Today, we’re joined by Maryanne Baines, a leading authority in cloud technology with deep expertise in evaluating cloud providers and their applications across industries. As UK businesses stand at a critical juncture, with 2026 emerging as a pivotal year for digital strategy, we’ll explore the seismic shifts underway. We will delve into the powerful trend of workload repatriation, the urgent need for enhanced cyber resilience in the face of new threats, and how evolving government policies are reshaping the data center landscape. Furthermore, we’ll cut through the AI hype to understand the real-world infrastructure demands of sovereign AI and the rise of regional edge computing as a cornerstone of the UK’s future digital economy.
With a staggering 87% of UK businesses reportedly planning to repatriate workloads, what are the specific cost, compliance, and control benefits they are truly seeking beyond the headlines? Could you walk us through a practical example of how an organization might transition a critical workload from a global hyperscaler to a more controlled hybrid model?
It’s a powerful shift we’re seeing, moving from a “cloud-first” mantra to a “workload-first” strategy. The benefits are very tangible. On the cost front, businesses grew tired of the unpredictable, often escalating bills from hyperscalers. They are seeking the financial stability of a private cloud or colocation model with more foreseeable expenses. For compliance, the conversation around data sovereignty has become a boardroom issue; businesses need to guarantee their data resides under UK jurisdiction. And control is about performance and visibility—gaining direct oversight of their infrastructure instead of being just another customer in a massive global machine.
A classic transition would involve a retail company. Let’s say their core customer database and e-commerce transaction engine are running on a major US-based public cloud. First, they conduct an audit and realize that not only are the costs becoming unmanageable, but their customers are asking tougher questions about where their personal data is stored. They then partner with a UK-based digital edge infrastructure provider. In a phased approach, they migrate the sensitive customer database to a private cloud within that provider’s UK data center. The public-facing, less sensitive web front-end might remain on the hyperscaler to handle traffic spikes. This creates a hybrid model, giving them sovereign control over their most critical asset while still leveraging the scalability of the public cloud where it makes sense.
High-profile security breaches in 2025 shifted the focus from pure prevention to the necessity of rapid recovery. Beyond standard backup, what specific methods or technologies should businesses implement to ensure they can get back on their feet quickly? And what key metrics would define a successful recovery for a mid-sized enterprise?
The mindset has fundamentally changed from “if” a breach happens to “when.” Standard backup is no longer enough because sophisticated attacks can compromise backups themselves. Businesses must now invest in true resilience. This means implementing robust Disaster Recovery (DR) solutions, often as a managed service, that can failover critical systems to a secondary site almost instantaneously. We’re also seeing a greater emphasis on creating immutable, or unchangeable, copies of data and air-gapped backups that are physically disconnected from the main network, making them invisible to attackers.
For a mid-sized enterprise, success isn’t just about getting data back; it’s about business continuity. The key metrics go beyond the technical. Of course, you have your Recovery Time Objective (RTO)—how fast you can be back online—and Recovery Point Objective (RPO)—how much data you can afford to lose. But a truly successful recovery is measured by client impact. Key metrics would include: restoring 95% of customer-facing services within one hour of an incident, ensuring no more than 15 minutes of transactional data is lost, and achieving full operational normalcy across all departments within a single business day. It’s about minimizing the shockwave of the attack across the entire organization.
New UK policies are creating a complex environment of both opportunities and hurdles for data centers. How will something like the Cyber Security & Resilience Bill change daily operations in a practical sense, compared to the challenges posed by Section 106 planning obligations? Could you walk us through the trade-offs a provider must navigate when planning a new facility?
They impact two very different stages of a data center’s life. The Cyber Security & Resilience Bill is an operational reality. On a day-to-day basis, it translates into more stringent security protocols, mandatory incident reporting timelines, and a much heavier administrative load to prove compliance. It shapes how you run the facility and demands a culture of constant vigilance. In contrast, Section 106 planning obligations are a strategic hurdle you face at the very beginning. While new fast-track planning laws for Nationally Significant Infrastructure Projects can speed up approvals, the Section 106 requirement to fund local community services can add significant, sometimes unexpected, costs to the project’s capital expenditure.
