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.
- Operate machines to process materials in compliance with applicable safety, energy, or environmental regulations.69
- Measure or weigh materials to be refined, mixed, transferred, stored, or otherwise processed.69
- Set up or adjust machine controls to regulate conditions such as material flow, temperature, or pressure.68
- Start agitators, shakers, conveyors, pumps, or centrifuge machines.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.
- Turn valves or move controls to admit, drain, separate, filter, clarify, mix, or transfer materials.01
- Install, maintain, or repair hoses, pumps, filters, or screens to maintain processing equipment, using hand tools.02
- Remove clogs, defects, or impurities from machines, tanks, conveyors, screens, or other processing equipment.03
- Examine samples to verify qualities such as clarity, cleanliness, consistency, dryness, or texture.04
- Clean or sterilize tanks, screens, inflow pipes, production areas, or equipment, using hoses, brushes, scrapers, or chemical solutions.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders
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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.
