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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Range Managers?
Task-level automation exposure for Range Managers: 18 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
45%
weighted task index
18 of 18 tasks scored
Median Salary
$73,010 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
25,950 people hold this job — size, not growth
Projected 2025–35
+5.3%
employment change
2,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 53 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$50,080 – $99,400
Highest medians
District of Columbia $99,400
Colorado $84,820
Maryland $83,290
Idaho $81,330
Wyoming $81,310
Lowest medians
Virgin Islands $50,080
Florida $51,130
South Carolina $52,310
Mississippi $59,510
Pennsylvania $61,480
State figures come from the 19-1031 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 45%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 3%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 56%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 6%
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 4/100
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
18 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) 18%
3 tasks
Medium exposure (≥ 35%) 68%
12 tasks
Low exposure (below 35%) 15%
3 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 →Constituent Tasks
#7504 · Core · 45% automatable
Tailor conservation plans to landowners' goals, such as livestock support, wildlife, or recreation.
decision support — Conservation plans need field data and stakeholder judgment
Plan natural resources conservation or restoration programs.
#7506 · Core · 62% automatable
Study grazing patterns to determine number and kind of livestock that can be most profitably grazed and to determine the best grazing seasons.
information retrieval — Literature research strong; farm observation human
Research livestock management methods.
#7498 · Core · 45% automatable
Maintain soil stability and vegetation for non-grazing uses, such as wildlife habitats and outdoor recreation.
decision support — Resource plans need ecological and political judgment
Develop plans to manage natural or renewable resources.
#7500 · Core · 45% automatable
Manage forage resources through fire, herbicide use, or revegetation to maintain a sustainable yield from the land.
decision support — Resource plans need ecological and political judgment
Develop plans to manage natural or renewable resources.
#7499 · Core · 30% automatable
Mediate agreements among rangeland users and preservationists as to appropriate land use and management.
interpersonal — Mediation requires live facilitation and trust
Mediate disputes.
#7501 · Core · 62% automatable
Study rangeland management practices and research range problems to provide sustained production of forage, livestock, and wildlife.
information retrieval — Literature research strong; farm observation human
Research livestock management methods.
#7502 · Core · 55% automatable
Offer advice to rangeland users on water management, forage production methods, and control of brush.
decision support — Land management advice needs site-specific judgment
Advise others about land management or conservation.
#21170 · Core · 49% automatable
Coordinate with federal land managers and other agencies and organizations to manage and protect rangelands.
text generation — Drafting agency communications aided; relationships human
Confer with others to conduct or arrange operational activities. Communicate with government agencies.
#7497 · Core · 40% automatable
Measure and assess vegetation resources for biological assessment companies, environmental impact statements, and rangeland monitoring programs.
physical manipulation — Measuring environmental characteristics needs instruments
Measure environmental characteristics.
#7507 · Core · 37% automatable
Plan and implement revegetation of disturbed sites.
decision support — Conservation plans need field data and stakeholder judgment
Plan natural resources conservation or restoration programs. Manage agricultural or forestry operations.
#7510 · Core · 45% automatable
Manage private livestock operations.
information retrieval — Literature research strong; farm observation human
Research livestock management methods. Manage agricultural or forestry operations.
#7503 · Core · 28% automatable
Plan and direct construction and maintenance of range improvements, such as fencing, corrals, stock-watering reservoirs, and soil-erosion control structures.
interpersonal — Managing farm operations needs situated human judgment
Manage agricultural or forestry operations.
#21169 · Core · 43% automatable
Regulate grazing, such as by issuing permits and checking for compliance with standards, and help ranchers plan and organize grazing systems to manage, improve, protect, and maximize the use of rangelands.
decision support — AI assesses compliance against written standards
Determine operational compliance with regulations or standards. Manage agricultural or forestry operations. Issue permits or other legal documents.
#7505 · Core · 50% automatable
Develop technical standards and specifications used to manage, protect, and improve the natural resources of range lands and related grazing lands.
decision support — Sustainability plans drafted; organizational buy-in human
Develop environmental sustainability plans or projects.
#7508 · Core · 62% automatable
Study forage plants and their growth requirements to determine varieties best suited to particular range.
information retrieval — Crop method research aided; field trials human
Research crop management methods.
#7511 · Core · 45% automatable
Develop new and improved instruments and techniques for activities, such as range reseeding.
decision support — AI proposes agricultural methods; field validation required
Develop agricultural methods.
#7509 · Core · 45% automatable
Develop methods for protecting range from fire and rodent damage and for controlling poisonous plants.
decision support — AI proposes agricultural methods; field validation required
Develop agricultural methods.
#24019 · Supplemental · 8% automatable
Apply herbicide to eliminate harmful plants.
physical manipulation — Applying chemicals to plants is field manual work
Apply chemical solutions to plants to protect against disease or insects or to enhance growth. Care for plants or animals.
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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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