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Are we preparing engineers well enough for the age of AI?

Gaurav Mishra

Over the last few months, while attending the Executive Programme in Business Management at ISB, I have had more exposure to AI and generative AI. It has made me think seriously about how engineering work may change over the next few years.

AI can certainly help us review information, prepare documents, compare requirements and explore possible solutions faster. But engineering is not only about producing an answer. It also involves understanding the actual process conditions, questioning assumptions, assessing risk and taking responsibility for the final decision.

This becomes even more important in safety-critical areas, where a technically convincing answer may still be wrong or incomplete.

So, I have been reflecting on a broader question:

How should engineers prepare for the growing use of AI without losing the technical judgement, practical understanding and professional accountability that define engineering?

I would be interested to hear how other engineers are approaching this in their own fields.

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  • I feel that we should exercise great cautions, just as we would with any other 'latest and greatest', 'solves all problems' style solutions. Carbon fibre should have replaced steel everywhere by now. RAAC (Reinforced Autoclaved Aerated Concrete) should have replaced regular concrete every where.

    AI will be great for the sales and marketing brochures. It may even improve them (have you seen most big engineering firm's web sites? - Dull as dishwater, on a good day!).

    AI (LLMs) write better (more acclaimed average) essays, stories, narratives, and keeps on theme.

    It will only help if it has suitable source material, and that's usually lacking. We are poor at writing down coherent rationales that pass as technically competent. Intellectual property is valuable, so we avoid actually writing down the hidden tricks, or partition the information to keep the tricks away from prying eyes. And often we don't realise that our systems are designed that way [e.g. customer drawings and corporate work instructions].

    We are meant to learn from our mistakes but the write ups are often rather sparse, so AI will make the same mistakes with little to learn from. (see Sleipner A for one of the early advantages of CAD stress modelling).

  • in reply to Philip Oakley

    Thank you, Philip. Your point about source material is very relevant. Companies want faster delivery, but much of our engineering knowledge and past learning is still not properly documented. AI may save time, but its output will only be as reliable as the information available to it.

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  • in reply to Philip Oakley

    Thank you, Philip. Your point about source material is very relevant. Companies want faster delivery, but much of our engineering knowledge and past learning is still not properly documented. AI may save time, but its output will only be as reliable as the information available to it.

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