Data Entry Keyers
Office & Administrative SupportAI 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
0.671
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: '25
0.70
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, 0% 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 7 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Compile, sort and verify the accuracy of data before it is entered.43-9021 | 0.016778.0 | 0.113265.6 |
Read source documents such as canceled checks, sales reports, or bills, and enter data in specific data fields or onto tapes or disks for subsequent entry, using keyboards or scanners.43-9021 | 0.004722.0 | 0.059534.4 |
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 |
|---|---|
Compile, sort, and verify the accuracy of data before it is entered.O*NET Task ID 11402 | 1.0 |
Compare data with source documents, or re-enter data in verification format to detect errors.O*NET Task ID 11403 | 1.0 |
Store completed documents in appropriate locations.O*NET Task ID 11404 | 1.0 |
Locate and correct data entry errors, or report them to supervisors.O*NET Task ID 11405 | 1.0 |
Maintain logs of activities and completed work.O*NET Task ID 11406 | 1.0 |
Select materials needed to complete work assignments.O*NET Task ID 11407 | 1.0 |
Resolve garbled or indecipherable messages, using cryptographic procedures and equipment.O*NET Task ID 11409 | 1.0 |
Read source documents such as canceled checks, sales reports, or bills, and enter data in specific data fields or onto tapes or disks for subsequent entry, using keyboards or scanners.O*NET Task ID 11401 | 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
Occupation information
Recent Changes Affecting This Occupation
May 2026: Anthropic observed-exposure score: high (routine tasks absorbed). High observed exposure; routine tasks already absorbed by LLMs.
[Source: Anthropic Economic Research (Massenkoff & McCrory, 2026)]Apr 2026: NBER survey of 6,000 executives: 69% use AI but 90% report zero employment impact. Executives predict -0.7% employment decline over next 3 years.
[Source: NBER Working Paper 34836]Apr 2026: Among highest automation exposure occupations. AI topped all job cut reasons in March 2026 with 15,341 positions (25% of total). Q1 cumulative AI cuts: 27,645.
[Source: Challenger March 2026]