The last few years have seen AI conversations dominated by the need for investment in hyperscale infrastructure as firms race to build ever larger training models. But as those conversations evolve, the emphasis is shifting to the next phase of AI adoption, focusing on the scaling of use cases and real-world value.
In line with this shift, organisations are looking beyond where AI is trained to the specifics of where it is actually used. Inference AI – the process of running AI models to generate real-time outputs and decisions – is rapidly becoming the critical lever for success in AI deployment.
AI: An Edge Challenge
While training a large language model typically happens centrally, AI value creation increasingly occurs at the point of interaction. Applications such as fraud detection, industrial automation, predictive maintenance, customer service, computer vision and AI-assisted decision making all depend on fast, localised inference. Latency is no longer just a technical metric; it directly impacts usability, operational efficiency and customer experience.
The narrative has suggested that public cloud and hyperscale environments would eventually absorb most enterprise workloads but, in fact, AI is exposing the limitations of this one-size-fits-all infrastructure model. Inference workloads often demand low-latency processing close to users and data sources, and far greater control over network infrastructure. And because the success of Inference AI is intrinsically linked to latency, we are seeing renewed interest in regional colocation and edge infrastructure, which is uniquely positioned to deliver this balance.
Latency: Only Part of the Picture
But while latency is critical, it’s by no means a panacea. Organisations are recognising that sending proprietary datasets and sensitive prompts into shared hyperscale AI environments introduces significant strategic risk.
The concept of a business’s AI applications – and indeed their intellectual property and competitive differentiation – being processed within ecosystems they do not fully control is not an IT issue to solve, but a board level concern.
Data Governance: Access and Control
Furthermore, sovereignty cannot simply be assumed because workloads operate within a country’s borders. As more questions emerge around where AI workloads reside, how data is governed, and who ultimately has operational access to intellectual property, demand for colocation and edge infrastructure is on the rise. The last 12 months in particular have seen growing demand from organisations looking to deploy AI inference workloads closer to where their data is generated and consumed.
Industry momentum reflects this change, with AI initiatives across Europe and the UK focusing heavily on regional infrastructure and inference capability rather than simply building centralised training clusters.
In fact research from Vanson Bourne demonstrated that 87% of enterprise CIOs plan to move away partially or fully from the public cloud over the next two years whilst Gartner predicts that organisations are set to migrate 20% of existing workloads from global public clouds to local or regional alternatives by the end of 2026.
Cost Certainty
In parallel, the economics around cost predictability are becoming a major driver for AI projects. Training frontier models requires vast capital investment and energy consumption, and AI workloads running continuously in public cloud environments can create significant operational expenditure uncertainty. Businesses increasingly want control and transparency, especially as AI transitions from experimentation into real-world business deployments.
Inference can often be distributed more efficiently across regional infrastructure environments, making edge and colocation models particularly attractive in securing stable, long-term AI operations without surrendering control of core assets or intellectual property.
Convergence for Control
There’s no question that we’re at an AI inflexion point.
True AI sovereignty increasingly depends on operational control across compute, networking and governance layers. Against this backdrop, AI infrastructure will almost certainly evolve into hybrid models where centralised training environments coexist with distributed inference at the edge. Organisations are therefore reassessing dependencies on hyperscale ecosystems and seeking infrastructure partners that can provide regional resilience, sovereign governance and private connectivity.
This is particularly important for regional economies. AI innovation cannot remain concentrated inside a few major hubs if nations want to unlock broad-based productivity growth. Regional AI infrastructure enables businesses across the UK to contribute to the AI economy without compromising sovereignty, performance or security. It democratises access to AI capability while strengthening national digital resilience.
A Distributed Future
The future of enterprise AI will not be built exclusively in a handful of massive facilities concentrated in global metro regions. It will increasingly depend on distributed, regional infrastructure capable of delivering sovereign, low-latency AI services where organisations actually operate.
The AI revolution will not only be defined by who builds the biggest models. Inference is paramount in determining the real-world success of AI, and as organisations place greater emphasis on sovereignty, security, latency and control, success will also be defined by who can deploy AI most securely, efficiently and locally.
This article first appeared in the latest edition of Data Centre & Network News.