US dataswitch to UK

Textile Knitting and Weaving Machine Setters, Operators, and Tenders

observing woven cloth to detect weaving defects, inspecting products to ensure that specifications are met and to determine if machines need adjustment, starting machines, monitor operations and making adjustments and installing level and align machine components. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 7%
staying human: 88%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 12 out of 100 (9–17 allowing for uncertainty): minimal exposure, across 19 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for textile knitting and weaving machine setters, operators, and tenders is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

What shifts is recording information about work completed and machine settings. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Set up, operate, or tend machines that knit, loop, weave, or draw in textiles. The job title says “textile knitting”, “weaving machine setters”, “operators” or “tenders”: officially one job, several names. The real job is the part underneath: threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But textile knitting and weaving machine setters, operators, and tenders is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 7%
staying human: 88%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Recording information about work completed and machine settings
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Record information about work completed and machine settings.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Writing down completed work and machine settings is simple record-keeping that software can capture and fill in.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Changing shape

1 task

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Notifying supervisors or repairing staff of mechanical malfunctions
The software now makes the first pass at supervisors, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Notify supervisors or repair staff of mechanical malfunctions.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (44–52 allowing for uncertainty): partial exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Passing on a fault report is straightforward written or spoken communication that software handles easily.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

17 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing
This work happens in the physical world: yarn, thread and fabric, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Threading yarn through guides and needles is done entirely by hand at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Inspecting products to ensure that specifications are met and to determine if machines need adjustment
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect products to ensure that specifications are met and to determine if machines need adjustment.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Checking finished product against the specification means handling the cloth and looking at the machine.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Observing woven cloth to detect weaving defects
This work happens in the physical world: woven cloth, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Observe woven cloth to detect weaving defects.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Spotting weaving faults means watching the cloth come off the loom in front of you.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Inspecting machinery to determine whether repairs
This work happens in the physical world: machinery, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect machinery to determine whether repairs are needed.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Deciding whether a machine needs repair means looking at and listening to it in person.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Starting machines, monitor operations and making adjustments
This work happens in the physical world: machines, monitor operations and making adjustments, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Start machines, monitor operations, and make adjustments as needed.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Starting a machine and adjusting it while it runs happens at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Stopping machines when specified amounts of product
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Stop machines when specified amounts of product have been produced.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Stopping the machine at the right point is done at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Removing defects in cloth by cutting and pulling out filling
This work happens in the physical world: defects, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Remove defects in cloth by cutting and pulling out filling.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cutting and pulling filling out of cloth is fine hand work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Examining looms to determine causes of loom stoppage
This work happens in the physical world: looms, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (2–10 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Working out why a loom stopped means examining the machine where it stands.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

Show the other 9 tasks

Studying guides, loom patterns, samples, charts or specification sheets or conferring with supervisors or engineering staff to determine setup requirements
staying human
The ratings behind this row put guides, loom patterns, samples well outside what today's tools can do on their own.
importance 4 · Supplemental
Source: “Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Reading patterns and specification sheets to work out the setup is document work, though plant-specific details matter.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Programing electronic equipment
staying human
This work happens in the physical world: electronic equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Program electronic equipment.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (13–37 allowing for uncertainty): low exposure, low confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Programming the pattern controller is screen work, but these systems are specialist and barely documented publicly.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Conferring with co-workers to obtain information about orders
staying human
The ratings behind this row put co-workers well outside what today's tools can do on their own.
importance 4 · Core
Source: “Confer with co-workers to obtain information about orders, processes, or problems.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the five ratings behind the score, with no single dominant reason.
The rating behind it: Getting order and process information from co-workers is quick shop-floor conversation, much of it never written down.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Adjusting machine heating mechanisms, tensions and speeds to produce specified products
staying human
This work happens in the physical world: machine heating mechanisms, tensions and speeds, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Adjust machine heating mechanisms, tensions, and speeds to produce specified products.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (4–12 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Adjusting heat, tension and speed means being at the machine and judging the result.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Setting up
staying human
This work happens in the physical world: up, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting up and running textile machinery is physical work at the machine.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Cleaning, oiling and lubricate machines, using air hoses, cleaning solutions, rags, oil cans or grease guns
staying human
This work happens in the physical world: lubricate machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning, oiling and greasing the machines is hands-on maintenance.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Operating machines for test runs to verify adjustments and to obtain product samples
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate machines for test runs to verify adjustments and to obtain product samples.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Running test batches and pulling samples is physical machine work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Repairing or replacing worn or defective needles and other components
staying human
This work happens in the physical world: worn, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Repair or replace worn or defective needles and other components, using hand tools.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Replacing worn needles and parts with hand tools is physical repair work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Installing level and align machine components, gears, chains, guides, dies, cutters or needles to set up machinery
staying human
This work happens in the physical world: level, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting and aligning gears, guides and needles is done by hand with tools.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $39,530a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 13,030in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: information in, a record out. The rows above are exactly that shape: recording information about work completed and machine settings and notifying supervisors or repairing staff of mechanical malfunctions. What it cannot do is be there in the room, and that is still where yarn, thread and fabric get done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 5% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 88% in rows it is nowhere near. That is the position, measured across 19 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. Recording information about work completed and machine settings is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to information, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding: a straight answer about what is actually being rolled out, and when
How long it takes: ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether yarn, thread and fabric are in scope or not. Nothing to log into, no license needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near notifying supervisors or repairing staff of mechanical malfunctions. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.

