It’s been two years since OpenAI launched ChatGPT, and the landscape of generative AI (GenAI) is quickly evolving. Numerous competitors have stepped into the arena, and industries like engineering are starting to adopt these technologies. Still, many are questioning whether these tools are appropriate and viable for their work.
Engineering plays a huge role in the UK economy, making up almost 20% of the workforce and contributing £646 billion in 2022. The sector is rebounding after facing setbacks during the COVID-19 pandemic. Yet, there’s a troubling trend: experienced engineers are retiring early, risking a loss of essential skills. To counter this, large companies like Rolls-Royce and BAE Systems are launching skills academies and the government advocates for apprenticeships to train the next generation.
Some firms are turning to AI to address this skills shortage. By optimizing how seasoned engineers spend their time, they hope to alleviate the gap. A survey by Professional Engineering, the magazine of the Institute of Mechanical Engineers (IMechE), delved into this topic during the summer of 2024. While the response rate was modest—125 members participated—the results shed light on how AI is reshaping the sector. Over 40% of respondents reported that their companies are currently utilizing AI tools, and another 20% plan to adopt them soon.
One of the key drivers for swift AI integration has been accessibility. Many tools are user-friendly and don’t require special hardware; for instance, accessing ChatGPT is as simple as opening a web browser. Alan King, head of global membership development at IMechE, highlighted the vast potential of AI while also emphasizing the associated risks: “We need safeguards because the potential for errors is magnified in engineering,” he said.
Regulation in engineering is stringent, with a host of rules, standards, and guidelines in place. These could help shape the ethical deployment of AI by serving as a framework for its use.
According to the survey, 58% of companies have incorporated AI within their engineering teams, while 42% use it in various business areas. The dominant tool? Large language models (LLMs), used by nearly 60% of firms. In addition, about one-third are leveraging machine learning and productivity tools like Microsoft 365 Copilot. On the other hand, more advanced tools, such as generative design and computer vision, see less frequent use.
Respondents expressed a strong interest in deploying AI for simulations and productivity improvements. Another interesting finding: approximately two-thirds believe AI will take over mundane tasks, freeing engineers to tackle more complex challenges. “AI will primarily serve as a co-pilot,” King remarked. “It can automate tedious tasks, allowing engineers to focus on more engaging work.”
However, anxiety persists. About 37% worry that AI could replace engineering jobs. More than 40% do not believe AI will maintain the current number of engineers in roles. A significant 66% fear AI’s rise might reduce oversight on projects, as these systems function somewhat like a black box, lacking transparency in how solutions are reached.
King commented on the unpredictability of AI: “In engineering, you can’t afford to operate in a Wild West environment. We need systems that deliver reliable and safe results.” The challenge lies in ensuring that AI-developed designs can be thoroughly verified. Engineers must assess AI outputs to confirm their suitability and appropriateness.
Security concerns also loom large, with over half of respondents highlighting potential vulnerabilities from AI tools, and nearly 50% pointed to issues of historical bias in data. Roughly 55% are uncomfortable with AI making critical engineering decisions, especially when using publicly available LLMs that could compromise sensitive data.
There’s strong consensus on the need for regulatory oversight in AI’s application within engineering. Speedy tech advancements often outpace slow-moving legislative processes, making this a daunting task. Some initiatives, like the EU’s AI Act, are underway, but there’s a real risk that they could quickly become outdated.
The rollout of AI in engineering is gaining momentum, but it’s fraught with challenges. As one survey participant put it: “While computers can swiftly identify patterns, there’s a risk that people might take AI results at face value without critical thinking.”
Different countries have unique engineering regulations, meaning an AI tool designed for one region may not fit seamlessly into another. “My concern is that AI becomes a cost-cutting measure rather than a means to enhance performance,” King cautioned. “If leveraged correctly, AI could lead to remarkable advancements across the board.”
While AI streamlines repetitive tasks, engineers must develop new skills to adapt. Proficiency in coding and prompt engineering will be crucial. Critical thinking will also become indispensable in navigating AI-generated solutions.