Mobile Farm and Forestry Plant Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Mobile Farm and Forestry Plant Operators (ISCO-08 8341) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 8% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Driving and tending tractor-drawn or self-propelled special-purpose forestry machinery to clear land, plant, harvest and carry trees and timber or perform other forestry operations;”

Scores 0.15 on the 2025 scale. The task of driving and tending to special-purpose forestry machinery involves significant physical control and real-time decision-making, similar to tasks like operating agricultural machinery and tending equipment. The adjusted scores for semantically similar tasks include operating agricultural machinery (0.165) and performing soil preparation (0.0475), both emphasizing the manual and physical nature of the tasks, which generative AI cannot currently replace. Additionally, while AI can assist with planning and route optimization, the primary operation demands human judgment, dexterity, and presence. Given the high-income context of Poland, AI could enhance operational efficiency in support roles, but the core physical duties remain human-oriented. Considering these factors and the limitations of AI in automating such physically intensive tasks, an adjusted score of 0.15 reflects the minimal role AI would play in directly automating this task.

Moving fastest, 2023 → 2025

“Driving and tending tractor-drawn or self-propelled special-purpose forestry machinery to clear land, plant, harvest and carry trees and timber or perform other forestry operations;”

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 8341, 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.

• Agricultural Equipment Operators
• Logging Equipment Operators

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Mobile Farm and Forestry Plant Operators sit at the 8th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mobile Farm and Forestry Plant Operators rank in the 8th 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: "Driving and tending tractor-drawn or self-propelled special-purpose forestry machinery to clear land, plant, harvest and carry trees and timber or perform other forestry operations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mobile Farm and Forestry Plant Operators". https://singulariki.com/gradient/8341-mobile-farm-and-forestry-plant-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.