Suscriptores de Seguros
Negocios y FinanzasExposición a la IA
- Fuente de datos: BLSPublicado: '26.08
Muy alto· relativa
BajoCuatro bandas relativasMuy altoValor por grupo ocupacional
Escala, base y fuente
Cuatro bandas relativas (Bajo / Moderado / Alto / Muy alto)
831 ocupaciones detalladas de la tabla de proyecciones de empleo de BLS. El valor se asigna por código de la National Employment Matrix (NEM), de modo que las ocupaciones que comparten un código NEM reciben la misma banda
- Fuente de datos: AnthropicPublicado: '26.03
0.063
0.000Rango de los valores aquí recogidos0.745Escala, base y fuente
Índice de exposición observada, 0–1 tal como se publica
Mapeado sobre tareas de O*NET
- Fuente de datos: ILOPublicado: '25
0.53
0.09Rango de los valores aquí recogidos0.70Valor por grupo ocupacional
Escala, base y fuente
Índice de exposición a la IA generativa, 0–1 tal como se publica
Grupo primario de la CIUO-08 — todas las ocupaciones con ese código reciben este valor
Calculado por este sitio, no publicado por la OIT: de las 1012 ocupaciones que este sitio vincula al conjunto de datos de la OIT, un 14% alcanza o supera este valor.
Qué tipo de cifra publica esta fuente
La categoría de BLS es un rango relativo, no un nivel absoluto, y tampoco es una medición de primera mano: agrupa en cuatro bandas los rangos percentiles de la ocupación en varios estudios publicados. No es una previsión de empleo ni de salarios, no es una probabilidad de adopción y no distingue entre automatización y aumento.
Task-level exposure
Solo tareas expuestas| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Decline excessive risks.13-2053 | 0.0072100.0 | 0.007678.3 |
Examine documents to determine degree of risk from such factors as applicant financial standing and value and condition of property. | —0 | 0.002121.7 |
| Not observed on any surface — 5 task(s) — These tasks have no row in the source for this release. The 0 in the share row is a display-stage composition ratio; absence is what the — in the raw row states. | ||
Write to field representatives, medical personnel, and others to obtain further information, quote rates, or explain company underwriting policies. | —0 | —0 |
Evaluate possibility of losses due to catastrophe or excessive insurance. | —0 | —0 |
Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe profitable distribution of risks, using reference materials. | —0 | —0 |
Review company records to determine amount of insurance in force on single risk or group of closely related risks. | —0 | —0 |
Authorize reinsurance of policy when risk is high. | —0 | —0 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.O*NET Task ID 1262 | 1.0 |
Decline excessive risks.O*NET Task ID 1261 | 0.5 |
Evaluate possibility of losses due to catastrophe or excessive insurance.O*NET Task ID 1263 | 0.5 |
Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials.O*NET Task ID 1264 | 0.5 |
Review company records to determine amount of insurance in force on single risk or group of closely related risks.O*NET Task ID 1265 | 0.5 |
Authorize reinsurance of policy when risk is high.O*NET Task ID 1266 | 0.5 |
Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.O*NET Task ID 21045 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page
Occupation information
Cambios recientes que afectan a esta ocupación
abr 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.
[Fuente: arXiv 2604.00186 (Gupta & Kumar, 2026)]mar 2026: Published evergreen blog analysis: AI exposure 64%, automation risk 62/100 in 2025.
[Fuente: AI Changing Work Blog]