Community Sentiment
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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Coil Winders, Tapers, and Finishers?
Task-level automation exposure for Coil Winders, Tapers, and Finishers: 11 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
29%
weighted task index
11 of 11 tasks scored
Median Salary
$48,220 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
12,840 people hold this job — size, not growth
Projected 2025–35
-4.0%
employment change
1,200 openings a year · BLS line item
Pay by state
BLS OEWS publishes 38 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$21,840 – $73,920
Highest medians
Washington $73,920
Louisiana $61,480
Iowa $59,780
Georgia $59,180
Virginia $56,230
Lowest medians
Puerto Rico $21,840
Mississippi $35,730
Nevada $38,140
Oklahoma $40,490
Oregon $41,600
State figures come from the 51-2021 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 29%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 72%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 10%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 17%
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 56/100
High
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) 21%
2 tasks
Medium exposure (≥ 35%) 7%
1 tasks
Low exposure (below 35%) 72%
8 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
#11904 · Core · 12% automatable
Operate or tend wire-coiling machines to wind wire coils used in electrical components such as resistors and transformers, and in electrical equipment and instruments such as bobbins and generators.
physical manipulation — Metal and plastic forming equipment run by hand
Operate metal or plastic forming equipment.
#11909 · Core · 8% automatable
Attach, alter, and trim materials such as wire, insulation, and coils, using hand tools.
physical manipulation — Manual trimming of workpieces
Trim excess material from workpieces.
#11906 · Core · 6% automatable
Cut, strip, and bend wire leads at ends of coils, using pliers and wire scrapers.
physical manipulation — Hands-on industrial material cutting
Assemble electrical or electronic equipment. Cut industrial materials in preparation for fabrication or processing.
#11905 · Core · 80% automatable
Review work orders and specifications to determine materials needed and types of parts to be processed.
information retrieval — Parse specs and materials from orders
Read work orders or other instructions to determine product specifications or materials requirements.
#11907 · Core · 31% automatable
Select and load materials such as workpieces, objects, and machine parts onto equipment used in coiling processes.
decision support — Input material selection from specs; human approves
Select production input materials. Load materials into production equipment.
#11908 · Core · 82% automatable
Record production and operational data on specified forms.
data analysis — Record production data into structured logs
Record operational or production data.
#11910 · Core · 6% automatable
Stop machines to remove completed components, using hand tools.
physical manipulation — Physically extracting parts from production equipment
Remove products or workpieces from production equipment.
#11911 · Supplemental · 55% automatable
Examine and test wired electrical components such as motors, armatures, and stators, using measuring devices, and record test results.
data analysis — Record production data into structured logs
Test electrical equipment or systems to ensure proper functioning. Record operational or production data.
#11914 · Supplemental · 5% automatable
Line slots with sheet insulation, and insert coils into slots.
physical manipulation — Hands-on assembly of metal or plastic parts
Assemble metal or plastic parts or products.
#11912 · Supplemental · 13% automatable
Apply solutions or paints to wired electrical components, using hand tools, and bake components.
physical manipulation — Physical control of heating/drying equipment
Apply protective or decorative finishes to workpieces or products. Operate heating or drying equipment.
#11913 · Supplemental · 9% automatable
Disassemble and assemble motors, and repair and maintain electrical components and machinery parts, using hand tools.
physical manipulation — Hands-on repair of production machinery
Disassemble equipment for maintenance or repair. Repair production equipment or tools. Maintain production or processing 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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