Quality Assurance Managers
ManagementAI exposure
- Data source: BLSPublished: 2026-08
High· relative
LowFour relative bandsVery highGroup-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
- Data source: AnthropicPublished: 2026-03
0.013
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.38
0.09Range of values carried here0.70Group-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.
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 90 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Evaluate power production or demand trends to identify opportunities for improved operations.11-3051 | 0.004544.6 | 0.001821.4 |
Participate in the development of product specifications.11-3051 | 0.002928.7 | —0 |
Identify opportunities to improve plant electrical equipment, controls, or process control methodologies.11-3051 | 0.002726.7 | —0 |
Confer with marketing and sales departments to define client requirements and expectations. | —0 | 0.005160.7 |
Monitor and operate communications systems, such as mobile radios. | —0 | 0.001517.9 |
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.
| Task | βE1 + 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 |
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®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page