客户服务代表
办公与行政支持AI暴露度
- 数据来源: BLS发布时间: '26.08
很高· 相对
低四级相对区间很高职业群单位数值
尺度 · 母数 · 来源
四级相对类别（低 / 中等 / 高 / 很高）
以BLS就业预测表中831个细分职业为基数。取值按 NEM（全国就业矩阵）代码给定，因此共用同一 NEM 代码的职业得到相同分档
- 数据来源: Anthropic发布时间: '26.03
0.701
0.000此处所载数值的范围0.745尺度 · 母数 · 来源
- 数据来源: ILO发布时间: '25
0.58
0.09此处所载数值的范围0.70职业群单位数值
尺度 · 母数 · 来源
生成式AI暴露度指数，按发布原值0–1
ISCO-08职业小类单位 — 共用同一代码的职业取值相同
本站计算，并非国际劳工组织发布的数值。在本站与 ILO 数据集相连的 1,012 个职业中，达到或高于此值的占 5%。
该来源发布的是什么性质的数值
BLS的类别是相对排位而非绝对水平，也不是一手测量：它把多项已发表研究给出的职业百分位排名归并为四个等级。它不是就业或工资预测，不是采用概率，也不区分自动化与增强。
Task-level exposure
显示隐藏的 13 项工作| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Review insurance policy terms to determine whether a particular loss is covered by insurance.43-4051 | 0.012328.4 | —0 |
Explain policies, procedures, or services to patients using medical or administrative knowledge.43-4051 | 0.008319.1 | 0.00421.0 |
Develop and distribute newsletters, brochures, or other printed materials to share information with patients or medical staff.43-4051 | 0.005913.6 | 0.00220.5 |
Resolve customers' service or billing complaints by performing activities such as exchanging merchandise, refunding money, or adjusting bills.43-4051 | 0.005011.5 | 0.061715.2 |
Keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken.43-4051 | 0.004610.5 | 0.228656.5 |
Provide consultation or training to volunteers or staff on topics such as guest relations, patients' rights, and medical issues.43-4051 | 0.00378.6 | —0 |
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 | 0.00194.3 | 0.070817.5 |
Refer patients to appropriate health care services or resources.43-4051 | 0.00184.1 | —0 |
Refer unresolved customer grievances to designated departments for further investigation. | —0 | 0.01634.0 |
Solicit sales of new or additional services or products. | —0 | 0.00751.9 |
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
与本职业相关的近期变化
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.
[来源: 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.
[来源: 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.
[来源: NBER Working Paper 34836]