Bank Tellers

Office & Administrative Support

AI exposure

  • Data source: BLSPublished: 2026-08

    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: 2026-03

    0.023

    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: 2025

    0.58

    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, 5% 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

Show 6 hidden tasks

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
Balance currency, coin, and checks in cash drawers at ends of shifts and calculate daily transactions, using computers, calculators, or adding machines.

O*NET Task ID 2549

1.0
Enter customers' transactions into computers to record transactions and issue computer-generated receipts.

O*NET Task ID 2553

1.0
Identify transaction mistakes when debits and credits do not balance.

O*NET Task ID 2555

1.0
Perform clerical tasks, such as typing, filing, and microfilm photography.

O*NET Task ID 2562

1.0
Compute financial fees, interest, and service charges.

O*NET Task ID 2570

1.0
Compose, type, and mail customer statements and other correspondence related to issues such as discrepancies and outstanding unpaid items.

O*NET Task ID 2572

1.0
Issue checks to bond owners in settlement of transactions.

O*NET Task ID 2573

1.0
Inform customers about foreign currency regulations and compute transaction fees for currency exchanges.

O*NET Task ID 2574

1.0
Quote unit exchange rates, following daily international rate sheets or computer displays.

O*NET Task ID 2575

1.0
Prepare work schedules for staff.

O*NET Task ID 2576

1.0

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