AI Automation Index

Will AI Replace Painting, Coating, and Decorating Workers?

Task-level automation exposure for Painting, Coating, and Decorating Workers: 9 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

19% weighted task index

9 of 9 tasks scored

Median Salary

$41,600 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

7,940 people hold this job — size, not growth

Projected 2025–35

+1.3% employment change

700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 37 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$31,550 – $61,210

Highest medians

Washington $61,210

Nevada $60,190

Colorado $54,070

Massachusetts $54,070

New Jersey $52,160

Lowest medians

South Dakota $31,550

Texas $32,430

Mississippi $33,330

Montana $34,790

Tennessee $35,540

State figures come from the 51-9123 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 19%

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

Physical work 87%

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 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 1/100 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

9 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) 13%

1 tasks

Medium exposure (≥ 35%) 10%

1 tasks

Low exposure (below 35%) 77%

7 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

#12527 · Core · 10% automatable

Apply coatings, such as paint, ink, or lacquer, to protect or decorate workpiece surfaces, using spray guns, pens, or brushes.

physical manipulation — Applying finishes requires hands-on coating work

Apply protective or decorative finishes to workpieces or products.

#12528 · Core · 19% automatable

Examine finished surfaces of workpieces to verify conformance to specifications and retouch any defective areas.

visual inspection — Finish inspection is physical/sensory quality check

Inspect finishes of workpieces or finished products. Operate painting or coating equipment.

#12529 · Core · 8% automatable

Clean and maintain tools and equipment, using solvents, brushes, and rags.

physical manipulation — Physical production equipment maintenance

Clean production equipment. Maintain production or processing equipment.

#12530 · Core · 66% automatable

Read job orders and inspect workpieces to determine work procedures and materials required.

visual inspection — Blueprints need spatial reading, human verifies sequence

Review blueprints or other instructions to determine operational methods or sequences.

#12534 · Supplemental · 35% automatable

Select and mix ingredients to prepare coating substances according to specifications, using paddles or mechanical mixers.

decision support — Input material selection from specs; human approves

Select production input materials. Mix ingredients to create specific finishes.

#12533 · Supplemental · 3% automatable

Place coated workpieces in ovens or dryers for specified times to dry or harden finishes.

physical manipulation — Manual loading into hot ovens or furnaces

Load items into ovens or furnaces.

#12531 · Supplemental · 9% automatable

Clean surfaces of workpieces in preparation for coating, using cleaning fluids, solvents, brushes, scrapers, steam, sandpaper, or cloth.

physical manipulation — Hands-on cleaning of physical workpieces

Clean workpieces or finished products.

#12536 · Supplemental · 5% automatable

Conceal blemishes in workpieces, such as nicks and dents, using fillers such as putty.

physical manipulation — Filling cracks is manual finishing work

Fill cracks, imperfections, or holes in products or workpieces.

#12532 · Supplemental · 9% automatable

Rinse, drain, or wipe coated workpieces to remove excess coating material or to facilitate setting of finish coats on workpieces.

physical manipulation — Hands-on cleaning of physical workpieces

Clean workpieces or finished 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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