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Plant and machine operators, and assemblers · code 8112

Mineral and stone processing plant operators

AI can write up the shift. Running a crusher plant is still hands-on.

Recording shift output and checking processed stone are mostly out of AI's reach. It barely touches eight of the ten tasks, including lining stone up on saws and stacking the finished product.

What they actually do (and what else they get called)

Mineral and stone processing plant operators monitor and operate machinery and equipment for processing rocks, minerals and stone to recover refined products for immediate use or further processing. Tasks include - (a) setting up and operating stationary plant and machinery which grind, crush, cut, saw and slice rocks, minerals and stones according to the specifications for the job; (b) positioning blocks and slabs of stone onto machines for sawing, cutting and further working; (c) monitoring and maintaining the flow of unprocessed rocks, minerals and stones from conveyors into machines; (d) operating washing, separating, leaching, precipitating, filtering, extracting and combining equipment to remove waste material and recover minerals; (e) combining mineral ores with solvents to facilitate further processing; (f) separating metal and mineral concentrates from ore and alluvial deposits by thickening, flotation, gravity separation, filtration, or magnetic and electrostatic separation; (g) observing meters, gauges and control panels, adjusting valves and controls to ensure the safe and efficient operation of equipment, detecting malfunctions and assisting with plant and machinery maintenance and repair; (h) examining processed materials visually or with hands to ensure compliance with established standards and job specifications, and collecting samples for testing in laboratories; (i) recording information about processing completed during shifts, such as quantities, types and dimensions of materials produced; (j) sorting, stacking and moving processed minerals and stone for packaging, further processing or shipping. Examples of the occupations classified here: - Coal washer - Crusher operator (mineral or stone processing) - Flotation worker (mineral processing) - Gold precipitator - Machine operator (stone cutting or processing) - Milling machine operator (minerals) Some related occupations classified elsewhere: - Stone cutter (hand or hand-powered tools) - 7113 - Stonemason - 7113 - Stone polisher (hand or hand-powered tools) - 7113 - Stone splitter (hand or hand-powered tools) - 7113 - Machine operator (cast stone production) - 8114 - Stone polishing machine operator - 8114

  • stone driller
  • stone planer
  • mineral processing operator
  • mineral crushing operator

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

79 / 100

Mostly holds up against AI

AI can take a few chores off your plate, but most of the day is still yours.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

155 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 10 tasks for how much of it AI could do. They average 20.8 out of 100, so it holds up at 100 − 20.8 = 79.2.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 67 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Mineral and stone processing plant operators 79

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Shift records and production reports.
  • Automated process control.
  • Quality sampling data.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Running and adjusting processing plant.
  • Keeping equipment safe and running.
  • Handling materials and stone.

Both

What's arriving

New work that only exists because the AI does.

  • Remote plant monitoring.
  • Processing minerals for batteries.
  • Water and waste reduction.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

33 could do without you

It could take over very little of the work on its own. It could help with 59 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

67 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 42 out of 100. The higher it is, the longer the robot waits.

67 How
hands-on
42 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 2 US jobs .

finger skill 42.9 · hand skill 42.9 · cramped spaces 39.7 · averaged · rule: hands-on 50+ · fiddly 39.4+

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 46, against an average of 21. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 46

    Recording information about processing completed during shifts, such as quantities, types and dimensions of materials produced

    Low
  2. 30

    Examining processed materials visually or with hands to ensure compliance with established standards and job specifications, and collecting samples for testing in laboratories

    Low
  3. 23

    Observing meters, gauges and control panels, and adjusting valves and controls to ensure the safe and efficient operation of equipment, and detecting malfunctions, and assisting with plant and machinery maintenance and repair

    Very Low
  4. 20

    Separating metal and mineral concentrates from ore and alluvial deposits by thickening, flotation, gravity separation, filtration, and or magnetic and electrostatic separation

    Very Low
  5. 19

    Monitoring and maintaining the flow of unprocessed rocks, minerals and stones from conveyors into machines

    Very Low
  6. 16

    Setting up and operating stationary plant and machinery which grind, crush, cut, saw and slice rocks, minerals and stones according to the specifications for the job

    Very Low
  7. 16

    Operating washing, separating, leaching, precipitating, filtering, extracting and combining equipment to remove waste material and recover minerals

    Very Low
  8. 15

    Combining mineral ores with solvents to facilitate further processing

    Very Low
  9. 13

    Sorting, stacking and moving processed minerals and stone for packaging, further processing or shipping.

    Very Low
  10. 11

    Positioning blocks and slabs of stone onto machines for sawing, cutting and further working

    Very Low

10 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
59
AI does it for you
33

Sidekick wins, by 26 points.

How good a match is this?

One Australian job matches this one closely, so this is about as reliable as it gets here.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 7129

So, should mineral processing operators be worried?

Short answer: not much. Some of the chores will change, and good riddance.

The records are moving. Recording shift output and examining materials are the most exposed tasks, both Low; running plant and separating minerals rate Very Low. Process control is increasingly automated, so operators supervise and fix the plant.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 39 jobs in the “Plant and machine operators, and assemblers” family, averaging 80. This one is below the family average.

Pit this against any other job →