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 claim files, such as records of settled claims and an inventory of claims requiring detailed analysis.74
- Refer questionable claims to investigator or claims adjuster for investigation or settlement.74
- Prepare reports to be submitted to company's data processing department.74
- Present cases and participate in their discussion at claim committee meetings.74
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.
- Examine titles to property to determine validity and act as company agent in transactions with property owners.01
- Examine claims investigated by insurance adjusters, further investigating questionable claims to determine whether to authorize payments.02
- Investigate, evaluate, and settle claims, applying technical knowledge and human relations skills to effect fair and prompt disposal of cases and to contribute to a reduced loss ratio.03
- Analyze information gathered by investigation and report findings and recommendations.04
- Investigate and assess damage to property and create or review property damage estimates.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Eligibility Interviewers, Government Programs
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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.
