Hand and Pedal Vehicle Drivers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Hand and Pedal Vehicle Drivers (ISCO-08 9331) score an average of 0.21 on a 0–1 exposure scale — more exposed than about 36% 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 5 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 0.34 on the 2025 scale. The task of collecting fares or charges shares similarities with several semantically proximal tasks, such as "Collecting payment from passengers and passing on to authorized station employees those passengers refusing to promptly pay charges on the train or identify themselves" (0.25), and "Collecting payments from customers and giving change and fiscal receipts" (0.375). Like these tasks, collecting fares involves a direct human interaction component, primarily when dealing with cash transactions or ensuring compliance with payment requirements. Generative AI can contribute by automating parts of the process, such as recording digital transactions, managing data entries, and possibly integrating with digital payment systems to streamline the payment collection process. However, the need for human judgment and interpersonal skills in addressing issues during transactions or handling non-compliance is irreplaceable by AI, as seen in the limitations for tasks involving direct human interaction. Given the technological infrastructure available in Poland, the potential for partial automation is acknowledged, but the need for human intervention leads to a balanced adjusted score that recognizes both aspects. Therefore, the adjusted score of 0.32 reflects the moderate potential for AI-assisted automation, while significant human engagement remains necessary.

Moving fastest, 2023 → 2025

“Inspecting vehicle components to identify wear and damage;”

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 9331, 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.

• Couriers and Messengers
• 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.

Hand and Pedal Vehicle Drivers sit at the 36th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Hand and Pedal Vehicle Drivers rank in the 36th 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 0.06 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 — "Hand and Pedal Vehicle Drivers". https://singulariki.com/gradient/9331-hand-and-pedal-vehicle-drivers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.