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Elementary occupations · code 9215

Forestry labourers

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

None of this job's tasks scored high enough to count. It ranks 10 of 427 for holding up against AI.

Also known as

  • 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.

90.6
Holds up
against AI Not Exposed

AI could take on 9.4 of 100, on average
so it holds up at 100 − 9.4 = 90.6

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

71.2
How hands-on
out of 100

From O*NET, matched to one US job

35.3
How fiddly
and cramped

finger skill 41.1 · hand skill 42.9 · cramped spaces 21.8 · averaged

Kind of hands-on work · hands-on 50+ · fiddly under 39.4 Hands-on, less fiddly work

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.

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 71.2 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 12.5, while the average is 9.4. 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. 12.5

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

    Very Low
  2. 10.8

    Collecting seeds, and planting seedlings

    Very Low
  3. 10.8

    Maintaining look-out for fires in forests

    Very Low
  4. 9.3

    Clearing undergrowth in forest stands and thinning young plantations

    Very Low
  5. 9.1

    Digging holes for tree planting

    Very Low
  6. 8.4

    Stacking and loading logs and timber

    Very Low
  7. 7.5

    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 0.7-work-setting

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.8. This one is above that.