Kitchen Helpers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Kitchen Helpers (ISCO-08 9412) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 11% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Unpacking checking, transferring, weighing and storing supplies in refrigerators, cupboards and other storage areas;”

Scores 0.15 on the 2025 scale. The task of unpacking, checking, transferring, weighing, and storing supplies involves significant physical manipulation and interaction with physical goods, which current Generative AI cannot automate. This aligns with similar tasks such as storing and handling goods (e.g., storing meat, accepting baggage) and arranging items (e.g., placing goods in stores or preparing raw materials), each demonstrating limited automation potential due to their manual nature. These tasks received adjusted scores mostly below 0.2, indicating minimal automation capacity. Furthermore, while AI could assist with aspects like inventory management or planning through data analysis, the core actions require human physical involvement. Therefore, given the physical and manual nature of the task, and considering the capabilities of Generative AI in supporting rather than replacing human roles in handling and storing supplies, the adjusted score of 0.15 reflects the limited potential for full task automation. In a high-income country with robust digital infrastructure like Poland, the supporting role of AI might be slightly more pronounced in logistics management, yet still relies heavily on human effort for execution.

Moving fastest, 2023 → 2025

“Assembling dishes for service;”

Model capability on this task changed by +0.09 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 9412, 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.

• Food Preparation Workers
• Dining Room and Cafeteria Attendants and Bartender Helpers
• Dishwashers
• Food Preparation and Serving Related 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.

Kitchen Helpers sit at the 11th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Kitchen Helpers rank in the 11th 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 0.05 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Unpacking checking, transferring, weighing and storing supplies in refrigerators, cupboards and other storage areas;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Kitchen Helpers". https://singulariki.com/gradient/9412-kitchen-helpers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.