Street and Related Service Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Street and Related Service Workers (ISCO-08 9510) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 27% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Receiving immediate payment.”

Scores 0.34 on the 2025 scale. The task of "Receiving immediate payment," much like "Collecting payments for sold products" and "Accepting payments for sold goods," involves direct interactions with customers and handling transactions. Generative AI can assist with digital payment systems by automating data entry, generating receipts, and possibly integrating with POS systems, thus enhancing the efficiency of these interactions. However, the physical handling of cash and cards, along with nuanced customer interactions and real-time decision-making, demands human oversight. Given the context of Poland’s high access to technology, the potential for AI to automate data-driven elements is noteworthy but limited by the need for human engagement in physical and interactive aspects. Tasks in the same cluster had automation scores reflecting partial potential, such as "Accepting payments for sold goods and issuing receipts" (0.152) and "Collecting payments for sold products" (0.375). Therefore, an adjusted score of 0.32 accounts for AI's partial automation potential while acknowledging that human presence is necessary for physically handling transactions and engaging with customers. This aligns with the observed automation capabilities within similar tasks and respects the inherent limitations of current AI technology in managing complex, real-world interactions without human facilitation.

Moving fastest, 2023 → 2025

“Approaching people on the street to offer services;”

Model capability on this task changed by +0.05 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 9510, 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.

• Personal Care and Service Workers, All Other

In context

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

Street and Related Service Workers sit at the 27th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Street and Related Service Workers rank in the 27th 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 0.10 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Receiving immediate payment.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Street and Related Service Workers". https://singulariki.com/gradient/9510-street-and-related-service-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.