Bus and Tram Drivers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Bus and Tram Drivers (ISCO-08 8331) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 26% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Controlling lighting, heating and ventilation on buses and trams;”

Scores 0.25 on the 2025 scale. The task of controlling lighting, heating, and ventilation on buses and trams involves a combination of monitoring and making manual adjustments, which can partially be supported by Generative AI through automated systems, such as IoT devices and sensors capable of managing environmental conditions. Similar tasks in the provided context indicate limited automation potential, especially those involving real-time decision-making and physical presence, like maintaining cleanliness or ensuring safety which had lower scores due to their manual nature. Tasks like monitoring temperature or controlling central heating show a moderate potential automation score due to the possible integration of AI and IoT for monitoring and adjusting purposes. Considering these factors and that this task will be carried out in a high-income country like Poland with robust access to technology, the adjusted score reflects a moderate potential for AI to support but not replace the human interaction required for these operations.

Moving fastest, 2023 → 2025

“Observing traffic to ensure safe progress;”

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 8331, 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.

• Subway and Streetcar Operators

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Bus and Tram Drivers sit at the 26th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Bus and Tram Drivers rank in the 26th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Controlling lighting, heating and ventilation on buses and trams;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Bus and Tram Drivers". https://singulariki.com/gradient/8331-bus-and-tram-drivers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.