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AI Automation Index
Will AI Replace Forest Fire Inspectors and Prevention Specialists?
Task-level automation exposure for Forest Fire Inspectors and Prevention Specialists: 16 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
35%
weighted task index
16 of 16 tasks scored
Median Salary
$56,870 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
2,780 people hold this job — size, not growth
Projected 2025–35
+13.1%
employment change
400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 15 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$31,720 – $97,370
Highest medians
California $97,370
Utah $90,020
Louisiana $75,480
Oregon $73,530
Maine $68,310
Lowest medians
Mississippi $31,720
Alabama $37,430
Arkansas $43,310
Idaho $47,050
Georgia $48,010
State figures come from the 33-2022 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 35%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 11%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 36%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 14%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 55/100
High
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
16 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 7%
1 tasks
Medium exposure (≥ 35%) 51%
8 tasks
Low exposure (below 35%) 42%
7 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 05/2025 · AI/SME
Operate drones to monitor and assess fire conditions, track fire progress, and identify safe access points for firefighters.
Constituent Tasks
#11102 · Core · 50% automatable
Relay messages about emergencies, accidents, locations of crew and personnel, and fire hazard conditions.
interpersonal — Live radio relay of incidents is real-time human
Relay information about incidents or emergencies to personnel using phones or two-way radios.
#21143 · Core · 44% automatable
Conduct wildland firefighting training.
interpersonal — Job-skill training content AI-assisted; coaching human
Train personnel to enhance job skills. Train personnel on proper operational procedures. Train employees in proper work procedures.
#11104 · Core · 35% automatable
Estimate sizes and characteristics of fires, and report findings to base camps by radio or telephone.
interpersonal — Live radio relay of incidents is real-time human
Assess characteristics of fires. Relay information about incidents or emergencies to personnel using phones or two-way radios.
#11103 · Core · 15% automatable
Direct crews working on firelines during forest fires.
physical manipulation — Firefighting direction is physical, live, high-stakes command
Direct fire fighting or prevention activities.
#11107 · Core · 20% automatable
Locate forest fires on area maps, using azimuth sighters and known landmarks.
visual inspection — Locating fires requires physical presence and vision
Locate fires or fire danger areas.
#11106 · Core · 8% automatable
Extinguish smaller fires with portable extinguishers, shovels, and axes.
physical manipulation — Firefighting equipment operated in hazardous physical space
Operate firefighting equipment.
#11112 · Core · 25% automatable
Patrol assigned areas, looking for forest fires, hazardous conditions, and weather phenomena.
visual inspection — Environmental hazard detection needs physical sensors
Patrol natural areas to ensure safety or enforce regulations. Monitor environmental conditions to detect hazards.
#11113 · Core · 45% automatable
Compile and report meteorological data, such as temperature, relative humidity, wind direction and velocity, and types of cloud formations.
information retrieval — Field measurement is human; AI helps record/interpret
Record information about environmental conditions.
#11109 · Core · 45% automatable
Examine and inventory firefighting equipment, such as axes, fire hoses, shovels, pumps, buckets, and fire extinguishers, to determine amount and condition.
data analysis — Materials inventory records and forecasting automatable
Inspect equipment to ensure safety or proper functioning. Maintain inventories of materials, equipment, or products.
#21144 · Core · 52% automatable
Educate the public about fire safety and prevention.
text generation — Educational content drafted; public delivery is human
Provide educational information to the public. Educate the public about fire safety or prevention. Provide safety training.
#11110 · Core · 43% automatable
Direct maintenance and repair of firefighting equipment, or requisition new equipment.
data analysis — Materials inventory records and forecasting automatable
Direct fire fighting or prevention activities. Maintain inventories of materials, equipment, or products.
#11108 · Core · 80% automatable
Maintain records and logbooks.
data analysis — Maintain operational records in structured systems
Maintain operational records.
#11105 · Core · 15% automatable
Administer regulations regarding sanitation, fire prevention, violation corrections, and related forest regulations.
physical manipulation — Firefighting direction is physical, live, high-stakes command
Direct fire fighting or prevention activities.
#11111 · Core · 6% automatable
Restrict public access and recreational use of forest lands during critical fire seasons.
physical manipulation — Physically blocking access to areas
Block physical access to restricted areas.
#11114 · Core · 22% automatable
Inspect camp sites to ensure that campers are in compliance with forest use regulations.
visual inspection — Security/safety walkthrough requires physical presence
Inspect facilities to ensure compliance with security or safety regulations.
#11115 · Core · 41% automatable
Inspect forest tracts and logging areas for fire hazards such as accumulated wastes or mishandling of combustibles, and recommend appropriate fire prevention measures.
decision support — Safety recommendations drafted; site verification human
Inspect facilities to ensure compliance with fire regulations. Recommend improvements to increase safety or reduce risks.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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