills, cyber resilience, cost, and operating models.
However, he considers “data readiness” to be the primary obstacle. He adds: “Without a reliable and AI-ready data foundation, even the most advanced infrastructure is insufficient, and pilot projects falter before reaching production.”
Data Before the Model
This point appears fundamental to Dell’s assessment of the Saudi phase, as the company indicates that 96 percent of Saudi institutions now view AI as a key part of their business strategy, according to its research on the state of innovation and AI.
However, this indicator, despite its importance, does not mean that the path to production has become easy. Many institutions still operate through outdated and fragmented systems, distributed data, inconsistent governance, and limited access to reliable real-time data.
According to Amin, the fastest-advancing institutions are those that treat AI “not as a standalone tool, but as a transformation of the entire operating model.”
Here lies the difference between ambition and operational infrastructure. An institution that wants to use AI for customer service, risk management, predictive maintenance, or patient data analysis not only needs a robust model but also requires its data to be discoverable, governed, reliable, and usable by AI systems in a timely manner.
Amin defines AI-ready data as data that is “discoverable, governed, reliable, and usable by AI systems in real-time.” This definition transforms the discussion from a narrow technical question to an institutional one: Does the institution know where its data is, who can use it, and can it be trusted when fed into a model or intelligent agent?
Data from Sensitive Sectors
In the Saudi banking sector, this could mean linking customer, transaction, and risk data across different environments while maintaining compliance and governance. In hospitals, it involves securely organizing clinical and imaging data so that AI can support diagnosis or improve operations without compromising patient privacy. For government entities, it means unifying citizen and operational data while preserving sovereignty and security controls. As for energy companies, it might involve combining operational, sensor, and geographic data to support predictive maintenance and improve performance.
Dell states that updates to its Dell AI Data Platform specifically target this point, by indexing billions of files and linking them into governed data pipelines. The platform includes capabilities such as GPU-accelerated SQL analytics, achieving up to six times faster performance, and vector indexing up to 12 times faster.
These details might seem technical, but they actually determine the speed at which an institution transitions from a limited experiment to a widely operational AI service. The slower data is accessed or the less organized it is, the more the data pipelines themselves become an operational bottleneck. Amin notes that these capabilities help reduce response time, improve accuracy, and expand AI services with higher efficiency.
Local Operating Economics
As AI transitions to more sensitive and continuous workloads, another question emerges: when does private or institution-controlled infrastructure become more suitable than the public cloud? Amin does not present this as a stark choice between cloud and private infrastructure; he believes the public cloud remains important for experimentation, flexibility, and quick access to AI services. However, he adds that there comes a stage where controlled infrastructure becomes “strategically better,” especially when workloads involve sensitive national or financial data, or when response time requirements are critical.
This aligns with what Dell presented at the conference regarding Deskside Agentic AI, a solution aimed at running some AI agents locally on high-performance workstations, rather than relying entirely on cloud programming interfaces.
The company states that this solution can, in some cases, reach a break-even point with the cost of cloud programming interfaces within three months, and reduce spending by up to 87 percent within two years. Amin interprets these figures from a broader perspective, stating that technology managers in Saudi Arabia must evaluate the economics of AI “over its full lifecycle, not just by focusing on initial infrastructure costs.” The cloud might appear attractive at the outset, but it can become more expensive when running continuous generative or agentic workloads at the scale of a large enterprise.
Processor Efficiency
For Saudi Arabia, this issue is also linked to sectors with regulatory and sensitive natures. Amin acknowledges that the most realistic use cases today are those that deliver clear productive and operational value while maintaining manageable governance.
He points out that private assistants within institutions and workflow in regulated sectors represent a compelling starting point in the Kingdom, due to the strong focus on data security and sovereignty. He also believes that programming assistants are rapidly gaining momentum because they offer direct benefits to development teams.
The transition to production requires not only data and architecture but also infrastructure capable of handling high workload density. In heavy AI environments, processing units are insufficient if data does not move quickly between computing, storage, and applications.
Amin notes that the network design in PowerRack includes a switching capacity exceeding 800 terabits per second per rack, explaining that the practical meaning of this capacity is to eliminate data traffic bottlenecks between GPUs, storage, and applications. The longer GPUs wait for data, the lower the efficiency of infrastructure investment. Conversely, when data moves with low latency, training and inference operations become faster and more effective.
Cooling as a Strategic Factor
This discussion cannot be separated from cooling and power, as AI increases rack density and power requirements within data centers, making cooling a strategic, not just operational, factor.
Amin notes that the ability of Dell PowerCool C7000 to support facility water temperatures up to 40 degrees Celsius means that data centers can operate with higher efficiency in hot climates, reducing reliance on energy-intensive cooling.
In Saudi Arabia, where the government and private sector are investing in sovereign AI infrastructure, he believes that cooling “is no longer merely an operational issue,” but has become linked to scalability, energy efficiency, and long-term viability.
Data and Model Security
Cyber resilience is part of AI readiness; an intelligent system is not reliable if its data is corruptible, its models are exploitable, or its infrastructure is not recoverable. Amin points out that an AI system “is only as reliable as the data and models it operates on,” and a cyberattack that corrupts data or harms a model can have significant consequences.
Therefore, he believes that the maturity of cyber resilience will directly impact the extent to which institutions trust expanding their adoption of AI. Here, Dell offers tools like Cyber Detect, which it claims can detect data corruption resulting from ransomware attacks and accurately identify the last known clean version.
Openness and Sovereignty
With Dell’s expanded partnerships with Google, Hugging Face, OpenAI, Palantir, ServiceNow, and SpaceXAI, the company emphasizes that institutions do not want to tie their AI strategy to a single model, cloud platform, or infrastructure package.
This openness, in Amin’s view, gives institutions a “choice” and reduces vendor lock-in risks, allowing them to develop their capabilities as technology evolves. This is crucial in a fast-moving market like Saudi Arabia, where integration and interoperability can become strategic advantages in themselves.
When Mohamed Amin was asked about the Saudi sectors that would first require AI-ready infrastructure, he placed government, energy, telecommunications, finance, and smart cities at the forefront, due to the volume of their data, their national importance, and the operational value that AI can unlock.
These sectors are also most closely linked to sovereignty, compliance, and security requirements. Therefore, building a secure and scalable AI infrastructure appears not merely a technical upgrade, but part of institutions’ ability to transform the Vision’s ambitions into measurable daily operations.
Between Michael Dell’s response regarding Saudi Arabia and Mohamed Amin’s vision for the region, the picture of the next phase becomes clear. The Kingdom is not entering the AI race merely from the perspective of consumption or experimentation, but from the perspective of building institutional capability.
However, true capability will not be measured solely by the number of data centers or the volume of investment, but by institutions’ ability to prepare their data, choose where to run their workloads, manage costs, protect their models and data, and scale their use without losing control or governance.