Operations Research Analysts

Computer & Mathematical

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

    0.429

    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

    0.56

    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, 8% 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
Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.

O*NET Task ID 7377

1.0
Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them.

O*NET Task ID 7385

1.0
Specify manipulative or computational methods to be applied to models.

O*NET Task ID 7386

1.0
Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data.

O*NET Task ID 7388

1.0
Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.

O*NET Task ID 7378

0.5
Analyze information obtained from management to conceptualize and define operational problems.

O*NET Task ID 7379

0.5
Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.

O*NET Task ID 7380

0.5
Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives.

O*NET Task ID 7381

0.5
Define data requirements, and gather and validate information, applying judgment and statistical tests.

O*NET Task ID 7382

0.5
Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes.

O*NET Task ID 7383

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

Mar 2026: New blog post analyzing AI impact on operations research analyst careers with 50% exposure and 32% automation risk.

[Source: AI Changing Work Blog]