How Many UK Businesses Do Not Have AI Trained Employee and Risks

AI & Automation: The Skills Gap Leaving UK Businesses Behind

The UK is facing an urgent crisis: the rapid adoption of AI is clashing head-on with a profound lack of in-house expertise. For UK SMEs, AI is not just a technological upgrade—it's a productivity imperative. However, the data reveal that most businesses are critically unprepared, creating a huge risk of competitive disadvantage.

This is the new reality: AI without trained people is just expensive, fragmented software.

How Many UK Businesses Lack AI-Trained Employees?

The challenge of lacking "AI-trained employees" manifests in two critical ways: a lack of adoption and a severe lack of capability. The numbers paint a stark picture:

The Adoption Gap

While the UK’s dedicated AI sector is growing rapidly, the mainstream adoption rate is still dangerously low for the wider economy, especially among SMEs.

• Vast Majority Not Using AI: Only 25% of UK businesses were reported to be actively using AI technology as of mid-2024. A staggering 43% of firms reported having no plans to use AI at all.
• The SME Lag: Larger businesses (250+ employees) are significantly more likely to use AI (around 30%), but the adoption rate drops dramatically for smaller businesses, leaving the critical SME engine of the UK economy exposed.
• Widespread Lack of Readiness: Even among companies that plan to invest more in AI, only 13% feel fully prepared to implement it.

The Capability Gap

The shortage of skilled individuals is even more severe, affecting both technical experts and the general workforce who need to use AI effectively.

• 72% Skills Shortage: A substantial 72% of UK firms report struggling with skills gaps in critical areas, including AI, data analytics, and cybersecurity.
• Workforce Untrained: Less than half of the UK's working population has received an opportunity to learn about using AI at work.
• Direct Skills Barrier: When asked about roadblocks, a lack of AI-specific skills and experience is cited as a top reason for AI projects failing, with less than 28% of business leaders believing their current workforce has sufficient skills for effective, safe AI use.

The Hidden Risks of Ignoring the AI Skills Imperative

Ignoring the AI skills gap is not a cost-saving measure; it is a direct driver of future risk, inefficiency, and lost competitive edge.

1. The Productivity & Financial Drain

The immediate lack of AI skills is creating measurable financial losses and project failures.

• Stalled Digital Transformation: A third of UK businesses are already struggling to fill roles in data, AI, and automation, directly hampering efforts to fully integrate AI into operations.
• Wasted Investment: More than a third (36%) of British businesses that have tried to implement AI solutions in the past 12 months have seen those projects fail, primarily due to the lack of proper skills and experience.
• Loss of ROI: While UK firms are pouring an average of into AI, 70% are unsure whether the technology is delivering its full potential, leading to piecemeal and fragmented adoption..

2. Security, Ethical, and Competency Risks

Untrained employees using powerful AI tools introduce entirely new categories of risk to the business.

Key Risk Areas in Workplace AI Adoption

Key Risk Areas in Workplace AI Adoption

Shadow IT and Misuse A growing concern for organisations is employees using unapproved or unsupported AI tools that fall outside official company systems. These tools often bypass established security and compliance protocols, creating exposure to data leaks or breaches. Research shows that over a third (38%) of workers using AI admitted to using it in inappropriate ways, including uploading copyrighted or confidential information.

Errors and Over-Reliance Another major risk lies in employees trusting AI output without sufficient verification. When staff depend too heavily on automated recommendations or generative content, errors and inaccuracies can quickly spread through business processes. In fact, 58% of workers using AI report relying on its output at work without evaluating its accuracy, increasing the risk of flawed decision-making.

Competitive Disadvantage Finally, businesses that fail to embrace AI strategically risk losing ground to competitors that do. While AI can dramatically enhance efficiency, innovation, and customer experience, many UK firms remain hesitant. With productivity already at an all-time low, leaders who delay AI adoption or fail to train their teams effectively risk falling behind their global counterparts.

The Strategic Solution: Closing the AI Gap with DevOps

The immediate answer to the UK's AI skills crisis is not an endless, costly search for senior AI engineers, but a foundational commitment to automation and capability building. This is where the Level 4 DevOps Engineer Apprenticeship, delivered by The Coders Guild, becomes the critical strategic solution for UK SMEs.

1. The Foundational Skill Set: Automation Before AI

You cannot implement AI and automation tools effectively without robust underlying infrastructure and processes. DevOps skills provide the essential groundwork:

• Infrastructure as Code (IaC) Mastery: AI models run on cloud infrastructure. Apprentices are trained to use tools like Terraform and Azure to create and manage the consistent, scalable cloud environments necessary for hosting large language models (LLMs) and other AI applications. This eliminates the "fragmented, piecemeal" AI adoption most businesses suffer from.
• CI/CD for Deployment Speed: DevOps teams master Continuous Integration and Continuous Deployment (CI/CD) pipelines. This is crucial for AI projects, where models often need to be updated, tested, and redeployed rapidly to learn from new data. The ability to deploy code 46 times more frequently (as cited in the DORA report) translates directly into faster AI iteration and better results.
• Monitoring & Observability: AI systems are complex and prone to unexpected errors. The DevOps focus on Observability ensures teams can track system health, spot issues (like data drift or security vulnerabilities), and recover from incidents 96 times faster than traditional teams, protecting your AI investment.

2. A Cost-Effective, In-House Talent Pipeline

The financial reality for many SMEs is that recruiting an experienced AI specialist at $\text{\textsterling}70,000$+ is unsustainable. Apprenticeships flip this challenge into an opportunity:

• Build, Don't Buy: Instead of competing for expensive external talent, the Level 4 Apprenticeship is designed to build capability from the inside out. Your apprentice is embedded in your team, applying learned skills to your specific business challenges from day one.
• Immediate & Billable Skills: The Coders Guild’s industry-led approach ensures apprentices gain skills that are "billable within weeks." They learn and apply key DevOps tools (Docker, Kubernetes, Python) in a hands-on, work-based learning environment, delivering tangible returns far faster than traditional academic routes.
• Strategic ROI: The UK Government Apprenticeships Evaluation highlights that 92% of employers report productivity gains within 12 months of taking on an apprentice. This is a direct measure of efficiency in the face of the $\text{\textsterling}275$ million in lost productivity caused by manual tasks.

3. Fostering a Culture of Continuous Improvement

AI requires a learning mindset and a culture that embraces change—core tenets of the DevOps philosophy.

• Empowering the Workforce: By training existing or new staff through an apprenticeship, you are actively addressing the fact that less than half of UK workers have been offered AI training. This investment sends a clear message: you are empowering your people to lead the digital transformation, mitigating the employee dissatisfaction and talent drain caused by outdated processes.
• Mitigating 'Shadow AI': A structured program provides clear guidance and training in the responsible use of AI, directly combating the risk of employees misusing unapproved tools or relying blindly on unverified AI outputs.
• Future-Proofing Beyond Today: The Level 4 DevOps framework instills a mindset of continuous learning and strategic thinking, ensuring your business is ready not just for the AI tools of today, but for the exponential technical changes of tomorrow.

You cannot adopt AI effectively until you master automation and quality assurance. That journey starts with people.

Ready to turn your AI ambition into proven capability?