READ: "Promise and peril as engineering firms roll out AI"
Less than two years after the release of ChatGPT, a survey by Professional Engineering and IMechE has found that AI and AI-related tools are already being used at more than two-fifths (41.9%) of respondents’ organisations, with plans to do so at a further 21%.
We spoke to Alan King, IMechE’s head of global membership strategy and author of recent paper “Harnessing The Potential of AI in Organisations”, about what the results mean for the industry.
What were your initial thoughts after seeing the results, which showed more than three-fifths of respondents feel positive about the introduction of AI in engineering?
I thought it was a very positive outlook. That optimism probably reflects the industry's readiness to embrace these kinds of innovative technologies. Hopefully they can tackle some of the major challenges that, as a society, globally, we face around issues like global warming, advancing nuclear fusion, and plastic pollution.
I wasn't necessarily sure if we'd see that in the survey. You never quite know, because when I talk to people it can often be quite a mixed bag. So I thought it was good to see the engineering community seeing it so positively.
What about the current adoption levels in engineering?
That again seemed quite positive, maybe it was slightly higher than I would have anticipated. In my own conversations with organisations, I don't always get a sense that they've moved that quickly. ChatGPT landed nearly two years ago now, November 2022, and I still feel organisations are at the thinking stage – but actually I thought the levels were surprisingly high, and quite positive.
There are certain tools that have a low barrier to entry, low friction to implement into the business. And also I think engineering organisations in general are a little bit more inquisitive than some other sectors. So perhaps they're more ready to adopt.
The results showed that some engineers are concerned about a loss of expertise in the profession as AI technologies are introduced.
We saw something similar years ago in the energy sector, with nuclear. There was a time in the UK when we'd lost a lot of our skills around nuclear, because we largely stopped building nuclear power stations. If you went to France, there were plenty of nuclear engineers, but in the UK there was a bit of a gap. That's been addressed now, with organisations like EDF Energy bringing their skillsets from France to the UK. But you could see something similar happen in AI, potentially, where if it starts doing too much then you lose that future capability.
Engineers also don’t necessarily trust organisations to implement AI in their best interests.
There is a definite need for organisations to be really transparent and communicate what their strategies are around this, and to reassure them that their roles in an AI-augmented future would be safe – that it's there to be a co-pilot with them, not simply to replace them.
That might be the case in some companies, but others might use AI to reduce job numbers where they can…
There's probably a certain degree of inevitability there. Commercial organisations follow the money, don't they? But I think there's an opportunity for a balanced approach – for businesses to look at it across the board and say ‘It’s not just about cost savings.’ [They] should be looking at how we can actually do more with this technology and accelerate the capability of the organisation, rather than just saving some money.
Is there a risk that the companies that are too slow on the uptake will fall behind?
I think that's definitely possible. I think what we'll see is smaller organisations prepared to take greater risks, and then perhaps the more established market leaders might take a more measured, cautious approach.
There are plenty of companies that were around at the dawn of the Internet that aren't around today, arguably because they didn't adopt the technology quickly enough.
I think there is a sweet spot to be found for organisations, where it's done in a way that's measured and acceptable, but doesn't leave the company looking like a legacy company.
We're not going to have a future in 10 years, 20 years’ time where AI isn't doing anything in engineering. All organisations need to start thinking about how they move towards that future, because it is coming.
Respondents said engineers will need to develop prompt engineering, coding and fact-checking skills – would you agree with that?
Absolutely. I think these are new skillsets that individuals in engineering companies and beyond need to start thinking about, because there's no doubt that, in a way, the spoken language for those individuals will become the programming language of the future.
Engineers and others will talk to systems, and those systems will build, develop and create what that individual is asking it to do. They won't need to use Python or other programming languages… it will be done through iteration, through conversation.
Any individual that starts [developing AI-related skills] now, that's going to give them a natural advantage over the coming years. In this early phase of AI development, it won't be the AI that replaces you, it will be people who know how to use the AI systems well.
What are the potential risks of reduced oversight of AI from human engineers?
In the current state of AI development, it’s absolutely critical. Oversight remains indispensable. Engineers bring contextual understanding and ethical judgement, which at the moment AI systems would struggle to replicate.
Organisations need to emphasise the need for a human-AI integrated approach, working together as a co-pilot is really important. Any organisation that doesn't do it that way could risk some severe problems in the future.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.