When Will AI Replace Technologues et techniciens en électrotechnique et électronique ?

The automation of Electrical and Electronic Engineering Technologist tasks is unfolding across distinct phases. While the broader impact is projected for 2034, the initial ripples of AI integration will be felt much sooner. Routine testing procedures and standard circuit assembly are the first to be affected, with automated test equipment (ATE) and AI diagnostic tools already beginning to replace entry-level functions.

Junior technicians engaged in repetitive testing and assembly tasks are likely to experience the initial wave of automation impact as early as 2030. This period will see a shift in demand towards more advanced skill sets, as AI takes over more predictable and standardized components of the job.

As we move towards the mid-to-late stages of this decade, the automation will become more sophisticated. While the executive summary doesn't provide a specific mid-wave percentage, it's implied that the 45% automation figure will be achieved through the integration of robotic assembly and advanced automated testing platforms, impacting a wider range of tasks.

The roles most susceptible to AI automation within Electrical and Electronic Engineering Technologists are those heavily reliant on routine testing procedures, standard circuit assembly, and basic troubleshooting workflows. Entry-level positions performing repetitive tasks are particularly vulnerable, as ATE and AI diagnostic tools are designed to handle these functions efficiently and at scale.

Conversely, senior technicians who specialize in complex system integration, custom modifications, and especially field service in unpredictable environments are better positioned. Their hands-on problem-solving skills in non-standard situations and the need for human oversight in regulatory contexts offer a significant buffer, allowing careers to extend potentially to 2038.

To navigate the evolving landscape, Electrical and Electronic Engineering Technologists must focus on upskilling and specializing. The vulnerability score of 61.64/100 indicates a clear need for adaptation. Embrace opportunities to work with AI diagnostic systems and advanced automated testing platforms, rather than being replaced by them.

Prioritize developing expertise in complex system integration and custom modifications. Furthermore, hands-on problem-solving skills for field service in unpredictable environments remain critical. Staying abreast of regulatory requirements and demonstrating the value of human oversight will also be key differentiators in a more automated future.