Shop Supervisors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Shop Supervisors (ISCO-08 5222) score an average of 0.36 on a 0–1 exposure scale — more exposed than about 66% 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

“Planning and preparing work schedules and assigning staff to specific duties;”

Scores 0.53 on the 2025 scale. The task of planning and preparing work schedules and assigning staff to specific duties is highly relevant for automation through Generative AI due to its structured nature and reliance on data processing, scheduling, and workforce management—all areas where AI excels. In the provided context, semantically similar tasks, such as organizing work teams and managing schedules (with automation scores ranging from 0.215 to 0.55), demonstrate varying automation potential depending on the degree of human oversight required. For example, "Planning and organizing work," with an adjusted score of 0.245, involves more strategic and interpersonal aspects, limiting automation. In contrast, tasks like "Planning the daily schedule for parcel delivery," with an adjusted score of 0.55, highlight high automation potential due to the structured nature of scheduling. Given the reliance on technological infrastructure in a high-income country like Poland, the task in question can be significantly automated, although it requires human oversight for complex, dynamic decision-making and adapting to unforeseen circumstances. Thus, an adjusted score of 0.5 represents a balance between the substantial AI capabilities for automating scheduling and planning tasks and the necessity for human flexibility and management.

Moving fastest, 2023 → 2025

“Ensuring that customers receive prompt service;”

Model capability on this task changed by +0.11 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 5222, 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.

• First-Line Supervisors of Retail Sales Workers

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 Supervisors sit at the 66th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Shop Supervisors rank in the 66th 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 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Planning and preparing work schedules and assigning staff to specific duties;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Shop Supervisors". https://singulariki.com/gradient/5222-shop-supervisors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.