Shop Sales Assistants

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Shop Sales Assistants (ISCO-08 5223) score an average of 0.38 on a 0–1 exposure scale — more exposed than about 74% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Selling goods and services, accepting payment by a variety of payment methods, preparing sales invoices and recording sales using cash registers;”

Scores 0.55 on the 2025 scale. The task of selling goods and services, accepting payment by various payment methods, preparing sales invoices, and recording sales using cash registers involves a mix of processes that can be partially automated with Generative AI. Tasks with similar elements have automation scores ranging from moderate to high, such as "Preparing sales registers" (0.65), "Issuing sales invoices" (0.68), and "Collecting payments for sold products" (0.375). Generative AI can assist significantly in automating routine processes like generating invoices and processing digital transactions, as well as integrating data for record-keeping tasks. However, human oversight is needed to handle exceptions, interact with customers, and manage cash transactions that require physical presence and judgment. Therefore, given the environment of Poland, a high-income country with ubiquitous access to digital tools, the adjusted score reflects the balance between automation potential for structured, repetitive tasks and the necessary human engagement for interactive and exception-based elements.

Moving fastest, 2023 → 2025

“Selling goods and services, accepting payment by a variety of payment methods, preparing sales invoices and recording sales using cash registers;”

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 5223, 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.

• Retail Salespersons
• Parts Salespersons

In context

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

Shop Sales Assistants sit at the 74th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Shop Sales Assistants rank in the 74th 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 fell by 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selling goods and services, accepting payment by a variety of payment methods, preparing sales invoices and recording sales using cash registers;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Shop Sales Assistants". https://singulariki.com/gradient/5223-shop-sales-assistants.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.