Engineering Technologists and Technicians, Except Drafters, All Other

In labs and plants, new tools are helping with data work but technicians still play the lead role. For example, research shows AI models can process fuel-cell sensor data (like voltages, currents, pressures) to flag problems much faster than simple spreadsheets ^[1]. This means some of the “document or analyze fuel cell test data” work is now augmented by AI – machines suggest patterns or make predictions, and humans review and decide.

By contrast, tasks like installing or testing a spark-ignition or diesel engine are mostly still done by people. Factories may use robots to lift heavy parts, but a technician’s judgment is required to hook up the engine, calibrate it, and interpret test results. In short, data analysis in this field is increasingly supported by AI, while hands-on engine work remains largely manual.

How quickly AI is used depends on several factors. Cost is one big reason: BLS data shows these technicians earn about $36 per hour on average ^[2]. For low-volume or complex tasks, buying expensive robots or custom AI may not pay off, so adoption can be slow.

On the other hand, inexpensive software for data analysis (spreadsheets with smart features or cloud tools) is widely available, which can help labs automate routine charting and reports. Economically, AI can save time and catch problems early ^[1], which is a benefit where fuel-cell testing or engine tuning is critical. Also, as vehicles shift toward electric and clean energy, companies may invest more in AI for new technologies (fuel cells, batteries, hydrogen) than in old gas engines.

Socially and legally, there aren’t big barriers: using AI to study test data is generally accepted, and engineers still do the final checks. In all, young technicians should know AI might handle boring data tasks, but human skills – understanding results, fixing unexpected problems, and working with engineers or customers – remain very valuable.