Freight Handlers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Freight Handlers (ISCO-08 9333) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 14% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Packing office or household furniture, machines, appliances and related goods to be transported from one place to another;”

Scores 0.17 on the 2025 scale. The task of packing office or household furniture, machines, appliances, and related goods to be transported is predominantly physical, requiring dexterity, strength, and coordination. Generative AI technologies, like ChatGPT, lack the capability to automate physical tasks that involve manual labor and situational awareness. Similar tasks identified in the context, such as "Packing goods and preparing them for shipment" (Automation Score: 0.15357) and "Reloading goods from containers to trucks and wagons" (Automation Score: 0.08958), are also physical in nature and have low adjusted scores reflecting minimal automation potential. While AI can support organizational aspects, such as generating packing lists or optimizing logistics processes, the need for human involvement in the actual packing remains high, limiting overall automation potential. Given these considerations, and the distinct nature from more logistical or data-driven tasks, an adjusted score of 0.15 reflects the realistic limitations of Generative AI in fully automating this predominantly manual task.

Moving fastest, 2023 → 2025

“Packing office or household furniture, machines, appliances and related goods to be transported from one place to another;”

Model capability on this task changed by +0.07 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 9333, 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.

• Laborers and Freight, Stock, and Material Movers, Hand
• Recycling and Reclamation Workers
• Tank Car, Truck, and Ship Loaders

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Freight Handlers sit at the 14th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Freight Handlers rank in the 14th 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 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Packing office or household furniture, machines, appliances and related goods to be transported from one place to another;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Freight Handlers". https://singulariki.com/gradient/9333-freight-handlers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.