カウンター・レンタル事務員
営業・マーケティングAI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 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
0.205
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: OpenAI公表時点: 2023
0.677
0.000ここに掲載された値の範囲0.844尺度・母数・出典
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 3 件を表示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 |
|---|---|
Answer telephones to provide information and receive orders.O*NET Task ID 681 | 1.0 |
Prepare rental forms, obtaining customer signature and other information, such as required licenses.O*NET Task ID 684 | 1.0 |
Receive orders for services, such as rentals, repairs, dry cleaning, and storage.O*NET Task ID 689 | 1.0 |
Provide information about rental items, such as availability, operation, or description.O*NET Task ID 691 | 1.0 |
Compute charges for merchandise or services and receive payments.O*NET Task ID 678 | 0.5 |
Recommend and provide advice on a wide variety of products and services.O*NET Task ID 680 | 0.5 |
Greet customers and discuss the type, quality, and quantity of merchandise sought for rental.O*NET Task ID 682 | 0.5 |
Keep records of transactions and of the number of customers entering an establishment.O*NET Task ID 683 | 0.5 |
Explain rental fees, policies, and procedures.O*NET Task ID 687 | 0.5 |
Reserve items for requested times and keep records of items rented.O*NET Task ID 688 | 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