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.
- Record the numbers and types of fish or shellfish reared, harvested, released, sold, and shipped.70
- Treat animal illnesses or injuries, following experience or instructions of veterinarians.70
- Train workers in techniques such as planting, harvesting, weeding, or insect identification and in the use of safety measures.68
- Perform both supervisory and management functions, such as accounting, marketing, and personnel work.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.
- Coordinate the selection and movement of logs from storage areas, according to transportation schedules or production requirements.01
- Assign tasks such as feeding and treatment of animals, and cleaning and maintenance of animal quarters.02
- Drive or operate farm machinery, such as trucks, tractors, or self-propelled harvesters, to transport workers or supplies or to cultivate or harvest fields.03
- Transport or arrange for transport of animals, equipment, food, animal feed, and other supplies to and from work sites.04
- Observe fish and beds or ponds to detect diseases, monitor fish growth, determine quality of fish, or determine completeness of harvesting.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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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.
