Retail Buyers
Sales & MarketingUnited States · BLS Employment Projections 2025–35: this SOC code is not among the published items.
AI exposure
- Data source: ILOPublished: 2025
0.39
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, 44% score at or above this value.
- Data source: OpenAIPublished: 2023
0.400
0.000Range of values carried here0.844Scale, basis and source
Task-level exposure
Show 13 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Obtain information about customer needs or preferences by conferring with sales or purchasing personnel.13-1022 | 0.003533.7 | 0.004930.6 |
Monitor and analyze sales records, trends, or economic conditions to anticipate consumer buying patterns and determine what the company will sell and how much inventory is needed.13-1022 | 0.002726.0 | 0.003622.5 |
Buy merchandise or commodities for resale to wholesale or retail consumers.13-1022 | 0.002726.0 | —0 |
Provide clerks with information to print on price tags, such as price, mark-ups or mark-downs, manufacturer number, season code, or style number.13-1022 | 0.001514.4 | —0 |
Determine which products should be featured in advertising, the advertising medium to be used, or when the ads should be run. | —0 | 0.003622.5 |
Monitor competitors' sales activities by following their advertisements in newspapers or other media. | —0 | 0.002113.1 |
Authorize payment of invoices or return of merchandise. | —0 | 0.001811.2 |
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 |
|---|---|
Provide clerks with information to print on price tags, such as price, mark-ups or mark-downs, manufacturer number, season code, or style number.O*NET Task ID 77 | 1.0 |
Monitor and analyze sales records, trends, or economic conditions to anticipate consumer buying patterns, company sales, and needed inventory.O*NET Task ID 66 | 0.5 |
Authorize payment of invoices or return of merchandise.O*NET Task ID 68 | 0.5 |
Consult with store or merchandise managers about budgets or goods to be purchased.O*NET Task ID 72 | 0.5 |
Determine which products should be featured in advertising, the advertising medium to be used, or when the ads should be run.O*NET Task ID 78 | 0.5 |
Monitor competitors' sales activities by following their advertisements in newspapers or other media.O*NET Task ID 79 | 0.5 |
Analyze environmental aspects of competing merchandise when making buying decisions.O*NET Task ID 19547 | 0.5 |
Compare transportation options to determine the most energy-efficient options.O*NET Task ID 19548 | 0.5 |
Develop strategies to advertise green products or merchandise to consumers.O*NET Task ID 19549 | 0.5 |
Identify opportunities to buy green commodities, such as alternative energy, water, or carbon-neutral products for resale to consumers.O*NET Task ID 19550 | 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