カスタマーサービス担当者
事務・管理サポート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: Anthropic公表時点: '26.03
0.701
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: '25
0.58
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5%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 % |
|---|---|
Explain policies, procedures, or services to patients using medical or administrative knowledge.43-4051 | 0.046122.4 |
Confer with customers by telephone or in person to provide information about products or services, take or enter orders, cancel accounts, or obtain details of complaints.43-4051 | 0.030514.8 |
Coordinate communication between patients, family members, medical staff, administrative staff, or regulatory agencies.43-4051 | 0.030414.7 |
Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills.43-4051 | 0.025012.1 |
Review insurance policy terms to determine whether a particular loss is covered by insurance.43-4051 | 0.01899.2 |
Develop and distribute newsletters, brochures, or other printed materials to share information with patients or medical staff.43-4051 | 0.01557.5 |
Identify and share research, recommendations, or other information regarding legal liabilities, risk management, or quality of care.43-4051 | 0.00793.9 |
Provide consultation or training to volunteers or staff on topics such as guest relations, patients' rights, and medical issues.43-4051 | 0.00773.7 |
Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.43-4051 | 0.00733.5 |
Collect and report data on topics such as patient encounters and inter-institutional problems, making recommendations for change when appropriate.43-4051 | 0.00643.1 |
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 |
|---|---|
Complete contract forms, prepare change of address records, or issue service discontinuance orders, using computers.O*NET Task ID 2584 | 1.0 |
Compare disputed merchandise with original requisitions and information from invoices and prepare invoices for returned goods.O*NET Task ID 2589 | 1.0 |
Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.O*NET Task ID 2578 | 0.5 |
Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills.O*NET Task ID 2579 | 0.5 |
Check to ensure that appropriate changes were made to resolve customers' problems.O*NET Task ID 2580 | 0.5 |
Contact customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments.O*NET Task ID 2581 | 0.5 |
Refer unresolved customer grievances to designated departments for further investigation.O*NET Task ID 2582 | 0.5 |
Determine charges for services requested, collect deposits or payments, or arrange for billing.O*NET Task ID 2583 | 0.5 |
Obtain and examine all relevant information to assess validity of complaints and to determine possible causes, such as extreme weather conditions that could increase utility bills.O*NET Task ID 2585 | 0.5 |
Solicit sales of new or additional services or products.O*NET Task ID 2586 | 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
この職業に関わる最近の変化
2026年6月: Cited by ADP Research as an example of a high-AI-exposure occupation. Group-level payroll data shows employment in high-exposure occupations down 0.2% year over year overall and down 4.3% for workers aged 22-25 (33rd consecutive monthly decline). The percentages are for the high-exposure group, not for this occupation alone.
[出典: ADP Research, Canaries Dashboard (June 2026)]2026年5月: Anthropic observed-exposure score: high (zero->observed gap small). High observed exposure due to simpler software pipelines and lower integration cost.
[出典: Anthropic Economic Research (Massenkoff & McCrory, 2026)]2026年4月: NBER survey of 6,000 executives: 69% use AI but 90% report zero employment impact. Executives predict -0.7% employment decline over next 3 years.
[出典: NBER Working Paper 34836]