Skip to content
man bot
Compare

Plant and machine operators, and assemblers · code 8152

Weaving and knitting machine operators

The looms run automatically. Working out why one stopped is still a person's job.

AI barely touches any of the thirteen tasks, from setting up knitting machines and tending looms to replacing needles and oiling machines.

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

Weaving and knitting machine operators set up, operate and monitor weaving and knitting machines which process yarn or thread into woven, non-woven and knitted products such as cloth, lace, carpets, industrial fabric, hosiery and knitted garments, or to quilt and embroider fabric. Tasks include - (a) setting up and operating batteries of automatic, link-type knitting machines to knit garments of specified pattern and design; (b) threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing; (c) tending automatic looms that simultaneously weave pile yarn, filling yarn and warp yarn material to produce carpets and rugs with various coloured designs; (d) operating and monitoring looms on which yarn or twist is intersected and knotted at regular intervals to form mesh; (e) operating and monitoring large automatic multi-needle machines to embroider material or to sew lengths of several layers of material to make yard goods, quilts or mattress coverings; (f) tending circular knitting machines with automatic pattern controls that knit seamless hose; (g) operating and monitoring knitting machines to knit hosiery to shape of foot and leg; (h) operating and monitoring machines for knitting heels and toes of socks into ribs or tops cut from circular fabric; (i) operating and monitoring machines which seam openings in toes of socks; (j) operating and monitoring crochet machines to knit lace, trimming, etc. of desired patterns or design; (k) examining looms to determine causes of loom stoppage, such as warp filling, harness breaks or mechanical defects; (l) repairing or replacing worn or defective needles and other components; (m) cleaning, oiling and lubricating machines, using air hoses, cleaning solutions, rags, oil cans and/or grease guns. Examples of the occupations classified here: - Carpet weaving machine operator - Knitting machine operator - Net production machine operator - Weaving machine operator Some related occupations classified elsewhere: - Carpet weaver - 7318 - Cloth weaver - 7318 - Knitter - 7318

  • textile machine operator
  • knitting machine operator
  • weaving 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

87 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 13 tasks for how much of it AI could do. They average 16.5 out of 100, so it holds up at 100 − 16.5 = 83.5.

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?

Weaving and knitting 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.

  • Pattern programming on machines.
  • Monitoring many machines at once.
  • Production and quality records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Finding why a loom stopped, and fixing it.
  • Threading and setting up machines.
  • Replacing needles and worn parts.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising more automated knitting and weaving.
  • Seamless and made-to-order knitwear.
  • Recycled yarns and new fibres.

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

61 How
hands-on
37 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 50 · hand skill 44.6 · cramped spaces 16.8 · 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 22, against an average of 17. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 22

    Setting up and operating batteries of automatic, link-type knitting machines to knit garments of specified pattern and design

    Very Low
  2. 20

    Tending automatic looms that simultaneously weave pile yarn, filling yarn and warp yarn material to produce carpets and rugs with various coloured designs

    Very Low
  3. 20

    Examining looms to determine causes of loom stoppage, such as warp filling, harness breaks or mechanical defects

    Very Low
  4. 18

    Operating and monitoring large automatic multi-needle machines to embroider material or to sew lengths of several layers of material to make yard goods, quilts or mattress coverings

    Very Low
  5. 17

    Operating and monitoring loom on which yarn or twist is intersected and knotted at regular intervals to form mesh

    Very Low
  6. 17

    Tending circular knitting machines with automatic pattern controls that knit seamless hose

    Very Low
  7. 17

    Operating and monitoring knitting machines to knit hosiery to shape of foot and leg

    Very Low
  8. 16

    Operating and monitoring machine which seams openings in toes of socks

    Very Low
  9. 16

    Operating and monitoring machine for knitting heel and toes of socks into ribs or tops cut from circular fabric

    Very Low
  10. 16

    Operating and monitoring crochet machine to knit lace, trimming etc. of desired pattern or design

    Very Low
  11. 13

    Threading yarn, thread and fabric through guides, needles, and rollers of machines for weaving, knitting or other processing

    Very Low
  12. 13

    Repairing or replacing worn or defective needles and other components

    Very Low
  13. 12

    Cleaning, oiling, and lubricating machines, using air hoses, cleaning solutions, rags, oil cans, and/or grease guns.

    Very Low

13 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, but only partly: not everyone in it does this job. 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 7117

So, should weaving and knitting machine operators be worried?

Short answer: not really. Put the kettle on.

Not about AI. Every task rates Very Low; the most exposed is setting up and running knitting machines. Textile machines are already largely automatic, so the work is setting up, fixing stoppages and keeping quality right.

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 →