Hybrid Cloud Services provider DTP Group warns that the Government's £187 million skills drive will fail without parallel investment in digital infrastructure capable of supporting an AI-native workforce.
The UK Government's newly announced £187 million TechFirst programme represents a significant commitment to preparing the nation's workforce for an AI-driven future. With plans to train 7.5 million UK workers in essential AI skills by 2030, equivalent to around 20% of the workforce, the ambition is clear. However, according to IT infrastructure experts at DTP Group, this skills revolution may be built on shaky foundations unless organisations simultaneously invest in the digital infrastructure needed to support it.
The challenge extends far beyond simply teaching people how to use AI tools. As Guy Hawkridge, Head of IT & Security at Leeds-based DTP Group, explains: "We're teaching young people to drive Ferraris on roads built for bicycles. Without the proper infrastructure foundation, even the most skilled AI-native workforce will be constrained by outdated systems that can't deliver on AI's potential."
The scale of infrastructure challenge
The economic stakes are substantial. The AI sector alone is valued at £72.3 billion and is projected to exceed £800 billion by 2035, whilst the IMF estimates that embracing AI can boost productivity by as much as 1.5% annually. If fully realised, these productivity gains could be worth up to an average £47 billion to the UK each year.
However, realising these benefits requires more than skilled workers, it demands infrastructure that can actually support AI workloads at scale. This is particularly challenging in sectors where digital infrastructure has historically lagged, such as education, public services, and healthcare.
Research commissioned by the Department for Science, Innovation and Technology shows that by 2035, around 10 million workers will be in roles where AI will be part of their responsibilities, with a further 3.9 million in roles directly involving AI. Yet many of these workers will be operating within organisations whose infrastructure cannot adequately support the deployment of AI.
From support function to strategic enabler
The shift towards AI-enabled operations fundamentally changes the role of IT infrastructure from a support function to a strategic enabler. Traditional infrastructure models, designed around predictable workloads and standardised applications, struggle to accommodate AI's dynamic requirements for compute power, data mobility, and real-time processing.
"AI workloads demand hybrid environments, high availability, and edge-to-cloud interoperability," explains Hawkridge. "They introduce new dependencies on data mobility, compute flexibility, and increasingly, on automation. This evolution reframes IT operations as a strategic partner in business transformation, not just a cost centre."
This transformation is already visible across enterprise environments, where AI-assisted IT service management, predictive maintenance systems, and intelligent workload balancing are becoming standard rather than experimental. For infrastructure leaders, the challenge is no longer if AI will impact their environments, but how they'll accommodate it without compromising governance, security, or resilience.
The hidden infrastructure requirements
While much attention focuses on the visible aspects of AI deployment, models, algorithms, and user interfaces, the underlying infrastructure requirements are often overlooked. AI systems require robust identity management, comprehensive monitoring capabilities, and secure data pipelines that can handle both structured and unstructured data at scale.
"A well-trained AI model is only as effective as the environment it runs in," notes Hawkridge. "Secure identity management, auditability, and compliance with frameworks like GDPR or Cyber Essentials must become foundational requirements, not afterthoughts."
This is particularly critical in sectors handling sensitive data, where AI deployment must balance innovation with stringent security and compliance requirements. Healthcare systems processing patient data, educational institutions managing student information, and public sector organisations handling citizen data all face unique challenges in implementing AI securely.
Building resilient AI-ready infrastructure
The conversation around AI readiness shouldn't be confined to skills training and literacy programmes. Organisations need to think more broadly about systems architecture: are networks structured to encourage safe AI experimentation? Can the current infrastructure support real-time AI inference? Do existing systems have the capacity and flexibility to utilise AI tools meaningfully?
Key infrastructure considerations for AI-ready organisations include:
- Hybrid Cloud Capabilities: AI workloads often require the flexibility to move between on-premises and cloud environments based on data sensitivity, compliance requirements, and performance needs.
- Edge Computing Integration: Many AI applications require low-latency processing, necessitating edge computing capabilities that can process data closer to where it's generated.
- Scalable Storage Solutions: AI systems generate and consume vast amounts of data, requiring storage solutions that can scale dynamically whilst maintaining performance.
- Network Resilience: AI applications often require consistent, high-bandwidth connectivity to function effectively, making network reliability critical.
- Security by Design: AI systems must be built with security as a foundational element, not an afterthought.
The education sector challenge
The education sector presents a particularly compelling case study for the infrastructure challenge. With 1 million students across every secondary school in the UK set to gain access to new AI skills training over the next three years, schools and colleges must simultaneously upgrade their digital infrastructure to support this learning.
Many educational institutions are already struggling with basic connectivity and device management challenges. Adding AI-enabled learning tools to this environment without addressing fundamental infrastructure limitations risks creating a frustrating experience for both educators and students.
"If we're serious about preparing the next generation for an AI-driven economy, we need to ensure that educational institutions have the infrastructure to support meaningful AI engagement," argues Hawkridge. "This means more than just providing AI tools, it means creating environments where students can experiment, learn, and innovate safely."
A joined-up approach to digital transformation
The success of the Government's AI skills initiative ultimately depends on organisations taking a holistic approach to digital transformation. Investment in human skills must be matched by investment in the infrastructure and systems that enable those skills to be applied effectively.
This requires organisations to move beyond thinking about AI as simply another software application and instead consider it as a fundamental shift in how technology systems operate and interact. It demands infrastructure that is secure, scalable, and sophisticated enough to support not just today's AI applications, but tomorrow's innovations.
As the UK embarks on this ambitious skills transformation, the organisations that will thrive are those that recognise AI readiness as both a people and infrastructure challenge. Those that invest in both will position themselves to fully realise the economic benefits of AI adoption. Those that don't may find their skilled workforce constrained by systems that simply cannot deliver on AI's promise.
The choice is clear: build the digital foundations today, or risk teaching tomorrow's workforce skills they cannot effectively apply.