AI Automation Index

Will AI Replace Sewers, Hand?

Task-level automation exposure for Sewers, Hand: 11 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

16% weighted task index

11 of 11 tasks scored

Median Salary

$36,480 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

2,190 people hold this job — size, not growth

Projected 2025–35

-12.0% employment change

600 openings a year · BLS line item

Pay by state

BLS OEWS publishes 18 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$21,840 – $47,600

Highest medians

Colorado $47,600

Washington $45,170

California $42,420

Iowa $42,290

South Carolina $39,690

Lowest medians

Puerto Rico $21,840

Louisiana $27,040

Texas $29,320

Indiana $34,410

Pennsylvania $35,560

State figures come from the 51-6051 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 16%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 88%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 19%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 0%

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 0/100 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

11 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) 0%

0 tasks

Medium exposure (≥ 35%) 19%

2 tasks

Low exposure (below 35%) 81%

9 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 →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

New 08/2024 · Incumbent

Patch materials, such as cotton or leather.

Constituent Tasks

#14110 · Core · 58% automatable

Select thread, twine, cord, or yarn to be used, and thread needles.

decision support — Input material selection from specs; human approves

Select production input materials.

#14114 · Core · 17% automatable

Measure and align parts, fasteners, or trimmings, following seams, edges, or markings on parts.

physical manipulation — Hands-on dimensional measurement of workpieces

Measure dimensions of completed products or workpieces to verify conformance to specifications. Align parts or workpieces to ensure proper assembly.

#14109 · Core · 8% automatable

Trim excess threads or edges of parts, using scissors or knives.

physical manipulation — Manual trimming of workpieces

Trim excess material from workpieces.

#14108 · Core · 4% automatable

Sew, join, reinforce, or finish parts of articles, such as garments, books, mattresses, toys, and wigs, using needles and thread or other materials.

physical manipulation — Sewing clothing requires fine manual dexterity

Sew clothing or other articles.

#14115 · Core · 4% automatable

Use different sewing techniques such as felling, tacking, basting, embroidery, and fagoting.

physical manipulation — Sewing clothing requires fine manual dexterity

Sew clothing or other articles.

#24001 · Supplemental · 17% automatable

Fit garments on clients, altering as needed.

physical manipulation — Marking reference points requires hands-on measurement

Measure materials to mark reference points, cutting lines, or other indicators. Measure clients to ensure proper product fit. Cut materials according to specifications or needs.

#14113 · Supplemental · 8% automatable

Smooth seams with heated irons, flat bones, or rubbing sticks.

physical manipulation — Hand sanding and finishing of physical objects

Smooth surfaces of objects or equipment.

#14111 · Supplemental · 38% automatable

Draw and cut patterns according to specifications.

visual inspection — Generates template designs; aesthetic fit needs human review

Design templates or patterns. Cut industrial materials in preparation for fabrication or processing.

#14112 · Supplemental · 4% automatable

Fold, twist, stretch, or drape material, and secure articles in preparation for sewing.

physical manipulation — Adjusting fabric is tactile manual work

Adjust fabrics or other materials during garment production.

#14116 · Supplemental · 4% automatable

Sew buttonholes, or add lace or other trimming.

physical manipulation — Sewing clothing requires fine manual dexterity

Sew clothing or other articles.

#14118 · Supplemental · 5% automatable

Tie, knit, weave or knot ribbon, yarn, or decorative materials.

physical manipulation — Sewing and assembling textiles needs fine dexterity

Assemble garments or textile products.

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Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

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

See the full method →

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