The roads out of here

You did not come here for a career change and I am not selling you one. But two roads out of here are worth knowing about, so here they are with the bill attached.

Textile Knitting and Weaving Machine Setters, Operators, and TendersTextile Cutting Machine Setters, Operators, and Tenders
a year or morematched on shared tasks
One of the tasks on your list is on theirs in the same words: “start machines, monitor operations, and make adjustments as needed”. Take both published task lists together and about 62% of the work in that job that the software is not taking is work you are doing today.
Half of what they do, you do already. The argument is about the other half, not about starting again.
The work the two jobs share
- You already do
  “Start machines, monitor operations, and make adjustments as needed.”
  They do
  “Start machines, monitor operations, and make adjustments as needed.”
- You already do
  “Inspect machinery to determine whether repairs are needed.”
  They do
  “Inspect machinery to determine whether repairs are needed.”
- You already do
  “Stop machines when specified amounts of product have been produced.”
  They do
  “Stop machines when specified amounts of product have been produced.”
What you would not already have: Nothing in your task list touches “place patterns on top of layers of fabric and cut fabric following…”, “adjust cutting techniques to types of fabrics and styles of garments” or “operate machines to cut multiple layers of fabric into parts for articles”. That is the part you would be learning from scratch, and it is roughly the 38% of their durable work you do not already hold.
The honest bill
A pay cut: $38,760 against your $39,530 (OEWS May 2025 (both)). I am saying the words: you would earn less. Decide that on purpose, not by accident.
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $38,760 against your $39,530, 1.9% less (OEWS May 2025 (both)). I am saying the words: you would earn slightly less. Decide that on purpose.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- Is the target job itself holding up: Textile Cutting Machine Setters, Operators, and Tenders scores 12/100 on this site’s own exposure measure (minimal), with 6% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 38% of their work you would be learning, not by any one course.
See what AI is doing to textile cutting machine setters, operators, and tenders
Textile Knitting and Weaving Machine Setters, Operators, and TendersTextile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders
a year or morematched on shared tasks
You already start machines, monitor operations, and make adjustments as needed. In that job the same thing shows up as start machines, monitor operation, and make adjustments as needed. Take both published task lists together and about 46% of the work in that job that the software is not taking is work you are doing today.
About 46% of it you could do on Monday. But you would be doing it for slightly less money, so want it for the work.
The work the two jobs share
- You already do
  “Start machines, monitor operations, and make adjustments as needed.”
  They do
  “Start machines, monitor operation, and make adjustments as needed.”
- You already do
  “Stop machines when specified amounts of product have been produced.”
  They do
  “Stop machines when specified amount of products has been produced.”
- You already do
  “Inspect machinery to determine whether repairs are needed.”
  They do
  “Inspect machinery to determine whether repairs are needed.”
What you would not already have: Nothing in your task list touches “tend machines that twist together two or more strands of yarn or…”, “replace depleted supply packages with full packages” or “observe operations to detect defects, malfunctions, or supply shortages”. That is the part you would be learning from scratch, and it is roughly the 54% of their durable work you do not already hold.
The honest bill
A pay cut: $38,670 against your $39,530 (OEWS May 2025 (both)). I am saying the words: you would earn less. Decide that on purpose, not by accident.
- The licence gate: Default-closed. This release carries no licence-register snapshot, so I could not check whether that job is regulated, which means I have to assume it might be. Before you spend a penny, look it up on the US Labor Department’s licensed-occupations finder; the free routes below link straight to it. The route is banded a year or more because of that unknown, not in spite of it.
- The entry ticket: Typical entry-level education is published for only ten occupations in this release, and neither this job nor that one is among them. So I cannot tell you whether a qualification stands in the way. Treat that as an open question to settle before you commit, not as a green light.
- What the pay gap is telling you: $38,670 against your $39,530, 2.2% less (OEWS May 2025 (both)). I am saying the words: you would earn slightly less. Decide that on purpose.
- What you live on meanwhile: Nobody is going to pay you to retrain. This is evenings and weekends alongside the job you already have, for a year or more, and if that is not possible right now then this route is not open right now, which is worth knowing before you start. The free American Job Center service listed below will talk training funding through with you before you pay anyone.
- Is the target job itself holding up: Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders scores 9/100 on this site’s own exposure measure (minimal), with 5% of its tasks in the top band. Employment projections are not published for this occupation in this release, so this is the exposure leg of the check only. It passed, which is the only reason it is here.
How long: A year or more, part-time, alongside the job you have. That band is set by the unchecked licence question and by the 54% of their work you would be learning, not by any one course.
See what AI is doing to textile winding, twisting, and drawing out machine setters, operators, and tenders

