Drivers of Animal-drawn Vehicles and Machinery

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Drivers of Animal-drawn Vehicles and Machinery (ISCO-08 9332) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 11% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Collecting fares or charges;”

Scores 0.33 on the 2025 scale. Collecting fares or charges primarily involves handling financial transactions and processing customer interactions—a domain where Generative AI can offer partial automation, particularly in processing digital payments and maintaining transaction records. Similar tasks in the provided context, such as "Collecting payments for services rendered" (adjusted score: 0.25) and "Collecting payments from customers and giving change and fiscal receipts" (adjusted score: 0.275), indicate a moderate potential for automation. These tasks share elements of handling payments, but significant human oversight is necessary for cash transactions, dealing with discrepancies, and customer interaction due to the need for interpersonal skills and real-time decision-making. Automated systems can be used for routine digital payments, but human intervention remains crucial to ensure accurate processing and to handle exceptions or disputes, especially in cash transactions. As conducted in a high-income country like Poland, the infrastructure supports technological integration, aligning the task with a moderate score of 0.3 while acknowledging the non-automatable human aspects.

Moving fastest, 2023 → 2025

“Collecting fares or charges;”

Model capability on this task changed by +0.13 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 9332, 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.

• Material Moving Workers, All Other
• Transportation Workers, All Other

In context

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

Drivers of Animal-drawn Vehicles and Machinery sit at the 11th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Drivers of Animal-drawn Vehicles and Machinery rank in the 11th 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 rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Collecting fares or charges;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Drivers of Animal-drawn Vehicles and Machinery". https://singulariki.com/gradient/9332-drivers-of-animal-drawn-vehicles-and-machinery.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.