Quality Assurance Managers

Management

AI exposure

  • Data source: BLSPublished: 2026-08

    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

    0.013

    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

    0.38

    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, 48% 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

Exposed tasks only

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
Review and update standard operating procedures or quality assurance manuals.

O*NET Task ID 15415

1.0
Document testing procedures, methodologies, or criteria.

O*NET Task ID 15423

1.0
Review and approve quality plans submitted by contractors.

O*NET Task ID 15406

0.5
Review statistical studies, technological advances, or regulatory standards and trends to stay abreast of issues in the field of quality control.

O*NET Task ID 15407

0.5
Generate and maintain quality control operating budgets.

O*NET Task ID 15408

0.5
Evaluate new testing and sampling methodologies or technologies to determine usefulness.

O*NET Task ID 15409

0.5
Coordinate the selection and implementation of quality control equipment, such as inspection gauges.

O*NET Task ID 15410

0.5
Collect and analyze production samples to evaluate quality.

O*NET Task ID 15411

0.5
Verify that raw materials, purchased parts or components, in-process samples, and finished products meet established testing and inspection standards.

O*NET Task ID 15413

0.5
Review quality documentation necessary for regulatory submissions and inspections.

O*NET Task ID 15414

0.5
Stop production if serious product defects are present.

O*NET Task ID 15405

0.0
Audit and inspect subcontractor facilities including external laboratories.

O*NET Task ID 15412

0.0

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