AI is Transforming Engineering – But How Do We Avoid the Pitfalls?

The Growing Influence of AI in Engineering

Artificial intelligence (AI) is revolutionizing multiple industries, and engineering is no exception. However, is its rapid adoption outpacing the sector’s ability to safeguard quality and safety? MM Engineering director Chris McDermid explores the best way forward.

AI's impact on engineering cannot be understated. According to Deltek’s 2024 Clarity Trends for Architecture, Engineering, and Consulting Firms report, 97% of engineering firms now use AI and machine learning. From streamlining administrative tasks to enhancing product design, AI is already an indispensable tool, and its capabilities continue to evolve rapidly.
 

AI in Engineering Today

At MM Engineering, we leverage AI for various operational functions, particularly to enhance efficiency. Generative AI, which Deltek found to be used by 92% of engineering firms, enables rapid content creation and data analysis. This is invaluable for producing technical documents, assembling information, and answering complex queries quickly.
This is especially beneficial in a project-based environment like ours. Since we work on bespoke designs rather than mass-producing identical products, we constantly manage evolving specifications. With multiple projects running simultaneously, manually retrieving and cross-referencing specification details can be time-consuming. AI helps streamline this process, allowing us to upload and analyse specification documents efficiently, saving significant time and effort.

The Role of Machine Learning in Engineering

While AI accelerates operational workflows, machine learning—particularly in the form of parametric modelling—is another game-changer. Parametric modelling allows engineers to automate aspects of the design process, which is especially useful when producing custom products.

Every project at MM Engineering follows a structured design process: information capture, drawing creation, customer approval, and final construction. While each product is tailored to client needs, they are typically modifications of our proven core designs. Parametric modelling enables software to apply these modifications automatically, handling calculations such as flood door dimensions, internal matrix configurations, and hole placements with precision.

 

The Future of AI in Engineering

Parametric design itself is not necessarily AI-driven; at its core, it is a form of computer-aided design (CAD) that executes pre-programmed instructions without deviation. However, the integration of AI with parametric modelling is an emerging trend. The question is: will AI soon progress beyond automating tasks to actively evaluating and suggesting product design improvements?

If so, this could significantly impact clerical and technical roles in engineering. While hands-on manufacturing roles are unlikely to be replaced by AI anytime soon, office-based technical positions may face increasing automation. This shift poses both opportunities and risks for the industry.

The Pitfalls of AI in Engineering

One of the primary concerns surrounding AI is the potential loss of highly skilled jobs. As AI tools become more sophisticated in answering technical questions and assisting with design processes, firms may begin reducing their technical workforce.

Cost savings are a driving force behind AI adoption, but reducing human oversight could lead to quality and safety issues. In sectors like flood defence, where reliability is paramount, cutting corners in technical expertise could have severe consequences. The last thing the industry needs is an influx of substandard products that fail under real-world conditions.

Unfortunately, I have already encountered engineering drawings that exhibit signs of AI generation—lacking the depth of expertise and attention to detail that a skilled engineer would provide. Many people have seen similar inconsistencies in AI-generated text and images online, which often contain subtle but critical errors. In engineering, such shortcomings could lead to serious safety risks.

For structural and safety-critical applications—which encompass most engineering projects—AI-generated outputs must be subject to rigorous scrutiny. The potential for design flaws with real-world consequences underscores the need for continued human oversight.

 

The Role of Regulation and Insurance

It remains to be seen how the insurance sector will respond to AI-driven engineering decisions, particularly concerning professional indemnity coverage. If cost pressures push firms toward greater AI reliance, insurers may demand additional safeguards or refuse to underwrite AI-generated work without human validation. While fully automated engineering design might one day become insurable, that reality is likely still a long way off.

How AI Can Benefit Engineering Without Risking Quality

The engineering sector is at a crossroads. The key is to harness AI's advantages while preserving the expertise required for quality assurance and safety. Used wisely, AI can free engineers from tedious, repetitive tasks, allowing them to focus on high-value activities that require human judgment and experience.

For small and medium-sized enterprises (SMEs), AI presents a major opportunity. Many SMEs struggle to allocate resources for detailed environmental impact assessments or carbon tracking. AI-driven automation in these areas could be a game-changer, helping firms meet regulatory requirements and improve sustainability efforts without overstretching their teams.

Beyond environmental tracking, AI could also enhance quality management, health and safety compliance, and operational efficiency. By strategically integrating AI into these areas, companies can optimize performance without sacrificing the human expertise that ensures products remain safe, functional, and reliable.

 

Conclusion

AI is a powerful tool, but it must be deployed responsibly. While it can drive efficiency and reduce costs, engineering firms must ensure that quality and safety remain paramount. The future lies not in replacing human expertise but in leveraging AI to support and enhance the capabilities of skilled engineers. Used wisely, AI has the potential to fill critical gaps and elevate the industry as a whole.