Accountants & Auditors

Business & Financial Operations

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

    0.348

    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

    0.51

    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, 17% 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

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
Prepare detailed reports on audit findings.

O*NET Task ID 21505

1.0
Prepare adjusting journal entries.

O*NET Task ID 21515

1.0
Establish tables of accounts and assign entries to proper accounts.

O*NET Task ID 21517

1.0
Report to management about asset utilization and audit results, and recommend changes in operations and financial activities.

O*NET Task ID 21506

0.5
Collect and analyze data to detect deficient controls, duplicated effort, extravagance, fraud, or non-compliance with laws, regulations, and management policies.

O*NET Task ID 21507

0.5
Inspect account books and accounting systems for efficiency, effectiveness, and use of accepted accounting procedures to record transactions.

O*NET Task ID 21508

0.5
Supervise auditing of establishments, and determine scope of investigation required.

O*NET Task ID 21509

0.5
Confer with company officials about financial and regulatory matters.

O*NET Task ID 21510

0.5
Examine and evaluate financial and information systems, recommending controls to ensure system reliability and data integrity.

O*NET Task ID 21511

0.5
Inspect cash on hand, notes receivable and payable, negotiable securities, and canceled checks to confirm records are accurate.

O*NET Task ID 21512

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: 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]

Mar 2026: Karpathy rates accountants 9/10 for AI exposure, among the highest-scoring occupations in his analysis of 342 US jobs.

[Source: Karpathy AI Exposure Score (Fortune)]

Mar 2026: While the arXiv study focused primarily on computer/math occupations, accountants (AI exposure 73/100) face similar dynamics: structural changes in AI adoption preceded the ChatGPT era. Workers with AI fluency are better positioned regardless of occupation.

[Source: Frank et al. (2026) arXiv:2601.02554]