Tobacco Preparers and Tobacco Products Makers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Tobacco Preparers and Tobacco Products Makers (ISCO-08 7516) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 15% 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

“Grading cured tobacco leaves by type, quality and locality where grown;”

Scores 0.21 on the 2025 scale. The task of grading cured tobacco leaves by type, quality, and locality involves nuanced judgment and sensory evaluation, similar to tasks such as determining the quality classes of fresh fruits and vegetables, assessing meat quality, and controlling food item quality. These tasks, while having some potential for AI assistance through image recognition and data analysis, still heavily rely on human sensory perception and judgment, which remain beyond the capabilities of current Generative AI to fully automate. The scores for semantically similar tasks, like determining fresh produce quality (0.35) and evaluating meat quality (0.235), highlight the limitations of AI in tasks requiring sensory feedback and subjective assessment. Given these factors and the high-income context of Poland with good tech access, an adjusted score of 0.28 reflects the limited but possible assistance from AI in data analysis, without replacing human expertise needed for the physical evaluation and nuanced grading processes in tobacco leaf assessment.

Moving fastest, 2023 → 2025

“Tending vacuum container which moistens tobacco for further processing;”

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 7516, 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.

• Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders
• Molders, Shapers, and Casters, Except Metal and Plastic
• Stone Cutters and Carvers, Manufacturing
• Glass Blowers, Molders, Benders, and Finishers
• Potters, Manufacturing
• Graders and Sorters, Agricultural Products

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.

Tobacco Preparers and Tobacco Products Makers sit at the 15th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Tobacco Preparers and Tobacco Products Makers rank in the 15th 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: "Grading cured tobacco leaves by type, quality and locality where grown;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Tobacco Preparers and Tobacco Products Makers". https://singulariki.com/gradient/7516-tobacco-preparers-and-tobacco-products-makers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.