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AI Automation Index
Will AI Replace Dishwashers?
Task-level automation exposure for Dishwashers: 13 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
8%
weighted task index
13 of 13 tasks scored
Median Salary
$34,810 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
477,450 people hold this job — size, not growth
Projected 2025–35
-1.0%
employment change
68,900 openings a year · BLS line item
Pay by state
BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$21,030 – $40,080
Highest medians
Washington $40,080
District of Columbia $38,670
California $37,870
Colorado $37,840
Hawaii $37,740
Lowest medians
Guam $21,030
Puerto Rico $23,020
Mississippi $26,300
Louisiana $26,440
West Virginia $26,460
State figures come from the 35-9021 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 8%
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 4/100
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
13 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%
13 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 08/2025 · Incumbent
Clean and sanitize the dining area, including tables.
Constituent Tasks
#11186 · Core · 7% automatable
Wash dishes, glassware, flatware, pots, or pans, using dishwashers or by hand.
physical manipulation — Hands-on dishwashing of physical tableware
Clean tableware.
#11188 · Core · 10% automatable
Maintain kitchen work areas, equipment, or utensils in clean and orderly condition.
physical manipulation — Food area cleaning requires physical labor
Clean food preparation areas, facilities, or equipment.
#11187 · Core · 8% automatable
Place clean dishes, utensils, or cooking equipment in storage areas.
physical manipulation — Moving goods in kitchens and storage areas
Store supplies or goods in kitchens or storage areas.
#11190 · Core · 10% automatable
Sweep or scrub floors.
physical manipulation — Food area cleaning requires physical labor
Clean food preparation areas, facilities, or equipment.
#11189 · Core · 8% automatable
Stock supplies, such as food or utensils, in serving stations, cupboards, refrigerators, or salad bars.
physical manipulation — Moving goods in kitchens and storage areas
Store supplies or goods in kitchens or storage areas. Stock serving stations or dining areas with food or supplies.
#11193 · Core · 5% automatable
Clean or prepare various foods for cooking or serving.
physical manipulation — Manual food preparation
Prepare foods for cooking or serving.
#11192 · Core · 5% automatable
Sort and remove trash, placing it in designated pickup areas.
physical manipulation — Physical collection and removal of trash
Remove trash.
#11195 · Core · 9% automatable
Transfer supplies or equipment between storage and work areas, by hand or using hand trucks.
physical manipulation — Hands-on moving of supplies to locations
Move equipment, supplies or food to required locations.
#11196 · Core · 8% automatable
Receive and store supplies.
physical manipulation — Moving goods in kitchens and storage areas
Store supplies or goods in kitchens or storage areas.
#11191 · Core · 10% automatable
Clean garbage cans with water or steam.
physical manipulation — Food area cleaning requires physical labor
Clean food preparation areas, facilities, or equipment.
#11198 · Supplemental · 4% automatable
Load or unload trucks that deliver or pick up food or supplies.
physical manipulation — Physical lifting and loading of shipments
Load shipments, belongings, or materials.
#11197 · Supplemental · 5% automatable
Prepare and package individual place settings.
physical manipulation — Hands-on packing of varied goods
Package food or supplies.
#11194 · Supplemental · 5% automatable
Set up banquet tables.
physical manipulation — Physically arranging tables and furniture
Arrange tables or dining areas.
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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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