Insurance Underwriters

Business & Financial Operations

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.063

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.53

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 14% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 5 hidden tasks
TaskClaude.aiRaw / share %APIRaw / share %
Decline excessive risks.

13-2053

0.0072100.00.007678.3
Examine documents to determine degree of risk from such factors as applicant financial standing and value and condition of property.
00.002121.7

Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

Recent Changes Affecting This Occupation

Apr 2026: ATE 0.42 by 2027 in SF Bay Tier 1. Rule-application heavy roles with thin P1 (interpersonal) and P2 (regulatory) penalties show fast agentic exposure climb.

[Source: arXiv 2604.00186 (Gupta & Kumar, 2026)]

Mar 2026: Published evergreen blog analysis: AI exposure 64%, automation risk 62/100 in 2025.

[Source: AI Changing Work Blog]