Could generative AI help to fill the skills gap in engineering?

OpenAI’s generative AI tool ChatGPT has been in the market for two years now, and as more competitors enter the field, GenAI technology is being adopted across various industries, including engineering. However, questions linger regarding its effectiveness and suitability.

The engineering sector, which comprises nearly a fifth of the UK workforce and contributed £646bn to the economy in 2022, is currently witnessing a resurgence after a slowdown during the Covid-19 pandemic. Despite this positive trend, the sector faces a challenge as experienced engineers retire early, potentially leading to a loss of critical skills. To address this, major engineering companies like Rolls-Royce and BAE Systems are establishing skills academies to train new employees, while the government is promoting apprenticeships.

Some companies are exploring the use of artificial intelligence (AI) to help mitigate the skills shortage by enabling experienced engineers to utilize their time more efficiently. A survey conducted by Professional Engineering, the magazine of the Institute of Mechanical Engineers (IMechE), in the summer of 2024 shed light on the adoption and obstacles of AI in the sector, focusing specifically on mechanical engineering but offering insights applicable to the broader engineering industry.

Despite some reservations, the deployment of generative AI has been relatively swift in the past two years, with tools like ChatGPT being easily accessible without requiring specialized hardware. Alan King, head of global membership development strategy at the IMechE, emphasizes the potential of AI in engineering but also underscores the need for safeguards to prevent mishaps in a profession as critical as engineering.

The engineering sector is heavily regulated, with various rules, standards, and regulations that serve as guidelines for AI implementation. The survey revealed that a significant percentage of companies have integrated AI tools into their engineering teams, with a focus on large language models (LLMs) and machine learning to enhance productivity.

Engineers are keen on utilizing AI for tasks like simulation, design optimization, and research to improve efficiency. While AI is expected to automate mundane tasks and enhance productivity, there are concerns about job replacement, reduced project oversight, security risks, and potential biases in AI-generated solutions.

Looking ahead, the deployment of AI tools in engineering presents both opportunities and challenges. Companies need to balance the benefits of AI automation with the necessity for skilled engineers to oversee and validate AI-generated solutions. It is crucial for engineers to develop new skills in coding and prompt engineering to effectively collaborate with AI systems and maximize the technology’s potential.