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Plant and machine operators, and assemblers · code 8342

Earthmoving and related plant operators

Earthmoving and related plant operators operate machines to excavate, grade, level, smooth and compact earth or similar materials. Tasks include - (a) operating and monitoring excavating machinery equipped with moveable shovels, grab-buckets or dragline buckets, to excavate and move earth, rock, sand, gravel or similar materials; (b) operating and monitoring machinery for digging trenches for sewers, drainage, water, oil, gas or similar pipelines; (c) operating and monitoring machinery equipped with concave steel blades to move, distribute and level earth, sand, snow and other materials; (d) operating and monitoring equipment to remove sand, gravel and mud from bottom of body of water; (e) operating and monitoring machines for hammering wooden, concrete or steel piles into ground; (f) operating and monitoring power rollers to compact and smooth layers of materials in making roads and pavements, and similar work; (g) operating and monitoring machines which spread and smooth concrete or bituminous or tar preparations to construct roadways, roads or similar surfaces. Examples of the occupations classified here: - Bulldozer operator - Excavator operator - Front-end loader operator - Pile-driver operator - Road-roller operator - Road surface laying machine operator - Snow-groomer operator

None of this job's tasks scored high enough to count. It ranks 49 of 427 for holding up against AI.

Also known as

  • bulldozer operator
  • surface mine plant operator
  • grader operator
  • road roller operator
  • pile driving hammer operator
  • scraper operator
  • snow-clearing worker
  • road construction worker
  • excavator operator
  • dredge operator

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

86.9
Holds up
against AI Not Exposed

AI could take on 13.1 of 100, on average
so it holds up at 100 − 13.1 = 86.9

That's the whole sum, and you can check it against the ILO's study.

77.9
How hands-on
out of 100

From O*NET, averaged across 5 US jobs

42.3
How fiddly
and cramped

finger skill 42.5 · hand skill 43.2 · cramped spaces 41.1 · averaged

Kind of hands-on work · hands-on 50+ · fiddly 39.4+ Hands-on, fiddly work in tight spaces

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.

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 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 77.9 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 15, while the average is 13.1. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 15

    Operating and monitoring equipment to remove sand, gravel and mud from bottom of body of water

    Very Low
  2. 14.8

    Operating and monitoring excavating machinery equipped with moveable shovel, grab-bucket or dragline bucket, to excavate and move earth, rock, sand, gravel or similar materials

    Very Low
  3. 14

    Operating and monitoring power roller to compact and smooth layers of materials in making roads, pavements and similar work

    Very Low
  4. 13.5

    Operating and monitoring machines which spread and smooth concrete or bituminous or tar preparations to construct roadways, roads or similar surfaces.

    Very Low
  5. 13

    Operating and monitoring machinery equipped with concave steel blade to move, distribute and level earth, sand, snow and other materials

    Very Low
  6. 11.8

    Operating and monitoring machinery for digging trenches for sewers, drainage, water, oil, gas or similar pipelines

    Very Low
  7. 10

    Operating and monitoring machines for hammering wooden, concrete or steel piles into ground

    Very Low

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

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Helped, or replaced?

The score above can't tell a tool that makes you faster from one that does the task instead of you. Jobs and Skills Australia looked at those two things separately, and this is their result, matched to this job.

2 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

AI helps you
52
AI does it for you
21.5

Helping wins by 30.5 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 7212, 7219

Compared with similar jobs

There are 39 jobs in the “Plant and machine operators, and assemblers” group, averaging 80. This one is above that.