Retail Salespersons
Sales & MarketingAI 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
0.322
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: '25
0.38
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, 48% 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 17 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Recommend, select, and help locate or obtain merchandise based on customer needs and desires.41-2031 | 0.109539.7 | 0.090650.9 |
Greet customers and ascertain what each customer wants or needs.41-2031 | 0.105938.4 | 0.054030.4 |
Describe merchandise and explain use, operation, and care of merchandise to customers.41-2031 | 0.036913.4 | 0.01096.1 |
Answer questions regarding the store and its merchandise.41-2031 | 0.01746.3 | 0.00663.7 |
Prepare merchandise for purchase or rental.41-2031 | 0.00401.4 | 0.00251.4 |
Ticket, arrange, and display merchandise to promote sales.41-2031 | 0.00230.8 | 0.01136.4 |
Maintain knowledge of current sales and promotions, policies regarding payment and exchanges, and security practices. | —0 | 0.00211.2 |
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 |
|---|---|
Maintain records related to sales.O*NET Task ID 698 | 1.0 |
Prepare sales slips or sales contracts.O*NET Task ID 704 | 1.0 |
Estimate cost of repair or alteration of merchandise.O*NET Task ID 715 | 1.0 |
Maintain knowledge of current sales and promotions, policies regarding payment and exchanges, and security practices.O*NET Task ID 696 | 0.5 |
Compute sales prices, total purchases, and receive and process cash or credit payment.O*NET Task ID 697 | 0.5 |
Recommend, select, and help locate or obtain merchandise based on customer needs and desires.O*NET Task ID 700 | 0.5 |
Answer questions regarding the store and its merchandise.O*NET Task ID 701 | 0.5 |
Describe merchandise and explain use, operation, and care of merchandise to customers.O*NET Task ID 702 | 0.5 |
Ticket, arrange, and display merchandise to promote sales.O*NET Task ID 703 | 0.5 |
Place special orders or call other stores to find desired items.O*NET Task ID 705 | 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