Office Machine Operators, Except Computer

Office machine operators handle copying, scanning, and similar tasks – for example, placing originals in feeders and running high-speed copiers or microfilm scanners ^[1]. Today, many machines have helpful features (auto-feeders, touchscreens, built-in OCR scanners, etc.), but most work still requires a person to set up and monitor the machine. In practice, modern offices are moving toward digital workflows – printing and paper use have fallen as more documents go online ^[2].

Experts note that digital technology has changed the information marketplace and boosted productivity, so fewer workers are needed to produce the same content ^{[2] [2]}. In other words, automation in this field so far has been about improving machines (like smarter scanners or predictive alerts) rather than full AI replacements. For now, routine tasks like loading paper or folding mail are largely done by hand, although digital tools (for example, software that auto-classifies scanned documents) help make some tasks faster and error-free ^{[1] [2]}.

Pure AI tools for these hands-on roles are still uncommon. Many duties are simple and physical, and the cost of new AI/robot solutions can be high compared to human labor. For example, the typical wage is only about $19 per hour ^[3], so it often costs less to use a person than to buy a fancy robot for feeding paper or sorting copies.

Moreover, the Bureau of Labor Statistics projects almost no growth (even a slight decline) in jobs for this occupation ^[1] – partly because digital workflows do much of the work. On the other hand, printers and copiers already include advanced automation (auto adjustments, maintenance alerts), so companies see efficiency benefits without needing “AI” per se. In large print centers, automation has greatly boosted productivity ^[2], so when it makes economic sense, businesses may adopt smarter machines.

Socially, replacing these roles with robots isn’t a big hurdle (the public isn’t using office copiers directly), but firms weigh savings against the upfront cost of AI systems. In summary, adoption is likely gradual: businesses will slowly use more software or smart features to ease work, but human judgment and flexibility remain important.