Office Clerks, General

Office & Administrative Support

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

    0.450

    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

    0.60

    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, 4% 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 5 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
Compile, copy, sort, and file records of office activities, business transactions, and other activities.

O*NET Task ID 828

1.0
Complete and mail bills, contracts, policies, invoices, or checks.

O*NET Task ID 829

1.0
Compute, record, and proofread data and other information, such as records or reports.

O*NET Task ID 831

1.0
Maintain and update filing, inventory, mailing, and database systems, either manually or using a computer.

O*NET Task ID 832

1.0
Process and prepare documents, such as business or government forms and expense reports.

O*NET Task ID 838

1.0
Type, format, proofread, and edit correspondence and other documents, from notes or dictating machines, using computers or typewriters.

O*NET Task ID 840

1.0
Prepare meeting agendas, attend meetings, and record and transcribe minutes.

O*NET Task ID 843

1.0
Troubleshoot problems involving office equipment, such as computer hardware and software.

O*NET Task ID 844

1.0
Communicate with customers, employees, and other individuals to answer questions, disseminate or explain information, take orders, and address complaints.

O*NET Task ID 826

0.5
Answer telephones, direct calls, and take messages.

O*NET Task ID 827

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

Occupation information

Recent Changes Affecting This Occupation

Apr 2026: Office clerks identified in OpenAI 18% high-pressure band; structured admin language tasks dominate role.

[Source: OpenAI Jobs Transition Framework, April 2026]

Mar 2026: Brookings study finds office clerks are the single largest group (2.5M workers) in the high AI exposure, low adaptive capacity category. Geographic concentration in college towns and state capitals.

[Source: Brookings Institution — Measuring US workers capacity to adapt (2026-01)]

Nov 2025: McKinsey MGI report flags office clerks doing routine filing, scheduling, and document processing as highly automatable. 30% of work hours could be automated in midpoint scenario by 2030.

[Source: McKinsey Global Institute]