How much of this occupation’s work can be materially affected by current AI systems.
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
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Stack finished packaged items, or wrap protective material around each item, and pack the items in cartons or containers.70
- Supply materials to spindles, conveyors, hoppers, or other feeding devices and unload packaged product.70
- Package the product in the form in which it will be sent out, for example, filling bags with flour from a chute or spout.69
- Stop or reset machines when malfunctions occur, clear machine jams, and report malfunctions to a supervisor.68
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.
- Attach identification labels to finished packaged items, or cut stencils and stencil information on containers, such as lot numbers or shipping destinations.01
- Secure finished packaged items by hand tying, sewing, gluing, stapling, or attaching fastener.02
- Clean packaging containers, line and pad crates, or assemble cartons to prepare for product packing.03
- Clean, oil, and make minor adjustments or repairs to machinery and equipment, such as opening valves or setting guides.04
- Clean and remove damaged or otherwise inferior materials to prepare raw products for processing.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Packers and Packagers, Hand
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Related work
Compare these careers →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.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
