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Elementary occupations · code 9321

Hand packers

Hand packers weigh, pack and label materials and products by hand. Tasks include - (a) weighing, wrapping, sealing and packing materials and various products by hand; (b) filling bottles, cans, boxes, bags and other containers with products by hand; (c) labelling products, packages and various containers by hand. Examples of the occupations classified here: - Labeller (hand) - Packer (hand) - Wrapper (hand) Some related occupations classified elsewhere: - Labelling machine operator - 8183 - Packing machine operator - 8183 - Wrapping machine operator - 8183

None of this job's tasks scored high enough to count. It ranks 141 of 427 for holding up against AI.

Also known as

  • hand packer
  • clothing finisher

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

80.2
Holds up
against AI Not Exposed

AI could take on 19.8 of 100, on average
so it holds up at 100 − 19.8 = 80.2

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

59.4
How hands-on
out of 100

From O*NET, matched to one US job

32.7
How fiddly
and cramped

finger skill 37.4 · hand skill 44.6 · cramped spaces 16.2 · 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 59.4 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 25, while the average is 19.8. 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. 25

    Labelling products, packages and various containers by hand.

    Low
  2. 18

    Weighing, wrapping, sealing and packing material and various products by hand

    Very Low
  3. 16.3

    Filling bottles, cans, boxes, bags and other containers with products by hand

    Very Low

3 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.

One Australian job matches, but only partly: not everyone in it does this job. Treat the numbers as a rough guide.

AI helps you
53
AI does it for you
29

Helping wins by 24 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 8321

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 85.1. This one is below that.