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 Graders and Sorters, Agricultural Products?
Task-level automation exposure for Graders and Sorters, Agricultural Products: 5 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
20%
weighted task index
5 of 5 tasks scored
Median Salary
$35,730 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
25,180 people hold this job — size, not growth
Projected 2025–35
-3.4%
employment change
5,800 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.
$27,470 – $48,740
Highest medians
Colorado $48,740
Minnesota $47,540
Maine $45,810
North Dakota $45,590
Nebraska $45,560
Lowest medians
Florida $27,470
Idaho $27,680
Mississippi $30,140
New Mexico $30,480
Arizona $31,700
State figures come from the 45-2041 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 20%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 43%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 22%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 14%
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
5 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) 14%
1 tasks
Medium exposure (≥ 35%) 0%
0 tasks
Low exposure (below 35%) 86%
4 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
#13352 · Core · 9% automatable
Place products in containers according to grade and mark grades on containers.
physical manipulation — Marking forestry products is manual field work
Mark agricultural or forestry products for identification. Package agricultural products for shipment or further processing.
#13351 · Core · 24% automatable
Weigh products or estimate their weight, visually or by feel.
physical manipulation — Hands-on measurement of agricultural products
Measure physical characteristics of forestry or agricultural products.
#13350 · Core · 8% automatable
Discard inferior or defective products or foreign matter, and place acceptable products in containers for further processing.
physical manipulation — Packaging agricultural products is manual handling
Package agricultural products for shipment or further processing.
#13349 · Core · 7% automatable
Grade and sort products according to factors such as color, species, length, width, appearance, feel, smell, and quality to ensure correct processing and usage.
physical manipulation — Agricultural material sorting is hands-on field work
Sort forestry or agricultural materials.
#13353 · Supplemental · 70% automatable
Record grade or identification numbers on tags or on shipping, receiving, or sales sheets.
data analysis — Record inventory data; field collection is human
Record agricultural or forestry inventory data.
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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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