Customer Service Representatives
Office & Administrative SupportAI exposure
- Data source: BLSPublished: '26.08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
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: AnthropicPublished: '26.03
0.701
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: '25
0.58
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 5% score at or above this value.
What kind of figure this source publishes
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.
Task-level 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
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]