Compensation and Benefits Managers

Management

AI exposure

  • Data source: BLSPublished: 2026-08

    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: 2026-03

    0.000

    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: 2025

    0.36

    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, 60% 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 3 hidden tasks

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.

TaskβE1 + 0.5 × E2
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.

O*NET Task ID 3267

1.0
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.

O*NET Task ID 3283

1.0
Conduct exit interviews to identify reasons for employee termination.

O*NET Task ID 3286

1.0
Advise management on such matters as equal employment opportunity, sexual harassment, and discrimination.

O*NET Task ID 3266

0.5
Administer, direct, and review employee benefit programs, including the integration of benefit programs following mergers and acquisitions.

O*NET Task ID 3268

0.5
Identify and implement benefits to increase the quality of life for employees by working with brokers and researching benefits issues.

O*NET Task ID 3271

0.5
Design, evaluate, and modify benefits policies to ensure that programs are current, competitive, and in compliance with legal requirements.

O*NET Task ID 3272

0.5
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.

O*NET Task ID 3273

0.5
Formulate policies, procedures and programs for recruitment, testing, placement, classification, orientation, benefits and compensation, and labor and industrial relations.

O*NET Task ID 3274

0.5
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.

O*NET Task ID 3275

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®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information