AI is moving from from hype to reality. Across industries, it’s being pitched as the key to smarter operations, sharper customer engagement, and new routes to growth.
In the UK, that potential is clearly recognized. According to recent research, 88% of UK business leaders see AI tools as crucial to their organization’s priorities in the next 12 months.
CTO of EMEA at Extreme Networks.
But here’s the disconnect: nearly half admit their IT infrastructure isn’t ready to support those ambitions.
Significant investment is being poured into AI projects across the country, yet many of them are stalling, underdelivering, or not progressing beyond pilot stage.
So, what’s going wrong?
The problem: modern ambitions meet outdated networks
Many organizations are approaching AI with vision but not with the foundational capabilities to support it. We’ve seen this story before – businesses racing to implement the latest technologies without addressing the underlying infrastructure required to make them function effectively.
AI isn’t plug-and-play. It’s data-hungry, compute-intensive, and demands fast, reliable access to information. That creates enormous pressure on legacy systems, many of which were built for a different era – one where data didn’t need to be processed in real time or accessed from the edge of a network.
One of the most common consequences is that AI projects never progress beyond the pilot phase. On paper, the business case looks strong: automating customer service, improving forecasting, personalizing digital experiences.
But in practice, the infrastructure can’t support live testing or scale effectively beyond a proof of concept. Without reliable, low-latency connectivity and flexible compute resources, initiatives stall – not because the models don’t work, but because the network can’t keep up.
Even when projects do launch, they’re often hampered by delays caused by poor data availability or fragmented systems. If clean, real-time data can’t flow freely across the organization, AI models can’t operate effectively, and the insights they produce arrive too late or lack impact.
The promise of AI is real, but its potential will remain out of reach until businesses address the infrastructural barriers standing in the way.
What an AI-native infrastructure really looks like
The good news? This is fixable. Fixing this means going beyond surface-level upgrades. It calls for infrastructure that is AI-native by design, built to support scale, speed, and continuous evolution.
That starts with cloud-native architecture. Unlike static, traditional systems, cloud-native environments offer the elasticity needed for AI workloads that shift constantly in size and complexity.
Whether training a large model or deploying across multiple teams, organizations need the ability to scale resources instantly, without overprovisioning or hitting performance limits.
Speed is equally vital. AI depends on fast, frictionless data movement, and any network delays can undermine time-sensitive use cases like fraud detection or real-time decisioning.
A high-performance, low-latency network ensures data can flow quickly, securely, and reliably, so AI-driven insights are delivered when they matter most. It also enables proactive identification and response to security issues before they have a chance to escalate or cause disruptions.
As more data is generated at the edge – in factories, stores, vehicles or remote offices – edge computing becomes a crucial piece of the puzzle.
Processing data closer to where it’s produced reduces latency and cuts down on bandwidth use. In environments like logistics or manufacturing, where split-second responses matter, this kind of agility is a game-changer.
The importance of adaptability
Adaptability also matters. AI workloads are in constant motion, which means infrastructure must be able to self-optimize. Automated systems can balance loads, reroute traffic, and resolve issues before they impact performance.
With observability (real-time visibility into infrastructure behavior), IT teams can stay ahead of problems, not just react to them.
And finally, innovation must be balanced with control. AI thrives on experimentation: new models, new datasets, rapid iteration. But that agility can’t come at the expense of control.
Particularly in regulated industries, infrastructure must enable fast-paced development while ensuring security, compliance, and proper oversight every step of the way.
Ultimately, the focus remains backwards: too many investments go toward front-end tools and models, which often deliver limited impact on their own.
Prioritizing foundational work to build a robust and scalable infrastructure is essential for enabling these tools to perform reliably and drive meaningful use cases with measurable ROI.
This isn’t just a UK problem. With 80% of AI projects struggling to deliver on expectations globally, primarily due to infrastructure limitations rather than the AI technology itself, what matters now is how we respond.
Platformization for AI success
Addressing infrastructure is only part of the solution. Businesses also need integration and cohesion.
In many organizations, departments operate in silos, relying on systems that don’t communicate. This fragmentation makes it difficult to consolidate data, train models effectively, or deliver AI-driven insights where they’re needed most.
Platformisation solves this by unifying systems, data flows, and digital operations into a single, integrated environment.
By consolidating network data across the enterprise on a shared infrastructure, organizations can streamline how data is captured, processed, and acted upon, eliminating inefficiencies that often derail AI initiatives.
This unified approach enables real-time insights, AI-driven anomaly detection, and advanced analytics to optimize performance, enhance security, and support confident, data-driven decisions.
Speed to insight is also critical. Many AI tools deliver the most value when their outputs are used immediately, not hours or days later.
Real-time processing capabilities within a platformization environment ensure those insights arrive when they’re still actionable, avoiding delays caused by batch processing or disconnected systems.
When data flows freely, decisions can be made faster, with greater confidence and precision.
In short, platformization is about removing the barriers between strategy and execution, and giving AI the environment it needs to actually deliver.
The path forward
For CIOs and IT leaders, the message is clear: focus on the infrastructure that makes AI work, not just the AI itself.
This means moving away from isolated upgrades and embracing strategic, end-to-end transformation.
It means treating the network not as a background utility, but as a critical lever for innovation.
And it means building systems that can support not just today’s AI, but tomorrow’s.
The UK government has pledged an additional £1 billion to scale up computing power by a factor of 20 – a signal of just how central AI has become to future growth.
But public investment alone won’t be enough. It’s up to individual organizations to ensure their infrastructure is ready to support what comes next.
The opportunity is clear, but so is the need to act.
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