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.
- Provide assistance to extraction craft workers, such as earth drillers and derrick operators.65
- Signal workers to start geological material extraction or boring.65
- Set up and adjust equipment used to excavate geological materials.63
- Unload materials, devices, and machine parts, using hand tools.61
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 up work areas and remove debris after extraction activities are complete.01
- Drive moving equipment to transport materials and parts to excavation sites.02
- Repair and maintain automotive and drilling equipment, using hand tools.03
- Provide assistance to extraction craft workers, such as earth drillers and derrick operators.04
- Unload materials, devices, and machine parts, using hand tools.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Helpers--Installation, Maintenance, and Repair Workers
Closely related work
Compare these careers →Rotary Drill Operators, Oil and Gas
Closely related work
Compare these careers →Operating Engineers and Other Construction Equipment Operators
Shares some work
Compare these careers →Maintenance Workers, Machinery
Shares some work
Compare these careers →Hoist and Winch Operators
Shares some work
Compare these careers →Machine Feeders and Offbearers
Shares some 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.
