The most advanced tools often require significant investment in licensing, infrastructure and integration. As a result, access to AI capabilities is often dictated by budget, not just technical readiness and expertise.
This raises an uncomfortable question: will AI create a deeper digital divide between organisations that can afford to adopt it fully, and those that can’t?
Well-funded industries are already using AI to gain a competitive edge, not just in capability, but in market position and dominance. In financial services, AI-driven trading algorithms, realtime fraud prevention and personalised customer experience systems are enabling faster, more profitable decisions. Institutions with the resources to invest heavily in these systems can process and act on data faster than their rivals, creating a widening performance gap.
This is particularly apparent in healthcare, where Trusts with the capital to deploy AI for diagnostic imaging, treatment planning and drug discovery are able to deliver better results at lower cost per patient. That improves both patient outcomes and operational efficiency, leading to a postcode lottery for citizens.
The result is a two-speed landscape: industries and organisations with the resources to fully adopt AI not only move faster but also consolidate their position, while those with fewer resources risk being left behind.
For the engineers, the risk is a widening skills and capability gap. Engineers in well-resourced environments will gain substantial experience working with advanced AI systems. Those in less resourced settings – Local Government, Third Sector – may not, making those industries less attractive to the very talent they need to drive innovation. The result is a self perpetuating cycle that sees ‘richer’ industries better able to attract the talent to help keep them ahead.
This divide isn’t just about access to AI, it’s about how engineering expertise develops. Teams with advanced AI may find themselves working at a more abstract level, with fewer opportunities for manual troubleshooting. Teams without it may retain those skills for longer, but at the cost of speed, efficiency, and end user experience.
Over time, we could see uneven skill profiles across the industry, with some engineers highly adept at managing AI-driven platforms, and others deeply experienced in hands-on engineering – but fewer with both.
AI will continue to develop, but its benefits will not be evenly distributed. Leaders should be asking how to avoid being left behind, whether that’s through direct investment, partnerships, or focused skills development.
The organisations that thrive will be those that combine access to advanced tools with a conscious effort to maintain engineering depth. Automation may change how work is done, but control still depends on people who understand the systems they run.
AI’s benefits are not guaranteed to reach all IT teams equally. Without careful planning, it could deepen the gap between well-resourced and under-resourced organisations. Bridging that divide will require more than just buying the tools – it will demand conscious investment in skills, knowledge and capability.
Chief Technology Officer (CTO)
Chelsea Chamberlin leads Roc’s Solution and Technology strategy, ensuring continual innovation and focussed partnerships which drive outcome based value for our customers. Chelsea’s background includes designing and delivering software and networking solutions within mission critical environments. Outside of work she is a green belt in Kick-boxing and mentor to young women paving careers in tech.
The best kind of change is often the kind you don’t notice happening. Systems run smoother, processes feel easier, people stop sighing at their screens – and suddenly everything just works a little better.
True modernisation is less about speed – and more about substance. We talk about modernisation as if it’s a race – a finish line you can cross and then move on. It isn’t.
The modern enterprise has shifted beyond recognition.
Hybrid working is embedded, cloud adoption continues at pace, and data now moves across a range of devices, locations, and applications. As this digital footprint expands, the traditional network perimeter has dissolved.
AI has altered the physics of cybersecurity. Threats now move faster than policies can adapt, and defences evolve in real time. The traditional idea of resilience – static controls, periodic reviews, and defined perimeters – no longer fits.
In an era where many students invest over £60,000 in a university education, the question of value for money has become a defining factor in university choice. But “value” is not universal.
Enterprise networks have reached an inflection point. Hybrid working is now embedded, cloud applications proliferate, and data moves continuously between users, devices and locations. UK organisations now manage more cloud identities than traditional endpoints, and more than half of enterprise traffic originates outside the office. As a result, network reliability, security and performance have shifted from operational concerns to board-level priorities.
AI is now woven into every part of cybersecurity operations. It analyses behaviour, identifies anomalies, and increasingly, takes action on its own. It’s faster, tireless, and in many cases more consistent than any analyst could be. But with that progress comes a harder question: how much decision-making are we prepared to give away?
There’s no escaping the squeeze. Demand is rising, budgets are flat, and no one’s in the mood for another “game-changing” announcement. People have heard so many promises of ‘once in a generation’ transformation that hype doesn’t hold much weight anymore.
There’s a moment every organization faces when it has to stop pretending the cracks aren’t there. The systems that don’t quite talk to each other. The processes everyone works around. The decisions that were right once — but no longer are.