Feature: Edge AI


Data centres have become a dense, dynamic hive of activity thanks to AI


AI – the engine behind the current data centre evolution


By Aoife Foley, Professor and Chair in Net Zero, The University of Manchester


with electricity demand rising and sustainability legislation tightening. It is clear that digital infrastructure can no longer operate in isolation.


A


The AI adoption We should first define what AI means in this context. Rather than true autonomous intelligence, it currently manifests as optimisation algorithms, predictive modelling and automated


16 July/August 2026 www.electronicsworld.co.uk


rtificial intelligence (AI) and cloud computing are changing the way data centres operate. Te convergence is redefining the role of data centres within an interdependent and, to date, constrained infrastructure. Tis is happening against the backdrop of a global energy crisis,


control. While these tools excel at forecasting demand and streamlining efficiency, they are not truly independent. Instead, they function as bounded agents, limited by their underlying system design, specific objectives and the data available to them. Consequently, the hurdles we face are systemic and architectural, not just computational. Furthermore, integrating AI is complicated by its significant,


fluctuating energy demands, which oſten clash with the low- latency, high-bandwidth requirements of 5G and immersive services. True latency is determined by the entire network chain, from radio access and transport to the core network. Ultimately, achieving technical standards for speed depends less on where data is generated and more on the physical proximity of computing power to the end user. Tis change in requirements has led to the adoption of distributed architectures, an approach that combines regional and edge data centres with hyperscale cloud infrastructure. By moving computing closer to the user, latency is reduced, enabling applications like augmented reality, gaming and industrial control. Naturally, there are trade-offs to this approach. By adopting


a distributed architecture, the local energy demand increases. Tere is also the growing bandwidth demand. Multi-gigabit speeds are possible with 5G, placing greater pressure on data centre interconnects and backhaul networks. Rather than treating network and compute as separate layers, it is imperative that they be optimised together. Terefore, managing the edge is a delicate balancing act where


improving one metric oſten compromises another. To resolve this, developers are turning to AI-driven optimisation to manage latency vs bandwidth, prioritising speed whilst managing data volume; energy vs utilisation, balancing power consumption with server demand; and dynamic allocation, using machine learning to shiſt workloads, automate cooling and react to real-time energy price signals.


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