Service Station Attendants

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Service Station Attendants (ISCO-08 5245) score an average of 0.24 on a 0–1 exposure scale — more exposed than about 44% 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 Minimal 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

“Undertaking stock control and preparing reports on fuel, oil, accessories and other items sold.”

Scores 0.56 on the 2025 scale. The task of undertaking stock control and preparing reports on fuel, oil, accessories, and other items sold shares characteristics with tasks that involve data handling, inventory management, and reporting. Generative AI can greatly enhance efficiency in these areas by automating data reconciliation, inventory tracking, and generating structured reports, which aligns it closely with tasks such as "Preparing sales registers" (0.65) and "Issuing sales invoices" (0.68). However, the physical aspects of stock control and the need for human oversight in resolving discrepancies and ensuring compliance, as seen in tasks like "Conducting control of medicines" (0.275) and "Receiving and documenting goods deliveries at a fuel station" (0.25), require human intervention. Given the task's reliance on data management and reporting, it is more automatable than tasks with significant physical elements, but less so than purely digital tasks. Therefore, considering the typical capabilities of Generative AI and the necessity for human oversight in a high-income country context like Poland, an adjusted score of 0.58 reflects a balanced potential for automation.

Moving fastest, 2023 → 2025

“Filling fuel tanks and containers to level specified by customer;”

Model capability on this task changed by +0.12 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 5245, 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.

• Automotive and Watercraft Service Attendants

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Service Station Attendants sit at the 44th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Service Station Attendants rank in the 44th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Undertaking stock control and preparing reports on fuel, oil, accessories and other items sold.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Service Station Attendants". https://singulariki.com/gradient/5245-service-station-attendants.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.