Food and Related Products Machine Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Food and Related Products Machine Operators (ISCO-08 8160) score an average of 0.15 on a 0–1 exposure scale — more exposed than about 18% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Mixing, pulping, grinding, blending and separating foodstuffs and liquids with churning, pressing, sieving, grinding and filtering equipment;”

Scores 0.19 on the 2025 scale. The task of mixing, pulping, grinding, blending, and separating foodstuffs and liquids with churning, pressing, sieving, grinding, and filtering equipment involves substantial physical manipulation and equipment operation, similar to tasks like operating machines in dairy production (adjusted score 0.12) and processing meat (score 0.155). These tasks demand physical presence and decision-making skills, suggesting limited potential for automation. Generative AI may support optimization and monitoring processes; however, it cannot perform the hands-on aspects. The similarity to tasks requiring manual dexterity and real-time adjustments suggests a moderate to low level of automation potential, slightly higher than purely physical maintenance tasks but lower than jobs primarily involving data analysis. Considering Poland's high digital infrastructure, the potential use of smart systems and improved efficiency might marginally increase the automation feasibility, resulting in an adjusted score of 0.23.

Moving fastest, 2023 → 2025

“Setting, operating and attending machinery and ovens to mix, bake and otherwise prepare bread and flour confectionery products;”

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 8160, 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 Batchmakers
• Food Processing Workers, All Other
• Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders
• Food Cooking Machine Operators and Tenders
• Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders
• Cooling and Freezing Equipment Operators and Tenders

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Food and Related Products Machine Operators sit at the 18th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Food and Related Products Machine Operators rank in the 18th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Mixing, pulping, grinding, blending and separating foodstuffs and liquids with churning, pressing, sieving, grinding and filtering equipment;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Food and Related Products Machine Operators". https://singulariki.com/gradient/8160-food-and-related-products-machine-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.