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AI Automation Index
Will AI Replace Fire-Prevention and Protection Engineers?
Task-level automation exposure for Fire-Prevention and Protection Engineers: 14 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
57%
weighted task index
14 of 14 tasks scored
Median Salary
$115,160 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
21,450 people hold this job — size, not growth
Projected 2025–35
+5.4%
employment change
1,200 openings a year · BLS line item
Pay by state
BLS OEWS publishes 45 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$76,700 – $131,540
Highest medians
California $131,540
Massachusetts $131,210
New Hampshire $130,500
Ohio $129,560
Washington $126,590
Lowest medians
West Virginia $76,700
Maine $82,140
Hawaii $82,300
Indiana $83,240
Mississippi $87,180
State figures come from the 17-2111 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 57%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 84%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 8%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 9%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 12/100
Low
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
14 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) 42%
6 tasks
Medium exposure (≥ 35%) 50%
7 tasks
Low exposure (below 35%) 8%
1 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 08/2025 · Occupational Expert
Develop or modify building codes and fire codes.
- New 08/2025 · Occupational Expert
Perform testing on fire protection equipment or building features, such as fire doors or smoke control systems.
- Revision 08/2025 · Occupational Expert
Review building plans to verify compliance with fire and building codes.
Replaced: Review building plans to verify compliance with fire code.
Constituent Tasks
#8984 · Core · 55% automatable
Advise architects, builders, and other construction personnel on fire prevention equipment and techniques and on fire code and standard interpretation and compliance.
decision support — General safety advice; accountability and context human
Advise others on health and safety issues.
#8982 · Core · 55% automatable
Design fire detection equipment, alarm systems, and fire extinguishing devices and systems.
text generation — Fire plans drafted; code compliance and site review human
Develop fire safety or prevention programs or plans.
#8983 · Core · 22% automatable
Inspect buildings or building designs to determine fire protection system requirements and potential problems in areas such as water supplies, exit locations, and construction materials.
visual inspection — Physical site verification against specs is human
Inspect facilities or sites to determine if they meet specifications or standards.
#24004 · Core · 63% automatable
Revision
Review building plans to verify compliance with fire code.
text generation — Document compliance checking against requirements is automatable
Evaluate construction projects to determine compliance with external standards or regulations. Examine documents to verify adherence to requirements. Review documents or materials for compliance with policies or regulations.
#20383 · Core · 75% automatable
Prepare and write reports detailing specific fire prevention and protection issues, such as work performed, revised codes or standards, and proposed review schedules.
text generation — Operational reports drafted from data
Prepare technical or operational reports.
#8988 · Core · 55% automatable
Consult with authorities to discuss safety regulations and to recommend changes as necessary.
decision support — General safety advice; accountability and context human
Advise others on health and safety issues.
#8993 · Core · 65% automatable
Evaluate fire department performance and the laws and regulations affecting fire prevention or fire safety.
information retrieval — Law-to-activity mapping; human owns compliance
Evaluate applicable laws and regulations to determine impact on organizational activities.
#23902 · Core · 37% automatable
Direct the purchase, modification, installation, testing, maintenance, and operation of fire prevention and protection systems.
decision support — Compliance coordination needs human accountability and site action
Coordinate safety or regulatory compliance activities. Direct installation activities. Direct equipment maintenance or repair activities.
#8991 · Core · 80% automatable
Attend workshops, seminars, or conferences to present or obtain information regarding fire prevention and protection.
information retrieval — AI tracks technical updates; human validates depth
Update technical knowledge.
#8986 · Core · 55% automatable
Determine causes of fires and ways in which they could have been prevented.
decision support — Root-cause hypotheses; human verifies in the field
Determine causes of operational problems or failures.
#8992 · Core · 55% automatable
Develop training materials and conduct training sessions on fire protection.
text generation — Safety standards content AI-draftable; delivery and site context human
Teach safety standards or environmental compliance methods.
#8990 · Core · 47% automatable
Study the relationships between ignition sources and materials to determine how fires start.
information retrieval — AI researches and synthesizes; human judges relevance
Examine debris to obtain information about causes of fires. Research topics in area of expertise.
#8989 · Core · 72% automatable
Develop plans for the prevention of destruction by fire, wind, and water.
text generation — Detailed work plans drafted from specifications
Prepare detailed work plans.
#8994 · Core · 67% automatable
Conduct research on fire retardants and the fire safety of materials and devices.
information retrieval — AI researches and synthesizes; human judges relevance
Research topics in area of expertise. Conduct research to inform art, designs, or other work.
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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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