Gini Loh System Tools

AI Automation Index

Will AI Replace Machine Feeders and Offbearers?

Task-level automation exposure for Machine Feeders and Offbearers: 13 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

18% weighted task index

13 of 13 tasks scored

Median Salary

$41,220 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Medium

42,330 people hold this job — size, not growth

Projected 2025–35

-13.1% employment change

4,000 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.

$22,400 – $55,120

Highest medians

Wisconsin $55,120
Wyoming $51,280
Minnesota $48,740
Colorado $48,400
Kansas $47,930

Lowest medians

Puerto Rico $22,400
Maine $32,310
Rhode Island $32,770
Florida $35,110
Virginia $36,290

State figures come from the 53-7063 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 18%

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

Physical work 64%

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 9%

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 21/100 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) 9%

1 tasks

Medium exposure (≥ 35%) 0%

0 tasks

Low exposure (below 35%) 91%

12 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

#14583 · Core · 24% automatable

Inspect materials and products for defects, and to ensure conformance to specifications.

visual inspection — Defect inspection of physical items needs presence

Inspect work to ensure standards are met. Inspect items for damage or defects.
#14591 · Core · 82% automatable

Record production and operational data, such as amount of materials processed.

data analysis — Record production data into structured logs

Record operational or production data.
#14585 · Core · 18% automatable

Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.

physical manipulation — Physical control of conveyors and material movers

Operate conveyors or other industrial material moving equipment.
#14589 · Core · 26% automatable

Weigh or measure materials or products to ensure conformance to specifications.

physical manipulation — Hands-on dimensional measurement of material

Measure product or material dimensions. Weigh materials to ensure compliance with specifications.
#14586 · Core · 12% automatable

Identify and mark materials, products, and samples, following instructions.

physical manipulation — Physically marking objects requires manipulation

Mark materials or objects for identification.
#14588 · Core · 8% automatable

Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.

physical manipulation — Machinery cleaning requires hands-on work

Clean facilities or work areas. Clean machinery or equipment.
#14587 · Core · 4% automatable

Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.

physical manipulation — Hands-on loading of processing equipment

Load materials into equipment for processing.
#14593 · Core · 8% automatable

Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.

physical manipulation — Manual moving of materials and supplies

Move materials, equipment, or supplies.
#14590 · Core · 5% automatable

Fasten, package, or stack materials and products, using hand tools and fastening equipment.

physical manipulation — Hands-on packaging of products

Package materials or products.
#14584 · Supplemental · 5% automatable

Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices.

physical manipulation — Hands-on packaging of products

Package materials or products.
#14595 · Supplemental · 3% automatable

Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.

physical manipulation — Manual shoveling of loose materials

Shovel materials.
#14594 · Supplemental · 18% automatable

Open and close gates of belt and pneumatic conveyors on machines that are fed directly from preceding machines.

physical manipulation — Physical control of conveyors and material movers

Operate conveyors or other industrial material moving equipment.
#14592 · Supplemental · 4% automatable

Add chemicals, solutions, or ingredients to machines or equipment as required by the manufacturing process.

physical manipulation — Hands-on loading of processing equipment

Load materials into equipment for processing.
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Community Sentiment

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Do you agree with the AI automation risk score calculated for this occupation?

Method

How this was scored

  1. 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
  2. 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. 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. 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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