AI Automation Index

Will AI Replace Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons?

Task-level automation exposure for Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons: 11 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

7% weighted task index

11 of 11 tasks scored

Median Salary

$40,470 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

7,490 people hold this job — size, not growth

Projected 2025–35

+1.5% employment change

700 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.

$23,470 – $53,340

Highest medians

Ohio $53,340

New York $52,000

Hawaii $51,540

California $46,220

Montana $44,720

Lowest medians

Mississippi $23,470

Michigan $30,300

Arizona $32,000

Georgia $35,430

New Mexico $35,860

State figures come from the 47-3014 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 7%

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

Physical work 100%

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 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 10/100 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%) 0%

0 tasks

Low exposure (below 35%) 100%

11 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

#13612 · Core · 10% automatable

Clean work areas and equipment.

physical manipulation — Equipment and facility cleaning is hands-on

Clean equipment or facilities.

#13617 · Core · 6% automatable

Supply or hold tools and materials.

physical manipulation — Physical assisting of skilled trades on site

Assist skilled construction or extraction personnel.

#13614 · Core · 4% automatable

Apply protective coverings, such as masking tape, to articles or areas that could be damaged or stained by work processes.

physical manipulation — Hands-on placement of protective coverings

Protect structures or surfaces near work areas to avoid damage.

#13618 · Core · 8% automatable

Smooth surfaces of articles to be painted, using sanding and buffing tools and equipment.

physical manipulation — Abrasive hand work on physical surfaces

Smooth surfaces with abrasive materials or tools.

#13615 · Core · 10% automatable

Erect scaffolding.

physical manipulation — Erecting temporary structures requires physical assembly

Assemble temporary equipment or structures.

#13613 · Core · 6% automatable

Perform support duties to assist painters, paperhangers, plasterers, or masons.

physical manipulation — Physical assisting of skilled trades on site

Assist skilled construction or extraction personnel.

#13616 · Core · 5% automatable

Fill cracks or breaks in surfaces of plaster articles or areas with putty or epoxy compounds.

physical manipulation — Applying filler to surfaces is hands-on

Apply material to fill gaps in surfaces.

#13619 · Supplemental · 10% automatable

Mix plaster, and carry plaster to plasterers.

physical manipulation — Physical handling of substances in workspace

Mix substances or compounds needed for work activities. Move construction or extraction materials to locations where they are needed.

#13622 · Supplemental · 4% automatable

Pour specified amounts of chemical solutions into stripping tanks.

physical manipulation — Physical pouring of materials into areas

Pour materials into or on designated areas.

#13620 · Supplemental · 10% automatable

Place articles to be stripped into stripping tanks.

physical manipulation — Surface preparation is hands-on physical work

Clean surfaces in preparation for work activities.

#13621 · Supplemental · 8% automatable

Remove articles such as cabinets, metal furniture, and paint containers from stripping tanks after prescribed periods of time.

physical manipulation — Physical moving of products between work areas

Move products, materials, or equipment between work areas.

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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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