From Digital Dreams to Physical Reality

Artificial intelligence (AI) continues to dominate boardroom conversations across the UK. In January this year Gov.UK announced that the UK AI sector attracted £200 million a day in private investment since July 2024. The scale of the ambition is clear, yet a critical disconnect is emerging. Organisations are investing heavily in AI talent, data strategies, and cutting-edge algorithms, yet many are discovering a fundamental truth: AI can’t run on aspiration alone.

This mismatch is born out by the numbers. According S&P Global Market Intelligence, the share of businesses scrapping most of their AI initiatives rose to 42%, up from 17% last year. The problem is rarely the model or the data scientists - it is the lack of physical environments capable of sustaining AI workloads. Running AI at scale requires a new breed of data centre, those that are purpose-built facilities engineered for high density, advanced cooling and resilience.

This is a major part of why AI initiatives might stall. Enterprises may have the right data scientists and the right models, but without access to an AI-ready data centre, they hit a physical barrier. Inadequate infrastructure – lack of appropriate hardware, software or cloud infrastructure to manage data and deploy AI models – is hindering AI progress. EY’s AI Pulse Survey found that 67% of senior business leaders admitted that their current infrastructure is actively slowing down their AI adoption. 

So what infrastructure is required to help AI initiatives be successful? 

AI workloads require specialist infrastructure that resides in AI-optimised data centres and run by data centre experts. These AI-optimised data centres house specific hardware such as graphics processing units (GPUs), tensor processing units (TPUs), and application-specific integrated circuits (ASICs) for their high parallel processing capabilities, which is essential for training and running complex AI models. Therefore, it is much more practical and cost effective to deploy AI applications in purpose-built facilities operated by data centre specialists.

AI readiness starts with construction

Much of the conversation about AI and infrastructure focuses on algorithms, processors, or cloud models. But AI readiness is grounded in the physical construction of the data centre itself. The design decisions made at build stage directly determine whether a facility can host and scale for AI.

In short, an AI-ready data centre is not just another building with racks. It is a purpose-engineered environment where design, materials and systems converge to support workloads that would overwhelm legacy facilities. 

The data centre is no longer a neutral warehouse for IT, it has become an optimisation layer in the AI value chain.

Three data centre priorities for AI-ready infrastructure:

1. High-density power readiness

AI racks are designed to handle processing power far beyond traditional levels. They have to have a high load capacity, ample space, configurable options, optimised airflow and cable management features. Because of this, they are much heavier than previous iterations and can weigh several tonnes. 

Many existing facilities were never built to accommodate these heavier racks, or the added weight of coolant and water required for liquid cooling,  so the base build of new AI facilities need to be enhanced. This means reinforced floors to cope with increased structural weight, modular halls, and layouts designed for density.

High-density workloads also require not just more power, but smarter power. Redundant distribution paths, advanced uninterruptible power supply systems (UPS), and the ability to segment power delivery by rack are critical. 

2. Precision cooling

AI applications demand extensive computational power to process vast amounts of data and perform complex tasks. This computational intensity translates into significant heat generation. 

Traditional air cooling can sometimes be insufficient when racks are running above 50 kilowatts. Liquid cooling technologies play a pivotal role in enhancing performance, increasing energy efficiency and improving the reliability of AI-centric operations. The adoption of advanced liquid cooling technologies not only optimises heat management and reuse, but it also contributes to reducing environmental impact by enhancing energy efficiency and enabling the integration of renewable energy sources into data centre operations. 

However, liquid cooling system are complex. Unlike air-based cooling systems, liquid cooling solutions require specialised components, such as cooling distribution units (CDU’s) which must be carefully integrated into data centre infrastructure. Designing a facility with liquid and immersion cooling systems capable of continuous performance means embedding pipework, pumps and containment systems into the architecture. 

Today, many data centres are transitioning from 100% air cooling to a hybrid model encompassing air and liquid-cooled solutions to improve efficiency, performance, and sustainability. 

3. Data and compute proximity 

A centrally located facility may deliver economies of scale, but it cannot overcome physics. If the compute is too far from the user, latency follows. And the tolerance for latency has collapsed in the AI era. AI models are only as good as the data they are trained and run on. Yet data often sits in multiple locations: across cloud platforms, enterprise systems and edge devices. When compute is physically distant from the data, latency rises and model accuracy suffers.

Internal teams may tolerate a half-second delay when running a batch job, but customers interacting with an AI-powered chatbot or real-time fraud system will not. Increasingly, enterprises are deploying AI applications in distributed data centres that bring compute closer to the user base.

For use cases such as autonomous vehicles, fraud detection or personalised services, milliseconds matter. If inference workloads are running in a facility hundreds of miles away from the data source, the user experience collapses. This is why proximity to data has become a performance factor in its own right.

The principle is straightforward - reduce physical distance and latency is reduced, and accuracy is improved. In practice, this reshapes the geography of data centre demand. Enterprises are beginning to choose data centre locations not just on cost or capacity, but on closeness to key datasets and user bases. Facilities near financial hubs, media clusters and population centres are becoming strategic assets.

Data centres as strategic AI enablers

AI is often framed around algorithms, skills and regulation. But without the physical foundations, none of these deliver value. Enterprises cannot run AI reliably or sustainably in legacy facilities. The future lies in AI-ready data centres - environments built for density, proximity and flexibility 

The organisations moving fastest on AI are those treating data centre strategy as core business strategy. They are aligning their AI objectives with facilities capable of delivering power, cooling and resilience at scale.

The differentiator in the AI race is no longer just data science or software talent. It is the readiness of the data centre behind it.