Electronics Engineers, Except Computer

AI is also helping with testing and maintenance tasks. In factories and labs, engineers use IoT sensors plus machine learning to predict when equipment will fail. This “predictive maintenance” means repairs can be done before a breakdown, saving time and repair costs ^{[2] [2]}.

Likewise, for documentation and planning, some engineers are trying tools like ChatGPT. In a research case, ChatGPT suggested good microcontrollers and sensors for an electronics project, giving beginners a quick starting point. It made some errors in details and code, though, so human engineers still checked and fixed the output ^{[3] [3]}.

This example shows AI can draft ideas and answer questions, but engineers must verify the results. Importantly, tasks that rely on human judgment – like brainstorming with colleagues or final design reviews – aren’t automated. An industry review notes that AI leaves “repetitive, resource-heavy work” to computers so engineers can focus on problem-solving and innovation ^[1].

On the other hand, not everything in electronics engineering is easy to automate, so adoption will take time. Advanced AI software and hardware tools can be expensive, and companies must train staff to use them safely. Engineers are rightly cautious: one study found ChatGPT gave plausible but partly incorrect answers (even fake references), so people still double-check AI’s work ^{[3] [3]}.

In high-stakes fields (like medical or aerospace electronics), engineers follow strict standards and won’t trust AI-generated designs without proof of reliability. Also, many tasks involve collaboration, creativity or ethical judgment – for example, deciding a safe design approach or negotiating with a client. Those human skills remain important, so even with AI tools around, companies rely on experienced engineers to oversee projects.

Overall, AI adoption in electronics engineering will likely grow steadily. Companies see that AI can automate routine calculations and data analysis, which brings economic benefits (faster design cycles, lower testing costs). Big firms are already investing in AI design software ^{[4] [2]}.

But because of training costs, industry norms, and the need for trust, AI is being added in stages. In the meantime, young engineers can view AI as a teammate – a tool that handles busywork so that human creativity and problem-solving stay at the center of the job ^{[1] [3]}.