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Transport & Logistics · Updated Aug 2026

Machine Feeders and Offbearers

Feed materials into or remove materials from machines or equipment that is automatic or tended by other workers.

JVS 2.0.0-phase4b
AI Exposure
47/100
Moderate

How much of this occupation’s work can be materially affected by current AI systems.

Replacement Risk
49/100
Moderate

How likely exposure is to translate into reduced human demand.

Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured

Evidence quality
Confidence82/100
Task coverage88%

Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.

Task-level evidence

What is driving the score?

Occupation scores are built from the task mix—not a single prediction about a job title.

JVS 2.0.0-phase4b
TaskImportanceAI impactExposure
Record production and operational data, such as amount of materials processed.High
80
Weigh or measure materials or products to ensure conformance to specifications.High
79
Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.Medium
74
Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.High
69
Inspect materials and products for defects, and to ensure conformance to specifications.High
40
Remove materials and products from machines and equipment, and place them in boxes, trucks or conveyors, using hand tools and moving devices.High
37
Identify and mark materials, products, and samples, following instructions.High
35
Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.High
25
Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.High
25
Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.Medium
25
Fasten, package, or stack materials and products, using hand tools and fastening equipment.Medium
24
Most exposed

Where AI can do more

Routine, digitized, and highly repeatable tasks face the greatest pressure.

  1. Record production and operational data, such as amount of materials processed.80
  2. Weigh or measure materials or products to ensure conformance to specifications.79
  3. Transfer materials and products to and from machinery and equipment, using industrial trucks or hand trucks.74
  4. Load materials and products into machines and equipment, or onto conveyors, using hand tools and moving devices.69
Hardest to automate

Where people still matter

These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.

  1. Shovel or scoop materials into containers, machines, or equipment for processing, storage, or transport.01
  2. Fasten, package, or stack materials and products, using hand tools and fastening equipment.02
  3. Push dual control buttons and move controls to start, stop, or adjust machinery and equipment.03
  4. Clean and maintain machinery, equipment, and work areas to ensure proper functioning and safe working conditions.04
  5. Identify and mark materials, products, and samples, following instructions.05
Where else this work leads

Related occupations

Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.

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AI risk 49

Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders

Closely related work

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AI risk 47

Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic

Related work

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Beyond AI capability

Adoption and labour-market outlook

Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.

Human dependency52
Physical dependency61
Adoption pressure43
Labour-market resilience51
Methodology & sources

O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.

Confidence82/100
CalculatedAug 21, 2026
Read methodology →