Australia will experience a shortage of 200,000 engineers by 2040, according to projections by Professionals Australia, a situation that could threaten economic growth and jeopardise infrastructure projects.
Already, 70% of engineers are born overseas, a number that would inevitably rise if suitable local candidates cannot be found.
To harness the many opportunities that artificial intelligence (AI) will open up, there are five main skills that engineers will need to learn, according to Robert Malkin, Senior Director at the infrastructure engineering software company Bentley Systems.
“AI can be an incredible assistant for engineers that unlocks new possibilities and helps find solutions to the many challenges society faces such as an ageing population and increasing pressures towards achieving net-zero emissions,” he said.
“So, we’re excited about finding out what’s possible and embracing developments like intelligent infrastructure to help build a better world and improve lives.”
Bentley’s industry-leading suite of products is used by professionals in 194 countries for the design, construction, and operation of roads, railways, water treatment systems, public works, mines, and industrial facilities. AI is at the heart of its approach to pioneering solutions using emerging technologies.
Futureproofing engineers
These are the five skills that Malkin believes will be critical for engineers
1. Programming skills
The staggering volumes of data generated today require advanced software platforms and programming languages such as Python and Java to gather and filter raw numbers into useful information. Most have a range of packages available and comprehensive libraries.
2. Data literacy
“AI only works when it has data to consume,” Malkin said. “So, it’s essential to devise optimum ways to feed in information from multiple sources. Only then can you utilise the outputs to meet specific needs such as identifying trends, gaining predictive insights or building digital twins.”
Intelligent software platforms can manage and combine large data sets to ensure a single source of truth exists and knowledge sharing is possible across organisations.
3. Understanding ethics and bias
AI will only respond to the information it’s given. If it contains an inherent bias, it’ll skew the response and potentially lead to misleading intelligence.
A good example of how even a small inbuilt bias can cause problems is when Amazon built an AI recruiting tool to review job applications in 2014, but had to scrap it four years later after it unfairly preferenced male candidates. The program had been fed thousands of previous applications, but most were from men, so it concluded they should be prioritised.
“Bias can be intentional or unconscious, but engineers need to proactively find ways to eliminate it if they want their findings to be representative,” Malkin said. “Ethics, meanwhile, is all about responding to any bias in a way that reflects the values of your organisation.
“For example, if you’re designing a public space, there are many ethical dilemmas to consider, such as whether you want to maximise economic opportunities, safety, traffic flows, aesthetics or something else. And AI may not have the answer.”
4. Systems thinking
Widespread digitisation has pushed connectivity to new levels, resulting in highly complex ecosystems that require a holistic approach.
“Engineers are very good at delivering projects, but in the future, they’ll have to be attuned to how every system they work on relates to wider networks,” Malkin said.
In the case of an airport, there’s a lot more going on than simply people getting on and off planes. There’s a mass transit system, rail links, bus routes, and a range of car parking options. In addition, there are information technology grids, predictive weather technology, retail space and more.
Therefore, an engineer tasked with installing a new system in just one of those areas should understand infrastructure intelligence to identify possible knock-on effects elsewhere.
5. Effective communication
To be successful, engineers must articulate the immense complexities of AI to all stakeholders, including those without a technical background.
“It’s about having management skills to engage deeply with the client or community to explain difficult concepts and, if necessary, find compromises that facilitate positive outcomes,” Malkin said.
It’s also about using software creatively to find new, imaginative ways to show how a project will eventually look. That might be through digital modelling, experiential demonstrations, noise simulations or virtual tours.
“That’s why I say to our clients that engineers should see AI — including generative AI — as an indispensable assistant that unleashes exciting new potential,” Malkin said. “And it’s only just getting started.”
