Shopkeepers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Shopkeepers (ISCO-08 5221) score an average of 0.43 on a 0–1 exposure scale — more exposed than about 81% 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 2 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Budgeting and maintaining records of stock levels and financial transactions;”

Scores 0.59 on the 2025 scale. The task of budgeting and maintaining records of stock levels and financial transactions involves both structured data handling and financial management. This task aligns closely with other tasks in the clerk category, such as "Preparing sales registers" (Score: 0.65) and "Issuing sales invoices" (Score: 0.68), which have high automation potential due to their structured nature. Generative AI can effectively automate data processing, generate reports, and assist in maintaining budgetary controls, similar to tasks involving data entry and financial record management. However, unlike purely automated tasks, budgeting requires strategic decision-making and contextual judgment, which currently limits full automation. In comparison, tasks involving budgeting and financial responsibility, like "Maintaining treasury books" (Score: 0.2775), show lower automation potential due to their complexity and the need for human oversight. The capability of AI to assist in financial operations is significant but not complete, due to the necessity of human expertise in strategic decisions and error checking. Given the high-income context of Poland with ready access to digital tools, the potential for partial automation is enhanced, leading to an adjusted score of 0.58, which reflects the balance between AI assistance and necessary human intervention in managing financial records and budgets.

Moving fastest, 2023 → 2025

“Taking inventory of goods in stock.”

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 5221, 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.

• General and Operations Managers

In context

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

Shopkeepers sit at the 81st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Shopkeepers rank in the 81st 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 0.18 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Budgeting and maintaining records of stock levels and financial transactions;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Shopkeepers". https://singulariki.com/gradient/5221-shopkeepers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.