Cargo and Freight Agents

Office & Administrative Support

AI exposure

  • Data source: BLSPublished: 2026-08

    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: 2026-03

    0.017

    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

    0.57

    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, 7% 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

Show 10 hidden tasks

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
Determine method of shipment and prepare bills of lading, invoices, and other shipping documents.

O*NET Task ID 8192

1.0
Estimate freight or postal rates and record shipment costs and weights.

O*NET Task ID 8194

1.0
Enter shipping information into a computer by hand or by a hand-held scanner that reads bar codes on goods.

O*NET Task ID 8195

1.0
Check import or export documentation to determine cargo contents and use tariff coding system to classify goods according to fee or tariff group.

O*NET Task ID 20273

1.0
Prepare manifests showing numbers of airplane passengers and baggage, mail, and freight weights, transmitting data to destinations.

O*NET Task ID 20274

1.0
Negotiate and arrange transport of goods with shipping or freight companies.

O*NET Task ID 8188

0.5
Advise clients on transportation and payment methods.

O*NET Task ID 8190

0.5
Retrieve stored items and trace lost shipments as necessary.

O*NET Task ID 8196

0.5
Inspect and count items received and check them against invoices or other documents, recording shortages and rejecting damaged goods.

O*NET Task ID 8199

0.5
Keep records of all goods shipped, received, and stored.

O*NET Task ID 8201

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