Forestry and Related Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Forestry and Related Workers (ISCO-08 6210) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 7% 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

“Training and supervising other workers in forestry procedures, including forestry labourers and plant operators.”

Scores 0.21 on the 2025 scale. The task of "training and supervising other workers in forestry procedures" involves significant interpersonal engagement, practical knowledge transfer, and real-time situational assessment, akin to tasks like supervising the wood production process (adjusted score: 0.24) and planning and coordinating actions of forestry subcontractors (adjusted score: 0.25). These tasks require nuanced human judgment, oversight, and adaptability in dynamic environments, which are not currently feasible for Generative AI to fully automate. AI can support these tasks by offering training simulations, tracking compliance with procedures, and generating insights on worker performance. However, the actual supervision and training involve human interaction, empathy, and mentorship, all crucial aspects beyond AI's capabilities. The adjusted score reflects generative AI's capacity to assist in structuring training programs or analyzing process data but acknowledges the necessity for human leadership and guidance. Thus, the score of 0.23 aligns well with the moderate automation potential seen in supervisory and training tasks within similar contexts.

Moving fastest, 2023 → 2025

“Operating and maintaining a skidder, bulldozer or other prime mover to pull a variety of scarification or site preparation equipment over areas to be regenerated;”

Model capability on this task changed by +0.07 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 6210, 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
• Logging Equipment Operators
• Forest and Conservation Workers
• Fallers
• Log Graders and Scalers
• Logging Workers, All Other

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.

Forestry and Related Workers sit at the 7th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Forestry and Related Workers rank in the 7th 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: "Training and supervising other workers in forestry procedures, including forestry labourers and plant operators.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Forestry and Related Workers". https://singulariki.com/gradient/6210-forestry-and-related-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.