Grants Management Specialists
Business & Financial OperationsAI 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
0.220
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.42
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, 33% 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 37 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Prepare requirements documentation for use by software developers.13-2099 | 0.080036.4 | 0.020028.6 |
Provide application or analytical support to researchers or traders on issues such as valuations or data.13-2099 | 0.080036.4 | 0.020028.6 |
Define or recommend model specifications or data collection methods.13-2099 | 0.030013.6 | 0.00000.0 |
Interpret results of financial analysis procedures.13-2099 | 0.01004.5 | 0.010014.3 |
Prepare written reports of investigation findings.13-2099 | 0.01004.5 | 0.00000.0 |
Recommend actions in fraud cases.13-2099 | 0.01004.5 | 0.00000.0 |
Evaluate business operations to identify risk areas for fraud.13-2099 | 0.00000.0 | 0.020028.6 |
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 |
|---|---|
Prepare written reports of investigation findings.O*NET Task ID 16046 | 1.0 |
Document all investigative activities.O*NET Task ID 16053 | 1.0 |
Maintain knowledge of current events and trends in such areas as money laundering and criminal tools and techniques.O*NET Task ID 16035 | 0.5 |
Train others in fraud detection and prevention techniques.O*NET Task ID 16036 | 0.5 |
Research or evaluate new technologies for use in fraud detection systems.O*NET Task ID 16037 | 0.5 |
Prepare evidence for presentation in court.O*NET Task ID 16038 | 0.5 |
Negotiate with responsible parties to arrange for recovery of losses due to fraud.O*NET Task ID 16040 | 0.5 |
Advise businesses or agencies on ways to improve fraud detection.O*NET Task ID 16044 | 0.5 |
Review reports of suspected fraud to determine need for further investigation.O*NET Task ID 16045 | 0.5 |
Recommend actions in fraud cases.O*NET Task ID 16047 | 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