Share and intensity of work current AI systems can materially affect.
SOC 19-4071
Forest and Conservation Technicians AI displacement risk
Forest and conservation technicians thin stands, patrol forests, map tract data, and lead seasonal crews. Drones and digital mapping improve surveying and fire detection, but the work itself happens in terrain: cutting, planting, patrolling, and enforcing regulations where the trees are.
Likely potential for exposed tasks to move to software after workflow integration.
This is a physical-presence role with modest automation exposure. Remote sensing changes how technicians target their work; it does not walk the tract, run the chainsaw, or lead the fire crew.
Distribution
Where Forest and Conservation Technicians 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.
20 O*NET task statements matched to SOC 19-4071. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $54,560 (May 2025, US national). The latest BLS row matched SOC 19-4071.
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 Forest and Conservation Technicians
SOC 19-4071 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 Forest and Conservation Technicians
The current evidence import matched 20 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 tasks20
SOC19-4071
- Core task / ID 5566
Thin and space trees and control weeds and undergrowth, using manual tools and chemicals, or supervise workers performing these tasks.
- Core task / ID 5563
Train and lead forest and conservation workers in seasonal activities, such as planting tree seedlings, putting out forest fires, and maintaining recreational facilities.
- Core task / ID 5570
Provide information about, and enforce, regulations, such as those concerning environmental protection, resource utilization, fire safety, and accident prevention.
- Core task / ID 5569
Patrol park or forest areas to protect resources and prevent damage.
- Core task / ID 21061
Map forest tract data using digital mapping systems.
- Supplemental task / ID 5571
Keep records of the amount and condition of logs taken to mills.
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
Thin and space trees and control undergrowth
Exposure 20, automation 8%, augmentation 30%.
O*NET evidence: Thin and space trees and control weeds and undergrowth, using manual tools and chemical... (ID 5566)
Train and lead forest and conservation workers
Exposure 22, automation 7%, augmentation 44%.
O*NET evidence: Train and lead forest and conservation workers in seasonal activities, such as planting... (ID 5563)
Patrol park and forest areas to protect resources
Exposure 20, automation 8%, augmentation 34%.
O*NET evidence: Patrol park or forest areas to protect resources and prevent damage. (ID 5569)
Map forest tract data using digital systems
Exposure 48, automation 26%, augmentation 58%.
O*NET evidence: Map forest tract data using digital mapping systems. (ID 21061)
TaskExposureAutomationAugmentation
Thin and space trees and control undergrowth208%30%
Train and lead forest and conservation workers227%44%
Patrol park and forest areas to protect resources208%34%
Map forest tract data using digital systems4826%58%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowForester
Training horizon: 24-36 months. Skill overlap 58. Wage preservation signal 134.
- Complete a forestry degree
- Earn state registration
- Lead management plans
role redesign
LowWildfire Management Specialist
Training horizon: 6-12 months. Skill overlap 62. Wage preservation signal 118.
- Earn fire qualifications
- Run prescribed burns
- Coordinate response crews
Comparison guides
Compare the next move before you commit
24-36 months
Forest and Conservation Technicians to Forester
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Forest and Conservation Technicians into Forester.
6-12 months
Forest and Conservation Technicians to Wildfire Management Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Forest and Conservation Technicians into Wildfire Management Specialist.
What the AI risk score means for Forest and Conservation Technicians
The displacement pressure score for Forest and Conservation Technicians 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. Map forest tract data using digital systems carries 26% automation pressure, while Map forest tract data using digital systems 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: $54,560 (May 2025, US national). Employment context: Fieldwork-heavy role with drone and mapping assists. Typical education: Associate degree common.
Wage vulnerability is 58, 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
- Drones improve fire detection
- Terrain keeps work physical
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Forest and Conservation Technicians, 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.
Field technique
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.
Crew leadership
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.
GIS data
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.
Regulatory enforcement
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 Forester, such as complete a forestry degree.
- By 90 days, compare internal openings and external postings for Forester or Wildfire Management Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Forest and Conservation Technicians
Will AI replace Forest and Conservation Technicians?
Forest and conservation technicians thin stands, patrol forests, map tract data, and lead seasonal crews. Drones and digital mapping improve surveying and fire detection, but the work itself happens in terrain: cutting, planting, patrolling, and enforcing regulations where the trees are. 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 Forest and Conservation Technicians work are most exposed to AI?
Map forest tract data using digital systems and Thin and space trees and control undergrowth show the strongest automation pressure in this model. Map forest tract data using digital systems and Train and lead forest and conservation workers are better treated as AI-augmented work.
What should Forest and Conservation Technicians learn next?
Start with Field technique, Crew leadership, GIS data. The most practical adjacent paths in this model are Forester and Wildfire Management Specialist.
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