The rise of AI coding assistants is reshaping the UK’s digital economy, transforming how technical talent is hired, trained and developed across a sector that employs 2.8 million people.
While these tools can significantly accelerate software development and unlock new gains in engineering productivity, they also require organisations to rethink how they assess skills, build teams and define career progression. In an environment where machines are evolving from collaborative copilots into digital colleagues, traditional talent models are increasingly under strain.
Understanding how AI is changing the workforce is only part of the challenge. Organisations must also build the foundations required to support an AI-enabled workforce. While it is tempting to frame this shift as disruption, it is better understood as an evolution, but one that demands a more considered and introspective approach to workforce strategy. This is particularly true for UK employers, many of whom, according to a government report, have a limited understanding of the AI skills their workforce will require.
For engineers, the definition of excellence is broadening. Success now depends on a combination of technical fluency, tool literacy, domain knowledge and human judgement. Yet demand continues to outstrip supply. The Institution of Engineering and Technology reports that 76% of UK engineering employers struggle to recruit for key roles, with shortages most acute in technical and specialist sustainability skills.
Increasingly, engineers are expected to collaborate effectively with intelligent systems, drive returns from agentic AI through personalised workflows, navigate ambiguity and make decisions informed by wider business and user contexts. In some roles, an engineer with solid (but not exceptional) coding skills, paired with strong system-design thinking and interpretive ability, may be more valuable than a specialist coder alone.
Below are five ways organisations can rethink IT and engineering talent for the AI era.
1. Adapt interviews to be more targeted and scenario-based
The impact of AI on talent structures begins with recruitment. As candidates increasingly use AI tools to produce polished outputs, organisations are being forced to dig deeper during interviews. Many are shifting towards scenario-based assessments grounded in real-world problems that reflect day-to-day engineering environments.
To counter over-reliance on AI assistance, employers are also reasserting the value of in-person interviews, combined with live projects or demonstrations. These formats require candidates to think independently and apply practical skills in real time.
Interview design can vary by role. Research-focused positions may emphasise algorithmic innovation, execution roles may prioritise tool proficiency and code robustness, while planning and design roles often focus on architecture and technology selection. Across all roles, interpretability matters; not only of AI models, but of entire systems. Candidates must be able to explain how they reason through complexity and ensure transparency, particularly when AI-generated code is involved.
2. Establish guardrails against AI over-reliance
The widespread availability of AI coding tools brings new risks. Candidates may lean too heavily on automation during interviews or trial assignments, making it difficult to assess their underlying understanding or their grasp of how code functions within a broader system.
Live coding exercises and detailed code walkthroughs, where candidates must explain their logic and decisions, can help distinguish genuine competence from superficial augmentation. These exercises also reveal whether candidates understand how to manage AI-generated outputs without increasing technical debt.
Beyond hiring, excessive reliance on AI can erode core skills, abstracting away critical technical details and limiting an engineer’s ability to debug, optimise or design systems holistically. Training and onboarding must therefore reinforce fundamentals, ensuring engineers retain a resilient skill base while integrating new tools into their workflow.
3. Tailor development by career stage
AI is also reshaping career paths differently for early-career and experienced engineers. For graduates, automation has raised expectations by reducing the value of some entry-level coding tasks. New hires are now assessed not only on what they can do independently, but on how effectively they evaluate, refine and improve AI-assisted outputs.
For more experienced engineers, value increasingly lies in abstraction, orchestration and mentorship. As routine coding is delegated to machines, senior professionals are expected to focus on system design, integration and guiding others. Those who combine deep domain expertise with AI-enabled productivity are likely to progress faster and have wider organisational impact.
4. Redefine onboarding and development
Onboarding and professional development must also evolve. Rather than relying on theoretical training, organisations should simulate real development environments where AI tools are embedded in everyday workflows. This allows employers to assess how engineers use tools in practice, how independently they work and how quickly they adapt as technologies change.
Hiring, however, is only the start. During probationary periods, organisations must evaluate performance in real conditions, examining not just task completion, but how effectively and autonomously work is delivered in an AI-enabled environment. The ability to improve output quality and identify weaknesses in AI-generated suggestions is becoming a key indicator of long-term success.
5. Shift to a human-plus-machine mindset
Ultimately, the evolution of AI talent is about more than tools; it reflects a broader shift in mindset. Leading organisations recognise that success depends on developing engineers who can operate in a “human-plus-machine” model, knowing when to trust AI, when to challenge it and when to explore alternative approaches.
Managing these hybrid teams has also prompted many organisations to seek external expertise. Advisory and managed services can help define return-driven use cases and ensure AI systems remain secure, accurate and effective in production.
The engineers of the future will not be defined solely by their ability to write code. They will be strategic thinkers, ethical technologists and adaptive learners who view AI as a long-term partner rather than a shortcut. For organisations, this requires a full lifecycle approach to adoption, albeit one that aligns talent strategy, tooling and governance.
Lenovo’s AI Adoption and Change Management Services support organisations in rethinking talent strategies, integrating AI tools effectively and building high-performing human-plus-machine teams.
The rise of AI coding assistants is reshaping the UK’s digital economy, transforming how technical talent is hired, trained and developed across a sector that employs 2.8 million people.
While these tools can significantly accelerate software development and unlock new gains in engineering productivity, they also require organisations to rethink how they assess skills, build teams and define career progression. In an environment where machines are evolving from collaborative copilots into digital colleagues, traditional talent models are increasingly under strain.
Understanding how AI is changing the workforce is only part of the challenge. Organisations must also build the foundations required to support an AI-enabled workforce. While it is tempting to frame this shift as disruption, it is better understood as an evolution, but one that demands a more considered and introspective approach to workforce strategy. This is particularly true for UK employers, many of whom, according to a government report, have a limited understanding of the AI skills their workforce will require.
