Fast Food Preparers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Fast Food Preparers (ISCO-08 9411) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 28% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Taking and serving food and beverage orders in eating places that specialize in fast service and carry-out food;”

Scores 0.25 on the 2025 scale. The task of taking and serving food and beverage orders in fast-service and carry-out eating places involves a mix of physical actions, real-time customer interaction, and decision-making, which are not fully automatable with current Generative AI capabilities. Semantically similar tasks such as "Accepting and processing orders" (automation score: 0.325) and "Issuing completed orders" (automation score: 0.365) highlight that while AI can assist with order entry, tracking, and processing, the physical delivery and nuanced customer engagement components require human intervention. The need for human oversight in ensuring customer satisfaction and handling non-standard requests further limits full automation. In a high-income country like Poland, where digital tools are readily accessible, AI can be leveraged to enhance efficiency in routine elements of the task, but it cannot replace the human element needed for customer service and precise handling. As such, a score of 0.25 reflects the partial automation potential with significant reliance on human involvement.

Moving fastest, 2023 → 2025

“Taking and serving food and beverage orders in eating places that specialize in fast service and carry-out food;”

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 9411, 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.

• Cooks, Fast Food
• Cooks, Short Order

In context

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

Fast Food Preparers sit at the 28th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Fast Food Preparers rank in the 28th 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 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Taking and serving food and beverage orders in eating places that specialize in fast service and carry-out food;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Fast Food Preparers". https://singulariki.com/gradient/9411-fast-food-preparers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.