Credit Counselors

Business & Financial Operations

AI exposure

  • Data source: BLSPublished: 2026-08

    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: 2026-03

    0.234

    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

    0.60

    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 1 hidden task

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
Calculate clients' available monthly income to meet debt obligations.

O*NET Task ID 18935

1.0
Estimate time for debt repayment, given amount of debt, interest rates, and available funds.

O*NET Task ID 18937

1.0
Explain services or policies to clients, such as debt management program rules, advantages and disadvantages of using services, or creditor concession policies.

O*NET Task ID 18938

1.0
Prepare written documents to establish contracts with or communicate financial recommendations to clients.

O*NET Task ID 18942

1.0
Explain general financial topics to clients, such as credit report ratings, bankruptcy laws, consumer protection laws, wage attachments, or collection actions.

O*NET Task ID 18952

1.0
Explain loan information to clients, such as available loan types, eligibility requirements, or loan restrictions.

O*NET Task ID 18953

1.0
Advise clients or respond to inquiries about financial matters in person or via phone, email, Web site, or Internet chat.

O*NET Task ID 18933

0.5
Assess clients' overall financial situations by reviewing income, assets, debts, expenses, credit reports, or other financial information.

O*NET Task ID 18934

0.5
Create debt management plans, spending plans, or budgets to assist clients to meet financial goals.

O*NET Task ID 18936

0.5
Interview clients by telephone or in person to gather financial information.

O*NET Task ID 18939

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