What Impact Is AI Having on Entry-Level Engineering Roles – And What Should You Do About It?

AI is not removing the need for entry-level engineers. It is changing what “entry-level” means.

For engineering employers, especially across manufacturing, automation, product development, maintenance and process engineering, the risk is not that junior roles disappear entirely. The bigger risk is that businesses stop building early-career talent pipelines because AI makes short-term productivity gains look easier than training.

AI is changing the first two years of an engineering career

Traditionally, junior engineers learned through repetitive technical work: documentation, CAD amendments, test data review, basic fault-finding, compliance admin, drawing checks and production support.

AI tools can now speed up parts of that work. In engineering environments, this may include generating reports, reviewing maintenance data, supporting design iterations, creating test summaries or flagging anomalies in production information.

That does not make junior engineers redundant. It means they need to become useful faster in areas AI cannot own: judgement, safety awareness, root-cause thinking, stakeholder communication and understanding how decisions affect real equipment, materials and processes.

The skills shortage has not gone away

The UK engineering market is still under pressure. The IET’s 2025 skills research found that automation and cyber security were the top digital skills needed for growth, both cited by 38% of employers, followed by data engineering at 34% and software engineering at 33%. It also found that 30% of organisations lack automation skills.

Manufacturing remains tight too, with recent estimates putting UK manufacturing vacancies at around 58,000–61,000.

So while AI may reduce some low-value tasks, it is also increasing demand for engineers who understand automation, data, controls, robotics, validation, reliability and digital manufacturing.

The danger: cutting junior roles creates a mid-level gap

If employers use AI to reduce graduate, apprentice or junior hiring, they may save cost now but weaken their future talent base.

Engineering capability is built through exposure: seeing failures on the shop floor, understanding tolerances, learning why a design works in theory but fails in production, and building confidence around quality, safety and compliance.

AI cannot replace that practical learning curve. If businesses remove entry-level routes, they will later struggle to hire experienced engineers who were never trained in the first place.

What hiring managers should do next

The best approach is not to hire fewer junior engineers. It is to redesign junior engineering roles.

Entry-level engineers should still handle real technical work, but with clearer expectations around AI-assisted productivity. That could mean:

• using AI to speed up documentation, not replace technical checking
• training juniors to validate AI outputs against engineering standards
• pairing early-career engineers with senior mentors on live production or design issues
• assessing curiosity, problem-solving and data confidence during interviews
• building development plans around automation, CAD, PLCs, quality systems, test methods or reliability engineering

The strongest junior hires will not necessarily be those who “know AI”. They will be those who can use digital tools while still thinking like engineers.

Recruitment 

AI is raising the bar for entry-level engineering roles, but it is not removing the need for early-career talent. Employers that keep investing in junior engineers, apprentices and graduates will be better placed to deal with future shortages in maintenance, design, automation, process, quality and manufacturing engineering.

If you’re hiring engineering talent and want a realistic view of the market in Wales or the UK, IntaPeople can help with salary benchmarking, talent mapping and hard-to-find engineering skillsets.