AI is likely to change more than the speed at which engineering work can be completed. As coding, modelling and analysis become easier to accelerate, aerospace organisations also need to consider how engineers continue to build their own knowledge and judgement.
This issue is at the heart of a recent episode of The Innovation Crowd, a podcast hosted by innovation specialist Helen Dawson, featuring Jonathan Burnip, Head of Advisory Services at Marshall Aerospace.
In the final article in our three-part series unpacking Helen and Jonathan’s discussion, we explore what AI could mean for early-career development, technical judgement and the way engineering organisations train and support their people.
Jonathan Burnip is Head of Advisory Services at Marshall Aerospace and a Fellow of the Royal Aeronautical Society.
The Advisory Services team draws on Marshall Aerospace’s experience gained through complex engineering and integration programmes to help innovators address new technical and organisational challenges.
A faster route through technical work
As Jonathan explains, engineers often develop across two broad dimensions: depth within a particular technical discipline and breadth across a wider knowledge system.
Technical education provides a firm theoretical foundation, while early-career roles allow engineers to apply that knowledge through detailed practical work. Broader understanding tends to develop with exposure to larger and more complex programmes, where engineers see how different disciplines interact.
Jonathan sees his own career as following this broad pattern: his early work in areas including structures and fatigue modelling helped build technical depth, while subsequent roles exposed him to increasingly complex problems and a wider understanding of systems.
AI has the potential to alter the pace of that progression: since tasks like coding and model building are areas where the technology can already provide substantial assistance, this could allow (or, depending on your perspective, oblige) engineers to explore far more scenarios during the same period.
“It’s reasonable to assume that early-career engineers will actually come through the learning curve much quicker.”
An engineer who might previously have investigated one or two possible configurations could potentially compare many more, giving early-career engineers exposure to a broader solution space. However, Jonathan cautions, faster technical work still needs to contribute to a genuine understanding of the system being analysed.
Knowing whether the answer can be trusted
Producing a result is only one part of engineering. Engineers also need to know why a result is credible, what assumptions sit behind it and whether it presents an appropriate solution to the actual problem.
Jonathan expects critical thinking, systems thinking and safety thinking to take on greater importance within the engineer’s role as routine technical activity becomes easier to accelerate.
“I think what probably remains for those early-careers engineers is their problem-solving, critical thinking, and safety thinking, alongside technical depth.”
First-principles knowledge remains particularly important. Engineers need enough technical depth to understand and evaluate results in context, identify where models may be weak, and recognise results that potentially conflict with a system’s underlying physics or behaviour.
The ability to explore many more options also places greater emphasis on selection: examining twenty configurations creates little value if there is no sound basis for deciding which of these configurations merit further development.
Engineering work may therefore shift gradually from producing individual answers towards critically evaluating a larger range of possible answers and understanding which can genuinely be trusted.
Preserving the learning
On one hand, there is no reason to retain inefficient ways of working simply because previous generations of engineers had to use them. For example, removing repetitive activity can free people to spend more time on problems that demand judgement and technical insight.
At the same time, some of the activities that are now becoming easier to automate have traditionally played a role in building that judgement.
Constructing a model from scratch teaches more than the mechanics of producing a finished output: debugging exposes assumptions and errors, changing variables helps an engineer develop an intuitive understanding of system behaviour, and unexpected results create a reason to investigate what is happening underneath.
“We’ll probably be asking people to spend less time doing the work and more time thinking, was it the right work? Is it the safe work?”
As tools handle more of the intermediate activity, organisations will need certainty that the human learning contained within that work is preserved. This does not require deliberately slowing engineers down, or withholding useful technology; it does, however, require more deliberate development of the knowledge needed to scrutinise the outputs those tools produce.
A different approach to coaching
The role of experienced engineers, managers and mentors is likely to evolve alongside these changes.
Traditional early-career development often relies on repetition and progressively greater responsibility. Engineers learn how to complete a task and, through repetition and variation, how to gradually apply the same principles to more difficult problems.
When technology compresses part of that process, coaching can place more emphasis on the reasoning behind the work.
“We may train people to ask the right questions, to understand why something is safe and why I can rely on it.”
Jonathan sees the emphasis moving towards the engineer’s ability to explain the assumptions behind a model, the evidence supporting a conclusion and the way an individual element interacts with the wider system. Coaching therefore becomes more focused on building the judgement needed to challenge and interpret results.
In short: for employers, greater access to powerful tools therefore must to be accompanied by equally strong engineering development.
What future engineers will need
As explored above, AI is likely to change the balance of work undertaken by aerospace engineers rather than remove the need for engineering expertise.
Some activities will become faster, allowing engineers to explore more configurations and analyse larger problem spaces. More time may be spent defining problems, questioning results, understanding interactions and deciding whether the available evidence supports a particular course of action. Jonathan sees that judgement as a lasting part of the engineer’s contribution.
“The real human value, I think, that remains is that understanding of the problem space and that ability to say, ‘Okay, so I’ve now done enough. I trust this. It’s safe. We can move forwards.’”
Strong technical foundations remain essential to reaching that point. So does the ability to think across systems, challenge assumptions and communicate why a conclusion deserves confidence.
For organisations, human technical capability will continue to matter, along with the ability to use increasingly capable tools without surrendering the judgement required to supervise them.
The aim is not to preserve an older model of engineering work unchanged, but to make sure new tools allow engineers to spend more time on higher-value thinking without losing the experience and understanding on which that thinking depends.
This completes our three-part series unpacking Helen and Jonathan’s discussion, covering effective AI adoption in military aerospace, responsible use in safety-critical environments and its potential impact on engineering careers.
You can listen to Jonathan’s full conversation with Helen Dawson on The Innovation Crowd.
Images used in this article were created using generative AI.