Livestock Farm Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Livestock Farm Labourers (ISCO-08 9212) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 6% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Monitoring livestock and reporting on their condition;”

Scores 0.15 on the 2025 scale. Monitoring livestock and reporting on their condition involves significant observational skills, interpretation of nuanced behaviors, and real-time decision-making, similar to tasks like "Recognizing symptoms of diseases based on the appearance and behavior of livestock animals" (0.335) and "Conducting behavioral observations" (0.12). While Generative AI can aid in data collection and suggest potential issues based on historical data, it lacks the ability to fully automate the in-person observation and human judgment required in these scenarios. Also, physical tasks such as moving among livestock and assessing their condition visually and physically remain beyond current AI capabilities. Therefore, aligned with tasks that require observational expertise and manual interaction, a score of 0.15 reflects the limited yet supporting role AI can play in the data handling aspect, while acknowledging the technology's constraints concerning direct animal interaction in a high-income country setting.

Moving fastest, 2023 → 2025

“Assisting with herding, droving and separating livestock for milking, shearing, transportation or slaughter and between pastures;”

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 9212, 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.

• Farmworkers, Farm, Ranch, and Aquacultural Animals
• Agricultural Workers, All Other

In context

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

Livestock Farm Labourers sit at the 6th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Livestock Farm Labourers rank in the 6th 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: "Monitoring livestock and reporting on their condition;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Livestock Farm Labourers". https://singulariki.com/gradient/9212-livestock-farm-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.