In the past 30 years, advances in computing technology have changed the way engineers work. Few graduate engineers are successful without knowledge of programming, computer-aided design (CAD) or software modelling.

We are now on the cusp of the next revolution in engineering technology: the Artificial Intelligence (AI) revolution. Within the next generation, AI technologies from Machine Learning (ML) to Large Language Models (LLMs) will be used to solve engineering problems just as C programming and CAD are now.

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Applications across domains

Already, we are seeing AI tools being integrated into various fields. Robotic control systems are now increasingly intelligent, using live data and feedback to optimise their performance. Predictive maintenance is allowing civil infrastructure such as bridges to be repaired in good time, rather than waiting for faults to become visible. And AI-powered simulation tools are accelerating the modelling of complex aeronautical systems, taking milliseconds rather than days.

In wireless communications, ML techniques are being used to analyse network traffic patterns to optimise the allocation of time and frequency resources between different users. Tools are being developed using AI to assist the design of networks through optimal placement of transmitters in urban environments. In the future, AI agents may design optimal waveforms based on real-time knowledge of the system, rather than using pre-defined standards.

The same trend is being seen across fields, leading to fundamental changes in how engineering is practised. While conventional workflows involve calculations, design, prototyping and testing followed by many iterations of optimisation and bug fixing, AI tools can accelerate initial design or optimisation steps. Further, generative AI tools could assist in idea generation, while ML analysis of product testing can aid quality control.

Changing skills demands and career opportunities

Industry is now seeking engineers with hybrid skills – people with the deep domain knowledge of an engineering graduate, but also familiarity and fluency in AI.

The scale of opportunity is huge: over a million engineering graduates are recruited into the Indian workforce every year, and the number of jobs listing AI experience as essential or desirable is rising all the time. While many of these are in data science or computing, increasingly positions are in industrial automation, robotics, and systems.

At present, vacancies are largely split into traditional engineering companies hiring graduates with AI experience, and AI software companies who want to bring their tools and expertise into traditional fields of engineering. In the former, the appointee may be one of only a few engineers in the organisation with AI skills, giving that individual great opportunities for career development and showing value to the company. Similarly, a hybrid engineer in a software company can communicate clearly with industrial clients and understand their problems, which also enhances career advancement.

As AI tools become further embedded across engineering, those candidates with a mixture of skills will become the senior engineers and managers of the future. This poses a problem for educators: How can we prepare our students to be leaders in this AI future?

Evolution in engineering education

As with changes brought about by computers, universities are responding to industrial needs for AI skills in all engineers. Modules in areas such as machine learning, agent-based modelling and data science are being offered to a wider pool of undergraduate engineers. While this has started with control and electronics degrees, which overlap with AI fundamentals, they are increasingly included in industrial engineering and robotics programmes. Long-term some of these modules may be integrated into courses for other engineering disciplines.

Specialised master’s courses in AI for engineering are also starting to emerge. These offer the opportunity for engineers from different disciplines to develop skills in AI technologies, making themselves well-placed to advance in the next-generation engineering workplace.

AI promises to disrupt the way an engineer works, but as with computers before them, the engineers themselves will still be needed. The future leaders of this profession will be defined by being able to work across boundaries, applying AI to solve complex real-world challenges and leading innovation in a tech-driven world.

The writer is a lecturer in wireless communications at the University of Sheffield, UK.