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.
- Oversee day-to-day group activities of residents in institution.73
- Keep records or prepare reports for owner or management concerning visits with clients.73
- Visit individuals in homes or attend group meetings to provide information on agency services, requirements, or procedures.73
- Provide information or refer individuals to public or private agencies or community services for assistance.73
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.
- Transport and accompany clients to shopping areas or to appointments, using automobile.01
- Observe and discuss meal preparation and suggest alternate methods of food preparation.02
- Oversee day-to-day group activities of residents in institution.03
- Visit individuals in homes or attend group meetings to provide information on agency services, requirements, or procedures.04
- Provide information or refer individuals to public or private agencies or community services for assistance.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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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.
