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Elementary occupations · code 9215

Forestry labourers

AI can map the forest from space. It can't plant the seedlings.

AI barely touches any of the eight tasks, from planting seedlings to felling trees with a chainsaw and cutting them into logs.

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

Forestry labourers perform simple and routine tasks to cultivate and maintain natural and plantation forests, and log, fell and saw trees. Tasks include - (a) digging holes for tree planting; (b) stacking and loading logs and timber; (c) clearing undergrowth in forest stands and thinning young plantations; (d) maintaining look-out for fires in forests; (e) removing major branches and tree tops, trimming branches and sawing trunks into logs; (f) operating and maintaining manual and hand-held machine saws to fell trees and cut felled trees and branches into logs; (g) collecting seeds and planting seedlings; (h) performing minor repairs and maintenance of forest roads, buildings, facilities and equipment. Examples of the occupations classified here: - Axeman/woman - Forestry labourer - Tree planter Some related occupations classified elsewhere: - Forestry worker (skilled) - 6210

  • forest worker

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

91 / 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

10 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 8 tasks for how much of it AI could do. They average 9.4 out of 100, so it holds up at 100 − 9.4 = 90.6.

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 71 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?

Forestry labourers 91

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.

  • Mapping sites and planning work from satellite images.
  • Fire detection and early warnings.
  • Recording what was planted, and where.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Working safely with chainsaws on rough terrain.
  • Planting and thinning by hand.
  • Spotting disease, damage or fire risk up close.

Both

What's arriving

New work that only exists because the AI does.

  • Large tree-planting and restoration schemes.
  • Drones for seeding and surveys.
  • Carbon and biodiversity monitoring records.

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.

– could do without you

We don't have a measure of how much of this job AI could take over.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

71 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?

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 35 out of 100. The higher it is, the longer the robot waits.

71 How
hands-on
35 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

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, matched to one US job .

finger skill 41.1 · hand skill 42.9 · cramped spaces 21.8 · averaged · rule: hands-on 50+ · fiddly under 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 13, against an average of 9. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 13

    Performing minor repairs and maintenance of forest roads, buildings, facilities, and equipment.

    Very Low
  2. 11

    Collecting seeds, and planting seedlings

    Very Low
  3. 11

    Maintaining look-out for fires in forests

    Very Low
  4. 9

    Clearing undergrowth in forest stands and thinning young plantations

    Very Low
  5. 9

    Digging holes for tree planting

    Very Low
  6. 8

    Stacking and loading logs and timber

    Very Low
  7. 8

    Operating and maintaining manual and hand-held machine saws to fell trees and cut felled trees and branches into logs

    Very Low
  8. 7

    Removing major branches and tree tops, trimming branches and sawing trunks into logs

    Very Low

8 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

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 forestry labourers be worried?

Short answer: not really. Put the kettle on.

No. Planting, clearing, felling and maintenance all rate Very Low. Satellite maps and fire detection help plan the work, but the work itself happens on foot, with saws and hand tools.

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 33 jobs in the “Elementary occupations” family, averaging 85. This one is above the family average.

Pit this against any other job →