営業担当者
営業・マーケティングAI露出度
- データ出典: BLS公表時点: '26.08
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: ILO公表時点: '25
0.49
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21%です。
- データ出典: OpenAI公表時点: '23
0.567
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
露出のある作業のみ| Task | Claude.aiRaw / share % |
|---|---|
Analyze customer bills and utility rate structures to select optimal rate structures for customers.41-3099 | 0.0022100.0 |
| Not observed on any surface — 15 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. | |
Contact prospective buyers or sellers of power to arrange transactions. | —0 |
Answer customer questions related to energy sales procedures, energy markets, or alternative energy sources. | —0 |
Forecast energy supply and demand to minimize costs and maximize availability. | —0 |
Negotiate prices or contracts for energy sales or purchases. | —0 |
Price energy based on market conditions. | —0 |
Develop or deliver proposals or presentations on topics such as the purchase or sale of energy. | —0 |
Monitor the flow of energy in response to changes in consumer demand. | —0 |
Purchase or sell energy or energy derivatives for customers. | —0 |
Create product packages based on assessment of customers' needs. | —0 |
Explain contracts or related documents to customers. | —0 |
Facilitate the delivery or receipt of wholesale power or retail load scheduling. | —0 |
Analyze and evaluate energy supply bids to determine the best options. | —0 |
Monitor energy supply contracts to ensure proper implementation and execution by suppliers. | —0 |
Prepare and send requests for price quotations to all energy companies in a particular market. | —0 |
Research and recommend new products or services, such as alternative energy sources or renewable energy credits. | —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 |
|---|---|
Create forms or agreements to complete sales.O*NET Task ID 23227 | 1.0 |
Develop sales presentations or proposals to explain service specifications.O*NET Task ID 23228 | 1.0 |
Inform customers of contracts or other information pertaining to purchased services.O*NET Task ID 23232 | 1.0 |
Maintain customer records using automated systems.O*NET Task ID 23233 | 1.0 |
Quote prices, credit terms, contract terms, or fulfillment dates for services.O*NET Task ID 23236 | 1.0 |
Answer customers' questions about services, prices, availability, or credit terms.O*NET Task ID 23222 | 0.5 |
Attend sales or trade meetings or read related publications to obtain information about market conditions, business trends, regulations, or industry developments.O*NET Task ID 23223 | 0.5 |
Compute and compare costs of services.O*NET Task ID 23224 | 0.5 |
Consult with clients after sales or contract signings to resolve problems and provide ongoing support.O*NET Task ID 23225 | 0.5 |
Contact prospective or existing customers to discuss how services can meet their needs.O*NET Task ID 23226 | 0.5 |
Distribute promotional materials at meetings, conferences, or trade shows.O*NET Task ID 23229 | 0.0 |
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
この職業に関わる最近の変化
2026年3月: ATE study identifies sales representatives among 236 information-intensive occupations at moderate-to-high risk of agentic AI displacement by 2030 in US tech hubs.
[出典: Gupta & Kumar (2026) Agentic AI and Occupational Displacement]