Elementary Workers Not Elsewhere Classified

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Elementary Workers Not Elsewhere Classified (ISCO-08 9629) score an average of 0.29 on a 0–1 exposure scale — more exposed than about 55% 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

“Selling admission tickets and passes to patrons at entertainment events or collecting tickets, passes and tags from patrons;”

Scores 0.48 on the 2025 scale. The task of selling admission tickets and passes at entertainment events or collecting them from patrons aligns closely with tasks like "issuing passes" (score 0.485) and "checking tickets at the entrance" (score 0.4). Both tasks require a combination of physical interaction, verification, and some aspects that can be streamlined with technology, such as automated ticket scanners or digital ticket distribution. In a high-income country like Poland, where digital transactions and internet access are prevalent, Generative AI can facilitate part of the process through automated systems for ticket selling and digital checking at entry points. However, human presence is still crucial for handling physical or security-related tasks and addressing customer service scenarios requiring direct interaction, such as assisting patrons or resolving ticketing issues. Therefore, a slightly higher score than "issuing passes" reflects the potential of AI to automate significant portions of the task, balanced by the need to maintain a human element for security and customer interaction.

Moving fastest, 2023 → 2025

“Examining tickets or passes to verify authenticity, using criteria such as colour and date issued;”

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 9629, 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.

• Amusement and Recreation Attendants
• Parking Attendants
• Ushers, Lobby Attendants, and Ticket Takers
• Locker Room, Coatroom, and Dressing Room Attendants
• Motion Picture Projectionists

In context

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

Elementary Workers Not Elsewhere Classified sit at the 55th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Elementary Workers Not Elsewhere Classified rank in the 55th 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 0.09 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selling admission tickets and passes to patrons at entertainment events or collecting tickets, passes and tags from patrons;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Elementary Workers Not Elsewhere Classified". https://singulariki.com/gradient/9629-elementary-workers-not-elsewhere-classified.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.