A provider planning a new facility is constantly balancing these forces. They might secure a prime location with fast-track approval, which is a huge win. But then they have to negotiate the Section 106 contribution. Is the cost of building a new local roundabout or funding a community center going to make the entire project financially unviable? It’s a trade-off between the speed and ease of getting the permit versus the upfront financial burden, all while knowing that once built, the operational costs will be higher due to the new cybersecurity regulations. It’s a complex dance of navigating upfront investment against long-term operational demands.
As the AI hype from 2025 settles into more practical applications, we’re hearing more about concepts like sovereign AI and inference AI. What specific infrastructure challenges do these create, and how does edge computing practically solve them for a sector like smart manufacturing?
The initial AI wave was all about massive training models in hyperscale data centers, but the next phase is about real-world deployment, and that brings new challenges. Sovereign AI is the requirement that an AI model, and the sensitive data it’s trained on, must remain within a country’s borders. Inference AI is the “thinking” part—where the AI makes decisions in real time—and it demands incredibly low latency. A centralized cloud hundreds of miles away simply can’t provide the speed or the data governance required for these applications.
This is where edge computing becomes essential. Imagine a smart factory floor filled with IoT sensors monitoring a production line. To use inference AI for quality control, the sensor data needs to be processed instantly. An edge data center located right there in the factory, or very nearby, can run the AI model locally. It can analyze a video feed, detect a microscopic defect in a product, and signal a robotic arm to remove it from the line in milliseconds. That’s a process that would be too slow and data-intensive if you had to send that high-definition video stream to a distant cloud and wait for a response. Edge solves both the low-latency need for inference and the data locality need for sovereign AI, keeping proprietary manufacturing processes secure and on-site.
We’re seeing predictions of more regional edge data centers being built near major UK cities. Beyond government initiatives, what are the primary business drivers for this decentralization away from the traditional London-centric model? Can you share some key performance indicators a company in the transport sector might see after shifting to a regional edge facility?
While government programs like the AI Growth Zones project are certainly a catalyst, the real momentum comes from pure business logic. First, there’s the pursuit of lower latency. For an ever-growing number of applications, from online gaming to real-time logistics, the speed of light is a real barrier, and being physically closer to your end-users provides a snappier, more competitive service. Second is resilience; concentrating all your critical infrastructure in the London and South East area creates a single point of failure. Distributing it regionally builds a more robust national digital economy. Finally, there are the practicalities of cost and sustainability, as power and land are often more available and affordable in other regions.
For a company in the transport sector, like a national haulage firm, the benefits are directly measurable. After moving their fleet management and logistics platform to a regional edge facility closer to their distribution hubs, they would see several KPIs improve. They could see a 30% reduction in data latency for their vehicle tracking systems, allowing for more efficient real-time rerouting. Their customer-facing booking app, now served from a closer data center, might see its page-load times improve by half a second, which is huge for user experience. And crucially, they gain a DR capability, meaning if there’s a major power outage in one region, their services can continue running from another, dramatically improving their overall service uptime.
What is your forecast for the UK’s hybrid cloud landscape over the next five years?
My forecast is for a landscape defined by intention and optimization rather than mass adoption. The “lift and shift” era is over. Over the next five years, businesses will become far more sophisticated in their approach. The default will no longer be “public cloud first,” but “workload-first,” where every application and dataset is strategically placed in the environment that best suits its specific needs for performance, cost, and compliance. We will see the rise of powerful management platforms that provide a single pane of glass to orchestrate workloads across on-premise, colocation, private cloud, multiple public clouds, and the burgeoning regional edge. The key drivers will be data sovereignty, forcing critical data into trusted domestic providers, and the demand for low-latency applications, which will fuel the build-out of a more distributed, resilient national infrastructure. It will be a far more complex, but ultimately more efficient and secure, hybrid world.
