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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Textile Knitting and Weaving Machine Setters, Operators, and Tenders?
Task-level automation exposure for Textile Knitting and Weaving Machine Setters, Operators, and Tenders: 19 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
26%
weighted task index
19 of 19 tasks scored
Median Salary
$39,530 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
13,030 people hold this job — size, not growth
Projected 2025–35
-13.7%
employment change
1,300 openings a year · BLS line item
Pay by state
BLS OEWS publishes 25 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$33,550 – $49,570
Highest medians
New York $49,570
New Hampshire $48,440
Maine $48,350
Washington $48,220
Minnesota $46,430
Lowest medians
Tennessee $33,550
Iowa $33,730
Texas $34,790
Mississippi $35,370
North Carolina $36,700
State figures come from the 51-6063 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 26%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 50%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 0%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 4%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 6/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
19 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 14%
3 tasks
Medium exposure (≥ 35%) 10%
2 tasks
Low exposure (below 35%) 76%
14 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#14165 · Core · 22% automatable
Observe woven cloth to detect weaving defects.
visual inspection — Textile inspection is tactile and visual, hands-on
Inspect textile products.
#14166 · Core · 5% automatable
Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.
physical manipulation — Feeding materials into equipment is physical
Feed materials or products into or through equipment.
#14163 · Core · 6% automatable
Remove defects in cloth by cutting and pulling out filling.
physical manipulation — Fabric cutting needs manual dexterity
Cut fabrics.
#14167 · Core · 18% automatable
Examine looms to determine causes of loom stoppage, such as warp filling, harness breaks, or mechanical defects.
visual inspection — Physical inspection of production machinery in situ
Inspect production equipment.
#14164 · Core · 22% automatable
Inspect products to ensure that specifications are met and to determine if machines need adjustment.
visual inspection — Textile inspection is tactile and visual, hands-on
Inspect textile products.
#14168 · Core · 65% automatable
Notify supervisors or repair staff of mechanical malfunctions.
text generation — Notifying maintenance needs is routine messaging
Notify others of equipment repair or maintenance needs.
#14170 · Core · 12% automatable
Start machines, monitor operations, and make adjustments as needed.
physical manipulation — Textile cutting/production is hands-on machine work
Operate textile cutting or production equipment.
#14174 · Core · 12% automatable
Stop machines when specified amounts of product have been produced.
physical manipulation — Textile cutting/production is hands-on machine work
Operate textile cutting or production equipment.
#14171 · Core · 18% automatable
Inspect machinery to determine whether repairs are needed.
visual inspection — Physical inspection of production machinery in situ
Inspect production equipment.
#14173 · Core · 55% automatable
Confer with co-workers to obtain information about orders, processes, or problems.
interpersonal — Colleague information exchange is relational, contextual
Exchange information with colleagues.
#14176 · Core · 21% automatable
Operate machines for test runs to verify adjustments and to obtain product samples.
physical manipulation — Operating production equipment physically on site
Conduct test runs of production equipment. Operate textile cutting or production equipment.
#14178 · Supplemental · 55% automatable
Program electronic equipment.
decision support — Programming production equipment needs physical setup and verification
Program equipment to perform production tasks.
#14169 · Supplemental · 12% automatable
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.
physical manipulation — Textile cutting/production is hands-on machine work
Operate textile cutting or production equipment.
#14181 · Supplemental · 8% automatable
Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.
physical manipulation — Physical control setting for cutting
Mount attachments or tools onto production equipment. Set equipment controls to meet cutting specifications. Install mechanical components in production equipment.
#14172 · Supplemental · 82% automatable
Record information about work completed and machine settings.
data analysis — Record production data into structured logs
Record operational or production data.
#14179 · Supplemental · 61% automatable
Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.
visual inspection — Blueprint setup reading partial, human confirms
Study blueprints or other instructions to determine equipment setup requirements. Exchange information with colleagues.
#14180 · Supplemental · 10% automatable
Repair or replace worn or defective needles and other components, using hand tools.
physical manipulation — Hands-on removal and fitting of worn components
Replace worn equipment components. Repair production equipment or tools.
#14175 · Supplemental · 8% automatable
Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.
physical manipulation — Manual lubrication of production equipment
Clean production equipment. Lubricate production equipment.
#14182 · Supplemental · 12% automatable
Adjust machine heating mechanisms, tensions, and speeds to produce specified products.
physical manipulation — Textile cutting/production is hands-on machine work
Operate textile cutting or production equipment.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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