Clearing and Forwarding Aents

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Clearing and Forwarding Aents (ISCO-08 3331) score an average of 0.57 on a 0–1 exposure scale — more exposed than about 95% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Ensuring that insurance is in order;”

Scores 0.64 on the 2025 scale. The task of ensuring that insurance is in order involves handling documentation, compliance, and potentially automating routine administrative duties, which Generative AI is well-suited to assist. Tasks such as "Concluding insurance agreements, preparing and delivering documents to the client" (with an adjusted score of 0.575) and "Maintaining and storing patient health records in various classification systems" (adjusted score 0.575) highlight the potential for automation in structured processes, while human oversight is necessary for compliance and handling exceptions. The automation of ensuring insurance order can align with these tasks in terms of data management and routine processing, with AI assisting in ensuring documentation and policy alignment. Given the high technological access in Poland, which facilitates the use of advanced digital tools, an adjusted score of 0.63 reflects the realistic automation potential, acknowledging the efficiency AI provides while recognizing the crucial need for human oversight in nuanced judgment and compliance verification.

Moving fastest, 2023 → 2025

“Signing and issuing bills of lading;”

Model capability on this task changed by +0.25 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3331, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Shipping, Receiving, and Inventory Clerks
• Cargo and Freight Agents
• Freight Forwarders

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Clearing and Forwarding Aents sit at the 95th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Clearing and Forwarding Aents rank in the 95th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.11 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Ensuring that insurance is in order;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Clearing and Forwarding Aents". https://singulariki.com/gradient/3331-clearing-and-forwarding-aents.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.