Shipping, Receiving, and Inventory Clerks
Office & Administrative SupportAI 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
0.000
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.37
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, 53% 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 8 hidden tasks| Task | Claude.aiRaw / share % |
|---|---|
Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials.43-5071 | 0.004645.5 |
Examine shipment contents and compare with records such as manifests, invoices, or orders to verify accuracy.43-5071 | 0.002928.6 |
Record shipment data, such as weight, charges, space availability, damages, or discrepancies for reporting, accounting, or recordkeeping purposes.43-5071 | 0.002626.0 |
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 documents, such as work orders, bills of lading, or shipping orders, to route materials.O*NET Task ID 4737 | 1.0 |
Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes.O*NET Task ID 4739 | 1.0 |
Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list.O*NET Task ID 4744 | 1.0 |
Contact carrier representatives to make arrangements or to issue instructions for shipping and delivery of materials.O*NET Task ID 4740 | 0.5 |
Confer or correspond with establishment representatives to rectify problems, such as damages, shortages, or nonconformance to specifications.O*NET Task ID 4741 | 0.5 |
Compare shipping routes or methods to determine which have the least environmental impact.O*NET Task ID 19834 | 0.5 |
Examine shipment contents and compare with records, such as manifests, invoices, or orders, to verify accuracy.O*NET Task ID 20276 | 0.5 |
Determine shipping methods, routes, or rates for materials to be shipped.O*NET Task ID 20279 | 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