Retail Salespersons

Sales & Marketing

AI exposure

  • Data source: BLSPublished: '26.08

    Very high· relative

    LowFour relative bandsVery high

    Group-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

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.322

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.38

    0.09Range of values carried here0.70

    Group-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.

    Source dataset

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

Exposed tasks only

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.

TaskβE1 + 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
Greet customers and ascertain what each customer wants or needs.

O*NET Task ID 694

0.0
Open and close cash registers, performing tasks such as counting money, separating charge slips, coupons, and vouchers, balancing cash drawers, and making deposits.

O*NET Task ID 695

0.0
Watch for and recognize security risks and thefts and know how to prevent or handle these situations.

O*NET Task ID 699

0.0
Demonstrate use or operation of merchandise.

O*NET Task ID 706

0.0
Clean shelves, counters, and tables.

O*NET Task ID 707

0.0
Exchange merchandise for customers and accept returns.

O*NET Task ID 708

0.0
Bag or package purchases and wrap gifts.

O*NET Task ID 709

0.0
Help customers try on or fit merchandise.

O*NET Task ID 710

0.0
Prepare merchandise for purchase or rental.

O*NET Task ID 712

0.0

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®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information