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.
- Maintain or verify records of time, materials, expenditures, or crew activities.70
- Dispatch personnel and vehicles in response to telephone or radio reports of emergencies.70
- Explain and demonstrate work tasks to new workers or assign training tasks to experienced workers.70
- Prepare, compile, and submit reports on work activities, operations, production, or work-related accidents.70
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.
- Compute or estimate cash, payroll, transportation, personnel, or storage requirements.01
- Requisition needed personnel, supplies, equipment, parts, or repair services.02
- Interpret transportation or tariff regulations, shipping orders, safety regulations, or company policies and procedures for workers.03
- Drive vehicles or operate machines or equipment to complete work assignments or to assist workers.04
- Monitor field work to ensure proper performance and use of materials.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
First-Line Supervisors of Helpers, Laborers, and Material Movers, Hand
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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.
