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AI Automation Index
Will AI Replace Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders?
Task-level automation exposure for Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders: 19 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
25%
weighted task index
19 of 19 tasks scored
Median Salary
$44,810 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
20,370 people hold this job — size, not growth
Projected 2025–35
+0.4%
employment change
2,500 openings a year · BLS line item
Pay by state
BLS OEWS publishes 44 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$35,340 – $77,300
Highest medians
North Dakota $77,300
Maryland $60,170
Colorado $59,590
Virginia $59,260
Alaska $55,280
Lowest medians
Florida $35,340
Montana $35,360
New Mexico $35,480
North Carolina $35,980
Mississippi $37,030
State figures come from the 51-3091 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 25%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 51%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 7%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 6%
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 41/100
Moderate
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
19 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) 12%
2 tasks
Medium exposure (≥ 35%) 6%
1 tasks
Low exposure (below 35%) 81%
16 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
#10058 · Core · 28% automatable
Observe, feel, taste, or otherwise examine products during and after processing to ensure conformance to standards.
visual inspection — Food quality needs taste, smell, physical inspection
Inspect food products. Evaluate quality of food ingredients or prepared foods.
#10061 · Core · 17% automatable
Set temperature and time controls, light ovens, burners, driers, or roasters, and start equipment, such as conveyors, cylinders, blowers, driers, or pumps.
physical manipulation — Manual temperature control adjustment
Adjust temperature controls of ovens or other heating equipment. Operate pumping systems or equipment.
#10059 · Core · 30% automatable
Observe temperature, humidity, pressure gauges, and product samples and adjust controls, such as thermostats and valves, to maintain prescribed operating conditions for specific stages.
visual inspection — Instrument monitoring needs physical sensor presence
Monitor instruments to ensure proper production conditions.
#10062 · Core · 52% automatable
Observe flow of materials and listen for machine malfunctions, such as jamming or spillage, and notify supervisors if corrective actions fail.
text generation — Notifying maintenance needs is routine messaging
Watch operating equipment to detect malfunctions. Notify others of equipment repair or maintenance needs.
#10063 · Core · 82% automatable
Record production data, such as weight and amount of product processed, type of product, and time and temperature of processing.
data analysis — Record production data into structured logs
Record operational or production data.
#10064 · Core · 25% automatable
Weigh or measure products, using scale hoppers or scale conveyors.
physical manipulation — Hands-on dimensional measurement of workpieces
Weigh finished products. Measure dimensions of completed products or workpieces to verify conformance to specifications.
#10060 · Core · 10% automatable
Operate or tend equipment that roasts, bakes, dries, or cures food items such as cocoa and coffee beans, grains, nuts, and bakery products.
physical manipulation — Hands-on cooking with heat and perishable materials
Operate cooking, baking, or other food preparation equipment.
#10076 · Core · 8% automatable
Signal coworkers to synchronize flow of materials.
physical manipulation — Embodied signaling in shared physical workspace
Signal others to coordinate work activities.
#10065 · Core · 80% automatable
Read work orders to determine quantities and types of products to be baked, dried, or roasted.
information retrieval — Parse specs and materials from orders
Read work orders or other instructions to determine product specifications or materials requirements.
#10067 · Core · 6% automatable
Fill or remove product from trays, carts, hoppers, or equipment, using scoops, peels, or shovels, or by hand.
physical manipulation — Physically extracting parts from production equipment
Remove products or workpieces from production equipment.
#10066 · Supplemental · 8% automatable
Take product samples during or after processing for laboratory analyses.
physical manipulation — Manual product sampling
Collect samples of materials or products for testing.
#10075 · Supplemental · 20% automatable
Test products for moisture content, using moisture meters.
visual inspection — Food product inspection is sensory and hands-on
Inspect food products.
#10070 · Supplemental · 15% automatable
Clear or dislodge blockages in bins, screens, or other equipment, using poles, brushes, or mallets.
physical manipulation — Physically clearing jams in machinery
Clear equipment jams.
#10072 · Supplemental · 5% automatable
Start conveyors to move roasted grain to cooling pans and agitate grain with rakes as blowers force air through perforated bottoms of pans.
physical manipulation — Feeding materials into equipment is physical
Feed materials or products into or through equipment.
#10068 · Supplemental · 15% automatable
Open valves, gates, or chutes or use shovels to load or remove products from ovens or other equipment.
physical manipulation — Hands-on production flow control
Adjust equipment controls to regulate flow of production materials or products.
#10069 · Supplemental · 4% automatable
Clean equipment with steam, hot water, and hoses.
physical manipulation — Manual sterilization of cooking equipment
Sterilize food cooking or processing equipment.
#10073 · Supplemental · 5% automatable
Smooth out products in bins, pans, trays, or conveyors, using rakes or shovels.
physical manipulation — Positioning raw materials is physical loading
Position raw materials on processing or production equipment.
#10074 · Supplemental · 5% automatable
Install equipment, such as spray units, cutting blades, or screens, using hand tools.
physical manipulation — Mounting tools requires hands-on setup
Mount attachments or tools onto production equipment.
#10071 · Supplemental · 8% automatable
Push racks or carts to transfer products to storage, cooling stations, or the next stage of processing.
physical manipulation — Physical moving of products between work areas
Move products, materials, or equipment between work 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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