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.
- Direct helpers engaged in placing blocking or outrigging under cranes.69
- Determine load weights and check them against lifting capacities to prevent overload.68
- Review daily work or delivery schedules to determine orders, sequences of deliveries, or special loading instructions.68
- Inspect and adjust crane mechanisms or lifting accessories to prevent malfunctions or damage.37
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.
- Clean, lubricate, and maintain mechanisms such as cables, pulleys, or grappling devices, making repairs, as necessary.01
- Move levers, depress foot pedals, or turn dials to operate cranes, cherry pickers, electromagnets, or other moving equipment for lifting, moving, or placing loads.02
- Load or unload bundles from trucks, or move containers to storage bins, using moving equipment.03
- Inspect cables or grappling devices for wear and install or replace cables, as needed.04
- Determine load weights and check them against lifting capacities to prevent overload.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Hoist and Winch Operators
Closely related work
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Related work
Compare these careers →Tank Car, Truck, and Ship Loaders
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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.
