Many organisations are investing heavily in AI tools to improve productivity, decision-making and customer experience. But as we explored in our latest Kaplan webinar, Why AI success starts with your infrastructure, the reality is clear: even the most advanced AI won’t deliver results without the right foundations in place.
Event panellists
Matt Rawlins, Director of Growth: Data and Technology, Kaplan
Michael Lafferty, Head of Data and Technology Apprenticeships, Kaplan
Chiraag Swaly, Head of Data and Technology Curriculum, Kaplan
Matt Rawlins studied for his ACCA qualification at Kaplan from the age of 18 and joined the business in 2006. He now supports organisations to develop the digital and data skills they need to thrive.
Michael Lafferty and Chiraag Swaly bring over 20 years of combined experience in digital and IT apprenticeships. Their work spans technical support, curriculum design and programme development, helping organisations build capability in data and technology roles.
The “Ferrari on a dirt track” problem
A recurring theme in the discussion was what we called the “Ferrari on a dirt track” problem. Businesses are adopting powerful AI tools, but expecting them to run on outdated or underprepared infrastructure.
Infrastructure, in this context, means everything that allows data to move effectively: networks, bandwidth, cloud environments, and security systems. When these aren’t fit for purpose, AI performance suffers.
“Infrastructure is one of the key pillars that can stop AI projects from scaling well.”
In some cases, the consequences go beyond inefficiency. One example shared in the session highlighted how a healthcare provider experienced a drop in diagnostic accuracy because their infrastructure couldn’t keep pace with real-time AI demands.
The takeaway is simple: AI is only as strong as the environment it runs in.
The evolving role of the IT technician
This shift is transforming the role of the IT technician.
No longer limited to “break-fix” support, today’s technicians are becoming what we described as digital mechanics — professionals responsible for maintaining the entire digital engine room of a business.
“They’re not just fixing laptops anymore — they’re ensuring the infrastructure can handle the ambition of the business.”
This includes managing high-speed networks, maintaining secure data flows, supporting cloud-based tools, and proactively resolving issues before they escalate.
Technologies like AIOps are accelerating this shift. By analysing patterns and predicting potential failures, these tools enable teams to resolve issues faster — and in some cases, before users even notice a problem.
The result is a move from reactive troubleshooting to proactive system management, improving uptime and overall business performance.
Security, shadow AI and digital trust
Security remains one of the biggest concerns for organisations adopting AI — and with good reason.
“Shadow AI” — where employees use personal AI tools without approval — is now widespread across many workplaces.
“Shadow AI is nearly universal in modern workplaces.”
While often well-intentioned, this behaviour can introduce serious risks, including the accidental exposure of sensitive data.
Rather than restricting access entirely, organisations need to focus on building secure, supported environments for AI use. This includes implementing tools such as data loss prevention (DLP), creating approved AI platforms, and educating employees on responsible usage.
A strong security culture — supported by both technology and training — is essential to building digital trust.
Bridging the tech talent gap
Alongside infrastructure challenges, many organisations face a growing skills gap. Finding experienced professionals in areas like cloud security, AIOps and AI support can be expensive and difficult.
A more sustainable approach is to develop talent from within.
Apprenticeships provide a practical, hands-on way to build capability, allowing individuals to learn while working directly within their organisation’s infrastructure. This not only develops relevant technical skills, but also ensures knowledge is embedded within the business.
For employers, this approach helps create a steady pipeline of talent, reduces reliance on contractors, and supports long-term retention.
Crucially, modern programmes go beyond technical training. They also develop communication, problem-solving and adaptability — all essential skills in an AI-driven workplace.
Building your AI-ready future
AI adoption isn’t slowing down. But success depends on more than just choosing the right tools.
“AI success isn’t just about the software you buy. It’s about the infrastructure you build — and the people who maintain it.”
Organisations that invest in their infrastructure — and the people who manage it — will be best positioned to unlock the full potential of AI.
That means strengthening your digital foundations, embedding security into your systems, and developing a workforce that can adapt as technology evolves.
Because ultimately, AI success doesn’t start with algorithms. It starts with your infrastructure.
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