Farming, Forestry and Fisheries Advisers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Farming, Forestry and Fisheries Advisers (ISCO-08 2132) score an average of 0.29 on a 0–1 exposure scale — more exposed than about 54% 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

“Preparing scientific reports and conducting advisory information sessions and lectures for farming, forestry and fishing communities and other groups.”

Scores 0.54 on the 2025 scale. The task of preparing scientific reports and conducting advisory information sessions and lectures for farming, forestry, and fishing communities involves significant human interaction, data presentation, and the interpretation of complex scientific information, which limits full automation. However, Generative AI can substantially aid in drafting reports, organizing technical content, and suggesting improvements for presentations. Adjusted scores for semantically similar tasks in the context range widely, but key similarities can be seen with tasks like "Developing publications about the conducted work" (0.55), where AI assists in content generation and organization while requiring human expertise for final interpretation and personalization. The task is less reliant on purely scientific analysis like "Preparing reports and expert opinions" (0.65) but aligns more closely with communication-focused tasks. The assumption of a high-income country like Poland enhances the feasibility of using AI for these automated assistance roles due to robust technological infrastructure. Given these factors, the score of 0.525 reflects the potential for AI to automate routine parts of report preparation and session content generation, while acknowledging the indispensable role of human expertise in delivery and nuanced advice.

Moving fastest, 2023 → 2025

“Developing procedures and techniques for solving agricultural problems and improving the efficiency of production;”

Model capability on this task changed by +0.27 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 2132, 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.

• Soil and Plant Scientists
• Farm and Home Management Educators
• Foresters

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Farming, Forestry and Fisheries Advisers sit at the 54th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Farming, Forestry and Fisheries Advisers rank in the 54th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing scientific reports and conducting advisory information sessions and lectures for farming, forestry and fishing communities and other groups.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Farming, Forestry and Fisheries Advisers". https://singulariki.com/gradient/2132-farming-forestry-and-fisheries-advisers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.