For the past several years, we have all been watching enterprises pour significant capital into AI infrastructure, whether it is data centers, compute clusters and sophisticated models. The ambition is real, and in many cases, so is the investment. Having worked at the intersection of technology and enterprise strategy for Huawei in this region, I find myself returning to one question: Are we building AI that actually works for the people and industries it is meant to serve?

Across the Middle East and Central Asia, AI infrastructure investment is accelerating, and that momentum is welcome. But investment in infrastructure is not the same as investment in outcomes. A data center optimized for high-density compute means little if the enterprise running it cannot point to a single workflow that has meaningfully improved. The risk is that we measure our progress in petaflops and parameter counts, while the harder question whether any of this is creating real value remains unasked.

Deploying AI for your enterprise means building a complete, integrated system, one that brings together compute, data, and model capability in service of a specific outcome. Consider what that looks like in practice: a teacher in a remote classroom gaining access to personalized educational tools; a doctor leveraging predictive diagnostics to save lives; a researcher accelerating the discovery of new materials or medicines. These scenarios represent the true standard against which every infrastructure decision should be measured.

The Huawei and Redington partnership holds a unique position in the AI ecosystem. What distinguishes our approach is that we address every layer of the stack and integrate them. Starting with Data Center Infrastructure: facility design, power architecture, precision cooling, and physical security, built for high-density AI workloads with energy efficiency at the core. At the compute layer, our Atlas 850E Server and Atlas 950 SuperPoD Cluster deliver the performance enterprises need for large-scale model training and real-time inference. Our AI-optimized storage layer eliminates the I/O bottlenecks that silently throttle AI performance in most deployments, a problem enterprise rarely diagnoses until it is costing them significantly. At the model and platform layer, we support over 30 plus foundation models natively, including DeepSeek, GPT, Llama, and GLM etc., with one-stop model deployment, management and migration tools. And at the application layer, we co-develop industry-specific and scenario-specific models with partners across finance, telecommunications, education, health, energy, and government. This is where the architecture earns its value and the question “What should AI do” receives a concrete answer.

A robust AI ecosystem is what converts infrastructure capability into industry outcomes, and that requires openness by design. Huawei’s reference architecture for intelligent transformation is built on the principles of openness, agility, and trustworthiness. It is why our ecosystem today includes over 1,200 partners — among them more than 30 hardware partners, alongside 1.8 million developers, and more than 2,500 industry-specific AI solutions. We are continuously deepening partnerships to ensure our customers develop their own industry-specific and scenario-based models, accelerating their path to measurable operational outcomes.

The Middle East and Central Asia region is building and setting its own terms for how intelligent AI transformation should work. My ask of every enterprise and technology leader is to hold the infrastructure accountable to the outcome. At Huawei, that is the standard we set for ourselves and the partnership we offer to the region.