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AI can ease some engineering capacity and skills constraints, but current evidence does not show that it can solve the world’s engineering shortage. It can support parts of engineering work and help some employers compensate for skills gaps. Yet adoption is uneven, AI can create demand for new specialist skills, and productivity gains depend on training, workflow changes and human oversight. The available figures do not establish how many engineering vacancies AI could fill worldwide.
What the evidence says about AI in engineering workplaces
The clearest recent engineering-sector figures here come from a UK survey, not a global census. The Institution of Engineering and Technology (IET), working with YouGov, surveyed 1,316 people with managerial responsibility at engineering or technology employers. Fieldwork ran from 10 February to 13 March 2025.
In the IET’s published summary, 58% of respondents said their employer currently used AI, but 18% said it used AI regularly. These are reports of adoption, not measurements of engineering output or evidence that jobs or vacancies were eliminated. The survey also found variation in adoption across UK regions. IET, 2025.
Employers were optimistic about potential benefits: 61% expected AI to improve productivity and 50% expected it to enhance problem solving. Those are expectations, not experimentally verified gains. The gap between reported use and regular use is a reminder that having access to AI does not automatically translate into sustained help with engineering work.
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How AI may help with skills gaps—and what that does not prove
AI may provide assistance where a team lacks particular skills or experience, but broader workforce evidence should not be mistaken for engineering-specific results. The OECD’s 2025 review reports that nearly two in five small and medium-sized enterprises (SMEs) had experienced a worker shortage in the prior two years, while one third reported a lack of staff skills or experience. Among SMEs that reported a skills gap, nearly 40% said generative AI helped compensate for it; one quarter said it helped compensate for a worker shortage. These are self-reported findings across sectors, not a count of engineering jobs filled or engineers replaced. OECD, “AI and skills,” 2025.
The distinction matters: a tool might help an existing engineer complete some tasks or work around a specific skills gap without providing the professional judgment, experience or accountable workforce needed to deliver an engineering project. The cited figures do not tell us how much time was saved, whether the assistance improved outcomes, or whether it reduced a particular engineering employer’s need to recruit.
AI can change the engineering skills mix
AI may ease some capacity constraints while increasing demand for people who can build, integrate, secure and verify AI-enabled systems. In the IET’s UK employer survey, automation and cybersecurity were each named by 38% as digital skills needed for growth. Data engineering was named by 34%, and software engineering by 33%. The same summary says 30% reported lacking automation skills, while 17% reported difficulty recruiting for data and software engineering roles as well as cybersecurity roles. The results point to a mix of recruitment and skills-development challenges, not just a shortage of workers in general. IET, 2025.
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Software engineering illustrates this shift, but should not stand in for every engineering discipline. Gartner’s October 2024 release reported an analyst forecast that 80% of the engineering workforce would need to upskill through 2027. The forecast was based on a fourth-quarter 2023 survey of 300 organizations in the United States and United Kingdom. In that survey, 56% of software engineering leaders rated AI/ML engineer as the most in-demand role for 2024 and identified applying AI/ML to applications as the biggest skills gap. These are software-engineering findings and a forecast, not a verified 2027 outcome for civil, mechanical, electrical or engineering work as a whole. Gartner, 3 October 2024.
European workforce planning also reflects a changing mix of needs. The Engineers for Europe 2025 skills strategy identifies shortages in areas including electrical and electronic engineering, ICT, and agronomic and environmental engineering, alongside capabilities such as AI, data, cybersecurity, renewable energy, sustainability and analytical problem-solving. It is a European strategy document, not a worldwide vacancy count. Engineers for Europe, Skills Strategy 2025.
Why adoption and training determine whether AI helps
AI’s potential depends on whether employers can make it useful in real workflows. The IET survey found that half of respondents cited lack of time as a barrier to upskilling or reskilling, while 46% said employee turnover hindered progress. The OECD review adds that skills were the main reason for not adopting AI for around 40% of employers in manufacturing and finance that had not adopted AI, and for more than half of SMEs not yet using generative AI. These cross-sector figures show how limited skills can obstruct adoption as well as create demand for it. The OECD also reports that more than half of workers using AI said their employer funded training, and trained AI users were more likely to report positive outcomes.
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This creates a practical constraint: organizations facing skills shortages may also lack the time, people or processes to introduce AI well. Buying or enabling a tool is only one step. Employers need to identify suitable tasks, train staff, adapt processes and make clear who checks and owns the resulting work.
Why AI does not remove the need for engineering judgment
Engineering work varies by discipline and task. Software development, design, analysis and documentation may involve different opportunities and assurance needs from field work or physical operations. Even where AI assists with a task, outputs that affect safety, reliability or the public need appropriate review and professional accountability.
The National Academies of Sciences, Engineering, and Medicine describes AI as a general-purpose technology whose future development remains uncertain. Its 2025 report notes that current AI systems can produce incorrect answers, show bias or fail to reason correctly from facts. It also warns that AI can improve worker outcomes or displace workers, and that benefits will likely require complementary investment. As the report 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 software engineering specifically, Gartner analyst Philip Walsh said that “human expertise and creativity will always be essential to delivering complex, innovative software.” That observation is scoped to software, but the broader point is relevant to responsible engineering use: AI assistance does not itself take responsibility for whether a consequential design or decision is sound.
What can—and cannot—be concluded about a worldwide shortage
The evidence supports a cautious conclusion: AI can help with some tasks and may compensate for certain skills gaps, but the cited sources do not quantify the global engineering vacancy gap or estimate what share AI could close. They combine different kinds of evidence—reported adoption, employer expectations, self-reported SME experiences and analyst forecasts—which cannot be merged into a causal estimate of jobs filled or shortage reduced.
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Engineering demand and the usefulness of AI can vary by geography, specialty, experience level and project pipeline. The available findings make those distinctions important, but do not provide a comparable worldwide measure for them. For broader context on workforce effects, the National Academies’ Artificial Intelligence and the Future of Work is a 2025 assessment of AI and work, not an engineering manual or a proposed solution to the shortage.
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