Poultry Producers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Poultry Producers (ISCO-08 6122) score an average of 0.19 on a 0–1 exposure scale — more exposed than about 30% 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 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Monitoring market activity, planning and coordinating production accordingly, maintaining and evaluating records of farming activities;”

Scores 0.41 on the 2025 scale. The task of "Monitoring market activity, planning and coordinating production accordingly, maintaining and evaluating records of farming activities" involves a combination of data analysis, strategic planning, and documentation management. Generative AI can significantly assist with analyzing market trends and generating insights for production planning and record-keeping. However, the need for human intervention in understanding local market nuances, strategic decision-making, and real-world execution presents limitations on full automation. Semantically similar tasks, such as managing crop documentation, strategic planning in farming, and coordinating production schedules, received scores between 0.41 and 0.50, due to their reliance on structured data management and decision-making processes that benefit from AI support but require human oversight. Given the high-income context in Poland, which allows extensive access to digital tools, this task's automation potential is moderate. The adjusted score of 0.375 reflects AI's capability to streamline the task but acknowledges the critical role of human expertise in strategic and context-specific matters.

Moving fastest, 2023 → 2025

“Training and supervising workers in poultry production procedures, maintenance duties and health and safety precautions, and hiring and discharging workers and contractors.”

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 6122, 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.

• First-Line Supervisors of Farming, Fishing, and Forestry Workers
• Animal Breeders

In context

Part of the 6 - Skilled agricultural, forestry and fishery workers major group. Return to the full gradient to see how the whole group sits.

Poultry Producers sit at the 30th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Poultry Producers rank in the 30th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Monitoring market activity, planning and coordinating production accordingly, maintaining and evaluating records of farming activities;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Poultry Producers". https://singulariki.com/gradient/6122-poultry-producers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.