Promotional Sales Representatives
Sales & MarketingAI exposure
- Data source: BLSPublished: 2026-08
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: 2026-03
0.079
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.42
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, 33% 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 16 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Suggest specific product purchases to meet customers' needs.41-9011 | 0.014740.1 | 0.013716.1 |
Provide product information, using lectures, films, charts, or slide shows.41-9011 | 0.007119.2 | 0.011813.8 |
Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services.41-9011 | 0.006216.9 | 0.011013.0 |
Learn about competitors' products or consumers' interests or concerns to answer questions or provide more complete information.41-9011 | 0.004411.9 | 0.009010.6 |
Prepare or alter presentation contents to target specific audiences.41-9011 | 0.00277.3 | 0.00253.0 |
Research or investigate products to be presented to prepare for demonstrations.41-9011 | 0.00174.5 | 0.00182.1 |
Identify interested and qualified customers to provide them with additional information. | —0 | 0.030736.1 |
Develop lists of prospective clients from sources such as newspaper items, company records, local merchants, or customers. | —0 | 0.00465.4 |
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 |
|---|---|
Prepare or alter presentation contents to target specific audiences.O*NET Task ID 13200 | 1.0 |
Write articles or pamphlets about products.O*NET Task ID 13213 | 1.0 |
Learn about competitors' products or consumers' interests or concerns to answer questions or provide more complete information.O*NET Task ID 13201 | 0.5 |
Research or investigate products to be presented to prepare for demonstrations.O*NET Task ID 13206 | 0.5 |
Recommend product or service improvements to employers.O*NET Task ID 13207 | 0.5 |
Develop lists of prospective clients from sources such as newspaper items, company records, local merchants, or customers.O*NET Task ID 13212 | 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