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

Textile Bleaching and Dyeing Machine Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect machinery to determine whether repairs are needed, and their equivalent is to inspect machinery to determine necessary adjustments and repairs. Across both published task lists that is about 22% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 22% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Woodworking Machine Setters, Operators, and Tenders, Except Sawing
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already start machines, monitor operations, and make adjustments as needed, and their equivalent is to start machines, adjust controls, and make trial cuts to ensure that machinery is…. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Maintenance Workers, Machinery
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already start machines, monitor operations, and make adjustments as needed, and their equivalent is to start machines and observe mechanical operation to determine efficiency and to detect problems. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 5% of its task weight, across 19 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like threading yarn, thread and fabric through guides, needles and rollers of machines for weaving, knitting or other processing arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Textile process operatives is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Sewing machinists

In UK official statistics this job is counted as Textile process operatives and Sewing machinists. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
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Check what your state actually requires before you pay for anything.
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Anywhere in the US:
CareerOneStop
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Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Nearby moves

The jobs above, as pages you can read the same way as this one. Your job shares its core work with these. That is what the match is, and it is all it is.

Noted, and thank you. We’ll email you if a Space for textile knitting / weaving machine setters / operators / tenders launches. Nothing else.

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No Space for textile knitting / weaving machine setters / operators / tenders yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for textile knitting / weaving machine setters / operators / tenders existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for textile knitting / weaving machine setters / operators / tenders launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Textile Knitting and Weaving Machine Setters, Operators, and Tenders (United States, SOC 51-6063), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 12 out of 100 (range 9–17, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Textile Knitting and Weaving Machine Setters, Operators, and Tenders” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record information about work completed and machine settings” (64/100, high); “Notify supervisors or repair staff of mechanical malfunctions” (48/100, partial); “Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements” (32/100, low). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Textile Knitting and Weaving Machine Setters, Operators, and Tenders” stay human?: About 88% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation” (0/100, minimal); “Repair or replace worn or defective needles and other components, using hand tools” (0/100, minimal); “Operate machines for test runs to verify adjustments and to obtain product samples” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Textile Knitting and Weaving Machine Setters, Operators, and Tenders” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 88% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Textile Knitting and Weaving Machine Setters, Operators, and Tenders calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
8 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.