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Plant and machine operators, and assemblers · code 8156

Shoemaking and related machine operators

Automated cutters mark out the leather. Someone still guides the stitching.

AI barely touches any of the three tasks: running machines that cut shoe parts, sew and finish them, and make bags, belts and luggage.

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

Shoemaking and related machine operators monitor and operate machines which produce and repair standard or special footwear, handbags and other accessories, mainly made of leather. Tasks include - (a) operating and monitoring machines which mark patterns and cut shoe parts; (b) operating and monitoring machines which sew shoe parts together, or edge, polish, or apply ornaments and perform finishing tasks; (c) operating and monitoring machines which produce luggage, handbags, belts and other accessories, as well as other items such as saddles, collars or harnesses. Examples of the occupations classified here: - Machine operator (footwear production) Some related occupations classified elsewhere: - Handicraft worker (leather) - 7318 - Cobbler - 7536

  • leather goods machine operator
  • automated cutting machine operator
  • footwear production machine operator
  • pre-stitching machine operator

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

84 / 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

85 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 3 tasks for how much of it AI could do. They average 16.3 out of 100, so it holds up at 100 − 16.3 = 83.7.

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 61 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?

Shoemaking and related machine operators 84

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.

  • Automated pattern marking and cutting.
  • Production planning.
  • Quality inspection with cameras.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Guiding stitching and finishing.
  • Spotting defects in leather and seams.
  • Setting up machines for new styles.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising automated cutting.
  • Made-to-order footwear production.
  • Recycled and alternative materials.

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

24 could do without you

It could take over very little of the work on its own. It could help with 56 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

61 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 39 out of 100. The higher it is, the longer the robot waits.

61 How
hands-on
39 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 46.4 · hand skill 42.9 · cramped spaces 28.2 · 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 18, against an average of 16. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 18

    Operating and monitoring machines which produce luggage, handbags, belts and other accessories, as well as other items such as saddles, collars or harnesses.

    Very Low
  2. 17

    Operating and monitoring machines which mark patterns and cut shoe parts

    Very Low
  3. 14

    Operating and monitoring machines which sew shoe parts together, or edge, polish, or apply ornaments and perform finishing tasks

    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 0.8-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
56
AI does it for you
24

Sidekick wins, by 32 points.

How good a match is this?

One Australian job matches this one closely, so this is about as reliable as it gets here.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 7117

So, should shoe and leather goods machinists be worried?

Short answer: not really. Put the kettle on.

Not about AI. Every task rates Very Low, from cutting shoe parts to sewing and finishing. Factories keep automating, so operators increasingly supervise machines and check quality.

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 →