Car, Taxi and Van Drivers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Car, Taxi and Van Drivers (ISCO-08 8322) score an average of 0.28 on a 0–1 exposure scale — more exposed than about 51% 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 1 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Determining most appropriate route;”

Scores 0.73 on the 2025 scale. The task of determining the most appropriate route is highly suited for automation with Generative AI technologies, especially in a high-income country like Poland where digital infrastructure is robust. The adjusted scores for semantically similar tasks, such as "Planning the optimal route for travel" with scores of 0.7225 and 0.7, indicate that route planning is well within the capabilities of AI. These tasks focus on analyzing multiple parameters such as traffic, distance, and time, aligning with AI's strengths in data processing and optimization. Compared to tasks that require dynamic human interaction or situational awareness, such as "Negotiating and agreeing with carriers" (score 0.3789), route planning can be more systematically automated. While human oversight might still be necessary for handling exceptions or last-minute changes, the core task of route planning can significantly benefit from AI automation, justifying an adjusted score of 0.72. This score reflects AI's advanced ability to perform this task autonomously, while still considering the occasional need for human intervention for special cases.

Moving fastest, 2023 → 2025

“Collecting fares, payments for deliveries, or documents certifying deliveries;”

Model capability on this task changed by +0.15 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 8322, 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.

• Light Truck Drivers
• Driver/Sales Workers
• Ambulance Drivers and Attendants, Except Emergency Medical Technicians

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.

Car, Taxi and Van Drivers sit at the 51st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Car, Taxi and Van Drivers rank in the 51st 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 rose by 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Determining most appropriate route;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Car, Taxi and Van Drivers". https://singulariki.com/gradient/8322-car-taxi-and-van-drivers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.