報酬・福利厚生マネージャー
経営AI露出度
- データ出典: BLS公表時点: 2026-08
高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.000
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.36
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは60%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 15 件を表示| Task | Claude.aiRaw / share % |
|---|---|
Develop methods to improve employment policies, processes, and practices, and recommend changes to management.11-3111 | 0.007720.7 |
Analyze statistical data and reports to identify and determine causes of personnel problems and develop recommendations for improvement of organization's personnel policies and practices.11-3111 | 0.006918.5 |
Analyze compensation policies, government regulations, and prevailing wage rates to develop competitive compensation plan.11-3111 | 0.006316.9 |
Direct preparation and distribution of written and verbal information to inform employees of benefits, compensation, and personnel policies.11-3111 | 0.00349.1 |
Design, evaluate and modify benefits policies to ensure that programs are current, competitive and in compliance with legal requirements.11-3111 | 0.00318.3 |
Advise management on such matters as equal employment opportunity, sexual harassment and discrimination.11-3111 | 0.00297.7 |
Mediate between benefits providers and employees, such as by assisting in handling employees' benefits-related questions or taking suggestions.11-3111 | 0.00297.7 |
Prepare detailed job descriptions and classification systems and define job levels and families, in partnership with other managers.11-3111 | 0.00266.9 |
Identify and implement benefits to increase the quality of life for employees, by working with brokers and researching benefits issues.11-3111 | 0.00154.1 |
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 |
|---|---|
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page