Cashiers and Ticket Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Cashiers and Ticket Clerks (ISCO-08 5230) score an average of 0.39 on a 0–1 exposure scale — more exposed than about 76% 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

“Issuing tickets for attendance at sporting and cultural events;”

Scores 0.61 on the 2025 scale. The task of issuing tickets for attendance at sporting and cultural events involves processing structured data and executing repetitive processes, which are highly suitable for Generative AI automation. AI can manage tasks such as generating and processing electronic tickets, validating payment methods, and even managing databases of sold tickets. Among the semantically similar tasks provided, issuing sales invoices has an adjusted score of 0.68, illustrating strong automation potential due to the structured nature of invoicing, akin to ticket issuance. However, unlike more complex tasks, this role has minimal requirements for human creativity or subjective judgment, but it still demands some human oversight for handling exceptions or customer service interactions, similar to the task of issuing certificates and managing insurance data, which scored 0.625. Considering these factors and acknowledging the accessibility of digital tools in a high-income country like Poland, the adjusted score of 0.58 reflects a significant, but not complete, potential for automation using current AI capabilities.

Moving fastest, 2023 → 2025

“Counting and recording money received or paid out and balancing against cash register sales records;”

Model capability on this task changed by +0.35 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 5230, 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.

• Cashiers
• Gambling Change Persons and Booth Cashiers

In context

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

Cashiers and Ticket Clerks sit at the 76th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Cashiers and Ticket Clerks rank in the 76th 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 0.14 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Issuing tickets for attendance at sporting and cultural events;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Cashiers and Ticket Clerks". https://singulariki.com/gradient/5230-cashiers-and-ticket-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.