Fruit, Vegetable and Related Preservers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Fruit, Vegetable and Related Preservers (ISCO-08 7514) score an average of 0.15 on a 0–1 exposure scale — more exposed than about 17% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Mixing and adding ingredients such as pectin, sugar, spices and vinegar to assist preservation and enhance texture, appearance and flavour;”

Scores 0.21 on the 2025 scale. The task of mixing and adding ingredients like pectin, sugar, spices, and vinegar to enhance food texture and flavor involves both physical manipulation and judgment based on sensory feedback (e.g., taste, consistency). This nature of work is similar to the task of "Preparing various types of fillings" (score: 0.15), which also involves manual dexterity and sensory evaluation, limiting the automation potential even in high-income contexts with extensive access to technology like Poland. However, unlike purely physical tasks, there is some potential for AI to assist in optimization or recipe recommendations. Additionally, tasks like "Preparing and dosing additives to paper pulp" (score: 0.25) involve similar components of manual preparation and process optimization. The adjusted score of 0.245 reflects AI's ability to partially support but not completely automate the process, considering human expertise in adjusting recipes and handling the physical aspects of food preparation.

Moving fastest, 2023 → 2025

“Mixing and adding ingredients such as pectin, sugar, spices and vinegar to assist preservation and enhance texture, appearance and flavour;”

Model capability on this task changed by +0.11 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 7514, 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 Processing Workers, All Other
• Food Cooking Machine Operators and Tenders

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Fruit, Vegetable and Related Preservers sit at the 17th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Fruit, Vegetable and Related Preservers rank in the 17th 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: "Mixing and adding ingredients such as pectin, sugar, spices and vinegar to assist preservation and enhance texture, appearance and flavour;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Fruit, Vegetable and Related Preservers". https://singulariki.com/gradient/7514-fruit-vegetable-and-related-preservers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.