Transport Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Transport Clerks (ISCO-08 4323) score an average of 0.49 on a 0–1 exposure scale — more exposed than about 88% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Coordinating and keeping records of operational activities concerning road transport, such as allocation and scheduling of vehicles and drivers, loading and unloading of vehicles and storage of goods in transit;”

Scores 0.58 on the 2025 scale. The task of coordinating and keeping records of operational activities concerning road transport includes scheduling, vehicle and driver allocation, and logistics tracking. This task shares semantic similarities with routing and logistics organization, which were scored at approximately 0.4 to 0.5 for automation potential, suggesting AI's capacity for data handling, optimization, and procedural management tasks. Tasks like planning transportation received scores around 0.38 to 0.4 due to the decision-making reliance, while others involving data management, such as maintaining registers, saw higher adjustability scores near 0.68 to 0.7. Given that Generative AI excels in automating repetitive and data-intensive processes, along with the availability of digital infrastructure in high-income countries like Poland, a mid-to-high potential for partial automation can be attributed here. The adjusted score of 0.62 reflects a balance between AI-assisted optimization and the need for human oversight in strategic decisions and exceptional handling in logistics and operational management.

Moving fastest, 2023 → 2025

“Coordinating and keeping records of operational activities concerning road transport, such as allocation and scheduling of vehicles and drivers, loading and unloading of vehicles and storage of goods in transit;”

Model capability on this task changed by +0.23 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 4323, 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.

• Dispatchers, Except Police, Fire, and Ambulance

In context

Part of the 4 - Clerical support workers major group. Return to the full gradient to see how the whole group sits.

Transport Clerks sit at the 88th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Transport Clerks rank in the 88th 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 fell by 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Coordinating and keeping records of operational activities concerning road transport, such as allocation and scheduling of vehicles and drivers, loading and unloading of vehicles and storage of goods in transit;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Transport Clerks". https://singulariki.com/gradient/4323-transport-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.