Mechanical Engineering Technologists and Technicians

In many workshops and labs, computers and robots help with routine work, but humans still play crucial roles. For example, engineers now use AI-driven design tools to auto-generate parts and sketches. A 2025 study found such “generative” software can cut design time by about 30% ^[1].

Similarly, test data from machines is often captured digitally. New methods are turning mechanical test results into machine-readable formats so that software (even machine learning) can analyze performance ^[2]. Digital “twins” and sensor networks can monitor equipment instead of a person reading each dial ^[3].

That said, many tasks remain largely human. Machines like mills and lathes may run automatically once set up, but a technician still programs and checks them. Writing work orders or cost estimates generally uses standard software or charts, not fully automated AI.

In short, AI and automation are beginning to augment tasks (for example by helping with design or data logging), but they are not yet fully replacing technicians. Mechanical Engineering Technologists still use judgment and hands-on skill in their core duties ^{[4] [2]}.

Will these tools spread quickly? Several factors play a role. Some AI software (for CAD or data analysis) is already commercially available, so big companies can start using it now.

Industries like automotive have rapidly experimented with AI – one survey found 76% of automakers are testing AI-driven engineering tools ^[3]. However, new tech can be costly. A mechanical technician in the US earns about \$68,700 a year ^[5], so businesses must decide if AI/robot investments save enough in labor or time.

Safety and complexity also matter: factories need to be very sure AI will work correctly before relying on it. In fact, many engineering firms report they lack trained AI staff and worry about “trusting” the results ^[3]. These concerns tend to slow adoption.

Still, change is happening gradually. In the future, tools like “smart” CAD or automated data collection may become common. Human skills – such as problem-solving, creativity, and manual dexterity – remain important because they are hard for AI to duplicate.

Overall, AI is more likely to augment technicians’ work than to make their roles obsolete, and staying adaptable will help technicians thrive ^{[3] [5]}.