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Manufacturing & Production · Updated Aug 2026

Textile Cutting Machine Setters, Operators, and Tenders

Set up, operate, or tend machines that cut textiles.

JVS 2.0.0-phase4b
AI Exposure
45/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
46/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence81/100
Task coverage84%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Thread yarn, thread, or fabric through guides, needles, and rollers of machines.High
69
Stop machines when specified amounts of product have been produced.High
68
Record information about work completed and machine settings.High
70
Adjust machine controls, such as heating mechanisms, tensions, or speeds, to produce specified products.High
68
Confer with coworkers to obtain information about orders, processes, or problems.Medium
70
Inspect products to ensure that the quality standards and specifications are met.High
39
Start machines, monitor operations, and make adjustments as needed.High
40
Study guides, samples, charts, and specification sheets or confer with supervisors or engineering staff to determine set-up requirements.High
48
Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.High
24
Place patterns on top of layers of fabric and cut fabric following patterns, using electric or manual knives, cutters, or computer numerically controlled cutting devices.High
23
Operate machines for test runs to verify adjustments and to obtain product samples.Medium
39
Adjust cutting techniques to types of fabrics and styles of garments.High
18
Install, level, and align components, such as gears, chains, guides, dies, cutters, or needles, to set up machinery for operation.Medium
23
Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns.Medium
25
Repair or replace worn or defective parts or components, using hand tools.Medium
25
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Record information about work completed and machine settings.70
  2. Confer with coworkers to obtain information about orders, processes, or problems.70
  3. Thread yarn, thread, or fabric through guides, needles, and rollers of machines.69
  4. Stop machines when specified amounts of product have been produced.68
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Adjust cutting techniques to types of fabrics and styles of garments.01
  2. Operate machines to cut multiple layers of fabric into parts for articles such as canvas goods, house furnishings, garments, hats, or stuffed toys.02
  3. Place patterns on top of layers of fabric and cut fabric following patterns, using electric or manual knives, cutters, or computer numerically controlled cutting devices.03
  4. Install, level, and align components, such as gears, chains, guides, dies, cutters, or needles, to set up machinery for operation.04
  5. Repair or replace worn or defective parts or components, using hand tools.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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AI risk 47

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AI risk 45

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency48
Physical dependency62
Adoption pressure47
Labour-market resilience54
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence81/100
CalculatedAug 21, 2026
Read methodology →