Can AI take the heavy lifting while engineers keep the wheel?

The potential of artificial intelligence (AI) is hard to ignore. It can accelerate complex workflows, uncover insights buried deep in system data and support engineers in making more informed decisions. However, to ensure the safety of critical systems it is essential to identify where AI truly provides value, and to understand the requirements which maintain ethical and technical oversight.

 

Accelerating engineering in complex systems

 

AI performs best in environments where analysis of large unstructured data repositories is required to provide data-driven insight. In aerospace and automotive engineering, for example, AI-driven analytics can spot early warning signs of hardware degradation long before a fault light flashes. Predictive maintenance is a standout success story. Using operational data already available, AI can forecast component failures with impressive accuracy.

 

This is more than theory. Expleo has demonstrated this in practice with a global carmaker, achieving 99 per cent accuracy in predicting onboard charger failures. That’s not just a number; it’s fewer breakdowns, fewer emergency fixes and fewer engineers wondering why the system gremlins always strike at 3 am.

 

Practical use cases across the lifecycle

 

AI’s value touches every stage of the engineering lifecycle. In embedded systems, AI can help teams shift from reactive testing to proactive design. By analysing requirements and flagging inconsistencies early on, AI can support engineers in building robust systems from the start. It doesn’t replace the engineering team but supports them in creating innovative solutions with increasing speed and reliability.

 

Later, during testing and validation, AI can generate unit test cases and link system tests back to requirements. In industries governed by strict standards such as ISO 26262, that means fewer headaches, more traceability and an easier time tracking what needs to be proven, and to whom.

 

Reducing complexity and cognitive load

 

Engineering increasingly involves managing more data than any human could reasonably sift through on their own. AI’s ability to process, filter and prioritise huge volumes of information gives teams a welcome reduction in cognitive overload.

 

AI does not replace human judgement; it simply clears the fog so engineers can see the runway.

 

Analysing data from digital twins

 

Digital twins are reshaping engineering by giving teams the ability to test and refine systems long before physical prototypes are built. When AI is added to the mix, digital twins level up further. AI continuously analyses their real-time data to explore failure scenarios, stress-test behaviours and predict how a system will respond to different conditions.

 

It’s like having a virtual lab assistant who never sleeps, never complains about late‑night simulations and never insists the model “worked fine yesterday”.

 

Staying in control

 

Throughout all this, human judgement remains essential. AI is a powerful tool, but it works best when it is used to enhance expertise, not override it. Engineers need to ensure that AI systems operate within safe boundaries, behave predictably and remain transparent in how they reach their conclusions.

 

Think of AI as a highly capable co‑pilot: brilliant at handling the dashboards, but not the one you want landing the plane solo in a storm.

 

If you’re interested in how AI can safely accelerate engineering without losing control, you can learn more in our upcoming webinar AI in Safety-Critical Engineering: Where It Helps, Where It Doesn’t, and How to Stay in Control. We’ll explore practical examples, real industry applications and the role AI plays in reducing complexity across the engineering lifecycle.