'The biggest shift happens when AI stops being a tool people occasionally use and becomes part of the operational fabric of the business': Why Arm is set to help power the next generation of AI workloads — and beyond

We speak to Arm's EVP of Cloud AI on what is really changing at the infrastructure layer

by · TechRadar

Features By Mike Moore Published 27 July 2026

(Image credit: ARM)

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As AI systems continue to evolve and advance, it's not just data center architecture being affected, but the wider infrastructure industry as a whole.

As Agentic AI workloads continue to scale, it is the CPU layer being particularly affected, with efficiency, coordination, and cost per task become increasingly important defining factors.

We spoke to Mohamed Awad, EVP, Cloud AI at Arm, to find out more.

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  • Gartner predicts that over 40% of agentic AI projects will be canceled by 2027 due to operationalization difficulties. How does Arm’s approach to "system-level" architecture specifically address the friction points, like cost and complexity, that are causing these cancellations?

I think the Gartner prediction reinforces something we’re hearing broadly across the industry. Most organizations aren’t struggling because today’s models aren’t capable enough. They’re struggling because operationalizing AI at scale is fundamentally different from demonstrating it in a pilot.

At the largest scale, we're seeing many of the same operational challenges play out in the physical world as well. Building AI infrastructure now means solving for power, supply chains, land, permitting, and deployment timelines just as much as advances in compute.

Moving from a handful of AI interactions to continuous, production-grade workflows requires a much more holistic view of the infrastructure stack – but it also requires organizations to rethink how AI interacts with their existing data and business processes. AI performs best where information is structured and workflows are well understood. As organizations extend AI into more complex enterprise environments, both the infrastructure and the operational environment have to evolve together.

That’s why we’ve focused on optimizing the system, from the CPU and platform through to the software ecosystem. As organizations move from pilots to production, success will increasingly depend on how efficiently their entire infrastructure works together – not just how capable any individual model or processor is.

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