Forestry Labourers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Forestry Labourers (ISCO-08 9215) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 2% 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

“Performing minor repairs and maintenance of forest roads, buildings, facilities, and equipment.”

Scores 0.13 on the 2025 scale. The task of "Performing minor repairs and maintenance of forest roads, buildings, facilities, and equipment" is inherently physical and involves on-site problem-solving, which current Generative AI cannot fully automate. Semantically similar tasks, such as "Performing minor repairs and maintenance tasks on roads, buildings, facilities, and forestry equipment" and "Performing maintenance and minor repairs on harnesses and logging equipment," have adjusted scores ranging between 0.075 to 0.15 due to the physical, hands-on nature involved. These tasks require manual dexterity, real-time judgments, and contextual adjustments in dynamic environments, reflecting limited automation potential. While Generative AI might assist with scheduling, planning, or providing diagnostic advice, the actual execution and nuanced understanding required for physical repairs remain largely human tasks. Given Poland's high-income status and technological infrastructure, AI can play a supportive role, but the core repair tasks are not fully automatable. Therefore, an adjusted score of 0.13 reflects this limited potential for AI automation, considering the constraints and comparisons with related tasks.

Moving fastest, 2023 → 2025

“Collecting seeds, and planting seedlings;”

Model capability on this task changed by +0.06 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 9215, 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.

• Forest and Conservation Workers
• Logging 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.

Forestry Labourers sit at the 2nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Forestry Labourers rank in the 2nd 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: "Performing minor repairs and maintenance of forest roads, buildings, facilities, and equipment.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Forestry Labourers". https://singulariki.com/gradient/9215-forestry-labourers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.