UK businesses are increasingly choosing private cloud infrastructure as the long-term home for their artificial intelligence (AI) systems, prioritising control and security over the convenience of public cloud services. This strategic shift reflects a growing understanding that AI is a unique, data-hungry, and deeply integrated technology that demands a more permanent and governed environment than transient cloud workloads.
Discussions among enterprises developing their own AI solutions have moved beyond initial model selection and use cases. The critical question now revolves around where these sophisticated systems should reside. Experts suggest that simply defaulting to public cloud environments due to familiarity or quick initial wins can lead to compounding issues over time, particularly as AI becomes embedded in core business processes and relies on sensitive, proprietary data.
Unlike many other cloud applications that are easily portable or can be scaled down, AI workloads are persistent and integral to operational performance. They improve through continuous use, drawing on an organisation's most valuable information. This creates significant dependencies concerning data location, access controls, model governance, and environmental evolution. Oliver Rowell, a solution architect at Xtravirt, highlighted the crucial question businesses must ask: "who has the keys to your data?" He warned that incorrect infrastructure choices can lead to escalating costs and complex governance challenges that are difficult to resolve once AI is deeply integrated.
A recent study, Broadcom's Private Cloud Outlook 2026, supports this trend, revealing that 56 percent of enterprises are either currently running or planning to run production AI inferencing in private cloud environments. This marks a notable change, as public cloud usage for similar workloads has decreased from 56 percent to 41 percent within a single year. This shift indicates a widespread recognition that production AI requires substantial compute power, low-latency connectivity, and robust governance capabilities, all of which are better managed within a controlled private environment.
The concept of data sovereignty remains a central concern for businesses handling sensitive information. Even when data is technically stored locally in a public cloud, foreign legal jurisdictions may still retain access rights through the cloud provider. This potential vulnerability is driving organisations to place a higher value on ownership, predictability, and direct control over their AI infrastructure. Will Rodbard, a master architect at Broadcom, emphasised that relinquishing control, even partially, means surrendering access to encryption keys or data, thereby compromising overall oversight and the ability to manage costs effectively. For businesses running critical AI workloads, full control extends beyond security and governance to provide greater visibility into resource consumption, leading to more predictable cost models as AI adoption expands.