Customer Service Representatives
Office & Administrative SupportAI exposure
Show 9 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
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 | 0.060085.7 | 0.020028.6 |
Contact customers to respond to inquiries or to notify them of claim investigation results or any planned adjustments.43-4051 | 0.010014.3 | 0.030042.9 |
Solicit sales of new or additional services or products.43-4051 | 0.00000.0 | 0.010014.3 |
Refer unresolved customer grievances to designated departments for further investigation. | —0 | 0.010014.3 |
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
Exposure figures by source
This site carries AI exposure figures from 4 datasets. For this occupation, 4 of them publish a figure of the kind shown below; the 3 published most recently are displayed. The full list is on the Credits & Sources page.
Data source: BLS
Very high
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
Data source: Anthropic
0.701
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Data source: ILO
0.58
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Each figure is published on its own scale and measures something different, so they cannot be added, averaged, or ranked against one another.
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
BLS draws on research that is also shown here — Anthropic — so a resemblance between them is not independent confirmation but the same input read twice.
AI exposure (ILO)
0.58 / 1
top 5% of all occupations
Occupation information
Recent Changes Affecting This Occupation
Jun 2026: 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.
[Source: ADP Research, Canaries Dashboard (June 2026)]May 2026: Anthropic observed-exposure score: high (zero->observed gap small). High observed exposure due to simpler software pipelines and lower integration cost.
[Source: Anthropic Economic Research (Massenkoff & McCrory, 2026)]Apr 2026: 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.
[Source: NBER Working Paper 34836]