Share and intensity of work current AI systems can materially affect.
SOC 19-1032
Foresters AI displacement risk
Foresters plan timber harvests, manage conservation projects, and negotiate land agreements. Satellite imagery and LiDAR now inventory stands remotely, changing how foresters measure — but harvest plans, contract negotiation, and walking the tract to verify what the imagery claims stay in the field.
Likely potential for exposed tasks to move to software after workflow integration.
Remote sensing makes one forester cover more ground, which is augmentation: the same professional reviews more acres with better data. Regulatory compliance and landowner negotiation keep the role relationship-and-judgment anchored.
Distribution
Where Foresters sits across 620 tracked roles
Displacement pressure 20 — higher than 23% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
25 O*NET task statements matched to SOC 19-1032. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $76,400 (May 2025, US national). The latest BLS row matched SOC 19-1032.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Foresters
SOC 19-1032 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 20/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Foresters
The current evidence import matched 25 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks25
SOC19-1032
- Core task / ID 196
Establish short- and long-term plans for management of forest lands and forest resources.
- Core task / ID 212
Plan cutting programs and manage timber sales from harvested areas, assisting companies to achieve production goals.
- Core task / ID 202
Determine methods of cutting and removing timber with minimum waste and environmental damage.
- Core task / ID 200
Negotiate terms and conditions of agreements and contracts for forest harvesting, forest management and leasing of forest lands.
- Core task / ID 207
Perform inspections of forests or forest nurseries.
- Core task / ID 195
Monitor contract compliance and results of forestry activities to assure adherence to government regulations.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Establish plans for management of forest lands
Exposure 38, automation 16%, augmentation 58%.
O*NET evidence: Establish short- and long-term plans for management of forest lands and forest resources. (ID 196)
Plan cutting programs and manage timber sales
Exposure 36, automation 16%, augmentation 54%.
O*NET evidence: Plan cutting programs and manage timber sales from harvested areas, assisting companies... (ID 212)
Perform inspections of forests and nurseries
Exposure 24, automation 9%, augmentation 44%.
O*NET evidence: Perform inspections of forests or forest nurseries. (ID 207)
Negotiate terms for harvesting and land leasing
Exposure 30, automation 12%, augmentation 52%.
O*NET evidence: Negotiate terms and conditions of agreements and contracts for forest harvesting, fores... (ID 200)
TaskExposureAutomationAugmentation
Establish plans for management of forest lands3816%58%
Plan cutting programs and manage timber sales3616%54%
Perform inspections of forests and nurseries249%44%
Negotiate terms for harvesting and land leasing3012%52%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowForestry Operations Manager
Training horizon: 6-12 months. Skill overlap 66. Wage preservation signal 122.
- Lead harvest programs
- Own contractor performance
- Manage certification audits
role redesign
LowGIS Forest Analyst
Training horizon: 3-6 months. Skill overlap 62. Wage preservation signal 110.
- Master remote-sensing workflows
- Automate stand inventories
- Serve land-management clients
Comparison guides
Compare the next move before you commit
6-12 months
Foresters to Forestry Operations Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Foresters into Forestry Operations Manager.
3-6 months
Foresters to GIS Forest Analyst
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Foresters into GIS Forest Analyst.
What the AI risk score means for Foresters
The displacement pressure score for Foresters is 20. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Establish plans for management of forest lands carries 16% automation pressure, while Establish plans for management of forest lands carries 58% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $76,400 (May 2025, US national). Employment context: Forest management with remote-sensing augmentation. Typical education: Bachelor degree in forestry; state registration in many states.
Wage vulnerability is 32, while transition feasibility is 62. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- LiDAR inventories stands remotely
- Contract negotiation stays human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Foresters, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Forest management planning
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
LiDAR data analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Timber contract negotiation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Field inspection
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Forestry Operations Manager, such as lead harvest programs.
- By 90 days, compare internal openings and external postings for Forestry Operations Manager or GIS Forest Analyst and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Foresters
Will AI replace Foresters?
Foresters plan timber harvests, manage conservation projects, and negotiate land agreements. Satellite imagery and LiDAR now inventory stands remotely, changing how foresters measure — but harvest plans, contract negotiation, and walking the tract to verify what the imagery claims stay in the field. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Foresters work are most exposed to AI?
Establish plans for management of forest lands and Plan cutting programs and manage timber sales show the strongest automation pressure in this model. Establish plans for management of forest lands and Plan cutting programs and manage timber sales are better treated as AI-augmented work.
What should Foresters learn next?
Start with Forest management planning, LiDAR data analysis, Timber contract negotiation. The most practical adjacent paths in this model are Forestry Operations Manager and GIS Forest Analyst.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics