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Plant and machine operators, and assemblers · code 8342

Earthmoving and related plant operators

The excavator can now dig to a GPS plan. Someone still has to know what's under the ground.

AI barely touches any of the seven tasks, from running excavators and dredgers to digging trenches and driving piles.

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

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

  • 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.

87 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

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

49 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 7 tasks for how much of it AI could do. They average 13.1 out of 100, so it holds up at 100 − 13.1 = 86.9.

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 78 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?

Earthmoving and related plant operators 87

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.

  • GPS machine control to design levels.
  • Site plans and progress logs.
  • Fuel and maintenance records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Operating safely around people and buried services.
  • Reading ground conditions as they change.
  • Precision work in tight spaces.

Both

What's arriving

New work that only exists because the AI does.

  • Remote and semi-autonomous machine operation.
  • Working from 3D site models.
  • Electric and hybrid plant.

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

22 could do without you

It could take over very little of the work on its own. It could help with 52 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

78 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.

78 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 5 US jobs .

finger skill 42.5 · hand skill 43.2 · cramped spaces 41.1 · 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 15, against an average of 13. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 15

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

    Very Low
  2. 15

    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. 14

    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. 12

    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 0.8-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
52
AI does it for you
22

Sidekick wins, by 31 points.

How good a match is this?

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

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 7212, 7219

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Tiny sample alert This job makes up under 0.01% of all the conversations: a tiny slice, mostly the paperwork rather than the work. People using AI for a job isn't the same as AI being able to do it.

Out of every 100 conversations

52
48

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should earthmoving plant operators be worried?

Short answer: not really. Put the kettle on.

Not about AI chatbots. Every task rates Very Low. Machine control already guides blades and buckets to digital designs, and remote operation is growing, so operators increasingly supervise the machine as much as drive it.

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 above the family average.

Pit this against any other job →