Jeff Jacob, Regional Business Development Lead – ISBG at ASUS, discusses how UAE organisations can move from AI pilots to deployment, with a focus on infrastructure, AI economics and long-term operations.
AI investment is growing across the UAE as the country advances its ambitions to become a global centre for artificial intelligence. Government initiatives include the Artificial Intelligence and Advanced Technology Council (AIATC) and Abu Dhabi’s Digital Strategy 2025–2027, which aims to establish the world’s first AI-native government. Businesses are also increasing their investments in AI. According to Cisco’s AI Readiness Index, nearly half of UAE organisations are dedicating 10% to 30% of their IT budgets to AI deployment.
As investment grows, an important question remains: how can businesses turn AI ambitions into practical, dependable applications?
While advances in AI models are attracting attention, the success of enterprise projects often depends on less visible factors, including data access, infrastructure design and operational responsibility. These decisions can determine whether an AI application delivers sustained business value or becomes an expensive experiment.
The economics of dependable AI
Infrastructure decisions should start with the intended workload. An internal chatbot that helps employees find documents has different requirements from an AI system supporting time-sensitive industrial operations.
Performance also depends on how well computing, storage and networking work together. Adding computing capacity alone will not solve performance issues if storage cannot supply data quickly enough or network bottlenecks restrict data flow. While cost per token is a useful technical metric, businesses also need to consider the overall cost of completing a task and the value it delivers.
Power consumption, cooling and automation are other important considerations, particularly as AI workloads increase in scale. High-density computing can place significant demands on data centre power and cooling systems. Direct liquid cooling can help manage thermal requirements in suitable deployments, but infrastructure choices should reflect the workload, facility design and operating conditions.
Data sovereignty and operational control
Where AI systems are deployed should depend on business requirements, security considerations and applicable regulations. For UAE businesses and government entities, data sovereignty can be an important factor in decisions about where information is stored, processed and accessed.
The UAE has also developed homegrown AI models, including the Falcon family from the Technology Innovation Institute (TII) and Jais, developed through a collaboration involving G42 and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Deploying such models for government or enterprise use requires organisations to assess hosting arrangements, data access, security and applicable regulatory requirements.
In-country infrastructure may be necessary for certain workloads, depending on sovereignty requirements and the sensitivity of the data involved. AI infrastructure must also be designed to accommodate computing demand, power and cooling requirements as workloads expand.
Governance becomes particularly important when AI systems can take actions rather than simply generate responses. Organisations need to define what these systems are authorised to do, when human intervention is required and who is accountable for their decisions and actions. Deployment models that keep sensitive data within an organisation’s controlled environment may be appropriate for some use cases, provided they meet the necessary security and operational requirements.
Planning for long-term AI operations
Deploying AI infrastructure is only one part of the process. Organisations must also account for integration with existing systems, ongoing maintenance, changing workloads and future expansion.
When evaluating infrastructure and technology partners, businesses should look beyond initial deployment costs. Compatibility between components, support arrangements, performance under expected workloads and the ability to scale can all affect long-term operating costs and reliability.
Organisations should also assess how well their infrastructure choices align with their AI strategy. Requirements may change as models evolve, data volumes increase and new applications move from pilot projects into production. Planning for these changes can help avoid costly redesigns and capacity constraints.
The UAE has built considerable momentum around AI adoption. The next challenge for businesses is to operate AI systems securely, economically and at scale. Organisations that plan for changing data requirements, growing workloads and appropriate human oversight will be better positioned to translate AI investment into lasting business value.
Disclaimer: This is a contributed opinion article by Jeff Jacob, Regional Business Development Lead – ISBG at ASUS. The views expressed are those of the author and do not necessarily reflect the views of Techitup. The article has been edited for length and readability.
