Shelf Fillers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Shelf Fillers (ISCO-08 9334) score an average of 0.20 on a 0–1 exposure scale — more exposed than about 34% 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

“Directing customers to location of articles sought;”

Scores 0.43 on the 2025 scale. The task of directing customers to the location of articles sought has similarities with tasks involving routine information delivery and customer assistance, akin to "Providing customers with full information about the goods for sale" (score 0.41) and "Assisting the buyer in choosing a product" (score 0.425). Generative AI can significantly aid in providing navigation-related information through interactive kiosks or mobile applications, suggesting optimized store layouts, or real-time directions. However, similar to providing comprehensive consumer product information, understanding unique customer inquiries and handling physical interactions still necessitates human intervention. Given the technological infrastructure in Poland and the nature of routine information processing, the task leans towards moderate automation potential but still respects the need for human presence in nuanced customer service situations. Therefore, an adjusted score of 0.42 accurately reflects this balance, acknowledging the capabilities of AI in information processing while recognizing the significance of human input.

Moving fastest, 2023 → 2025

“Noting what has been sold and collecting goods needed from the stockroom;”

Model capability on this task changed by +0.17 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 9334, 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.

No U.S. role resolves through the crosswalk for this occupation. Search the encyclopedia for the closest match →

In context

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

Shelf Fillers sit at the 34th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Shelf Fillers rank in the 34th 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 rose by 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Directing customers to location of articles sought;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Shelf Fillers". https://singulariki.com/gradient/9334-shelf-fillers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.