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AI can help engineering teams do some work faster and compensate for certain skills gaps, but current evidence does not show that it can solve the world’s engineering shortage. Adoption is uneven, and using AI effectively requires training, workflow changes and human review. The available figures describe UK engineering employers, broader small and medium-sized businesses, and software engineering separately—not a global estimate of engineering vacancies AI could fill.
What the evidence says about AI in engineering
The clearest recent engineering-sector figures here come from a UK survey by the Institution of Engineering and Technology (IET), conducted with YouGov. It surveyed 1,316 people with managerial responsibility at engineering or technology employers; fieldwork ran from 10 February to 13 March 2025.
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In the IET’s published summary, 58% of respondents said their employer currently used AI, but only 18% said it used AI regularly. Those are reports of adoption, not proof that AI increased output or removed a vacancy. Expectations were more optimistic: 61% expected AI to improve productivity, and 50% expected it to enhance problem-solving. Expectations are not measured results. IET, 2025
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →The same survey points to a skills-and-capacity problem, not just a shortage of people. Employers most often named automation and cybersecurity as digital skills needed for growth (38% each), followed by data engineering (34%) and software engineering (33%). Thirty per cent reported lacking automation skills; 17% said they had difficulty recruiting for data and software engineering roles as well as cybersecurity roles. Half cited lack of time as a barrier to upskilling or reskilling, and 46% said employee turnover hindered progress. These responses suggest AI adoption and workforce development may compete for the same limited time and expertise.
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Where AI may help—and what it cannot establish
AI can support particular tasks or help workers make use of scarce expertise. Broader evidence suggests some businesses perceive that benefit, but it should not be mistaken for an engineering-specific result. In its 2025 review, the OECD reported that nearly two in five small and medium-sized enterprises (SMEs) had experienced a worker shortage in the previous two years, and one third reported a lack of skills or experience among staff. Among SMEs reporting a skills gap, nearly 40% said generative AI helped compensate for it; a quarter said it helped compensate for a worker shortage. These are self-reported findings across sectors, not counts of engineers replaced or vacancies filled. OECD, “AI and skills” (2025)
AI can also change which skills employers need. Gartner’s October 2024 release forecast that 80% of the engineering workforce would need to upskill through 2027 as generative AI changed work. That is an analyst forecast, not a verified outcome—and Gartner’s cited survey concerned software engineering. In a survey of 300 organizations in the United States and United Kingdom in the fourth quarter of 2023, 56% of software engineering leaders rated AI/ML engineer the most in-demand role for 2024 and identified applying AI/ML to applications as the biggest skills gap. Those results should not be generalized to civil, mechanical or electrical engineering. Gartner, 3 October 2024
Why productivity gains depend on people and organizations
Having access to an AI tool does not by itself create the expertise, time or processes needed to use it well. The OECD found that around 40% of employers in manufacturing and finance that had not adopted AI cited skills as the main reason; more than half of SMEs not yet using generative AI did so as well. More than half of workers using AI reported receiving employer-funded training, and trained users were more likely to report positive outcomes. These cross-sector findings underline a practical obstacle: a skills shortage can make it harder to implement the tools intended to ease it. OECD, 2025
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The National Academies of Sciences, Engineering, and Medicine describes AI as a general-purpose technology whose future development remains uncertain. Current systems can give incorrect answers, show bias or fail to reason correctly from facts. The Academies notes that AI can improve worker outcomes or displace workers, and that getting its benefits likely requires investment in skills and organizational processes. As its 2025 summary puts it: “As was the case with earlier general-purpose technologies, achieving the full benefits of AI will likely require complementary investments in new skills and new organizational processes and structures.” National Academies, Artificial Intelligence and the Future of Work (2025)
For engineering work, those limits make human review and accountability central, especially wherever errors could affect safety or public trust. A European skills strategy identifies shortages in areas including electrical and electronic engineering, ICT, and agronomic and environmental engineering, while emphasizing AI, data, cybersecurity, renewable energy, sustainability and analytical skills in a changing workforce. It is a European strategy document, not a worldwide vacancy census. Engineers for Europe, Skills Strategy 2025
What employers should take from the evidence
The evidence supports treating AI as one possible way to increase capacity—not as a substitute for workforce planning or professional judgment. The value of a tool will vary with the task, discipline, readiness of the team and consequences of an error. A software-development finding does not automatically apply to physical operations, field work or other engineering specialties.
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- Identify the specific bottleneck. Distinguish a lack of people from a lack of particular skills, recruitment difficulties or insufficient time to train and adapt.
- Match the tool to the task. Evaluate where AI can assist with work and where engineering expertise, verification or physical work remains essential.
- Budget for adoption. Training and changes to processes are part of implementation, not optional extras that can be assumed to happen on their own.
- Keep responsibility clear. AI-generated or AI-assisted work needs appropriate review, with accountability assigned to qualified people.
- Measure actual outcomes. Track whether a defined workflow improves, rather than treating reported use or expected benefits as proof that a staffing gap has closed.
Can AI solve the global engineering shortage?
No available figures cited here establish how many engineering vacancies exist worldwide or what share AI could close. The evidence shows reported use and expected benefits among UK engineering and technology employers, self-reported shortage assistance among SMEs across sectors, and forecasts focused on software engineering. It does not support a numerical prediction that AI will solve the global shortage.
Engineering demand and the usefulness of AI vary by geography, specialty, experience and project needs. AI may ease specific constraints when teams have the skills and organizational support to use it, but the evidence points to adaptation alongside human expertise—not a demonstrated replacement for the engineering workforce.
Further reading
Artificial Intelligence and the Future of Work (National Academies of Sciences, Engineering, and Medicine, 2025) examines broader questions about AI, productivity, workforce effects and skills.
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