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AI Automation Index
Will AI Replace Logging Equipment Operators?
Task-level automation exposure for Logging Equipment Operators: 9 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
25%
weighted task index
9 of 9 tasks scored
Median Salary
$49,740 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
21,060 people hold this job — size, not growth
Projected 2025–35
-3.8%
employment change
3,700 openings a year · BLS line item
Pay by state
BLS OEWS publishes 36 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$30,230 – $75,140
Highest medians
Washington $75,140
Illinois $71,100
Texas $62,890
California $61,940
South Dakota $58,740
Lowest medians
Kentucky $30,230
West Virginia $36,130
Oklahoma $37,270
Missouri $41,770
Maine $44,780
State figures come from the 45-4022 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 25%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 69%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 14%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 10%
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 68/100
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
9 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) 24%
2 tasks
Medium exposure (≥ 35%) 0%
0 tasks
Low exposure (below 35%) 76%
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 remote-controlled logging machines and drones for dangerous or hard-to-reach tasks.
Constituent Tasks
#9802 · Core · 20% automatable
Inspect equipment for safety prior to use, and perform necessary basic maintenance tasks.
visual inspection — Condition assessment needs physical presence
Inspect equipment or facilities to determine condition or maintenance needs. Maintain forestry, hunting, or agricultural equipment.
#9807 · Core · 8% automatable
Control hydraulic tractors equipped with tree clamps and booms to lift, swing, and bunch sheared trees.
physical manipulation — Forestry equipment operation is heavy hands-on work
Operate forestry equipment.
#9806 · Core · 60% automatable
Grade logs according to characteristics such as knot size and straightness, and according to established industry or company standards.
data analysis — Log analysis aided; domain interpretation human
Evaluate log quality.
#9803 · Core · 8% automatable
Drive straight or articulated tractors equipped with accessories such as bulldozer blades, grapples, logging arches, cable winches, and crane booms to skid, load, unload, or stack logs, pull stumps, or clear brush.
physical manipulation — Forestry equipment operation is heavy hands-on work
Operate forestry equipment.
#9804 · Core · 8% automatable
Drive crawler or wheeled tractors to drag or transport logs from felling sites to log landing areas for processing and loading.
physical manipulation — Forestry equipment operation is heavy hands-on work
Operate forestry equipment.
#9809 · Core · 78% automatable
Fill out required job or shift report forms.
data analysis — Maintain structured personnel records in systems
Maintain personnel records.
#9805 · Core · 8% automatable
Drive tractors for building or repairing logging and skid roads.
physical manipulation — Forestry equipment operation is heavy hands-on work
Operate forestry equipment.
#9808 · Supplemental · 7% automatable
Drive and maneuver tractors and tree harvesters to shear the tops off of trees, cut and limb the trees, and cut the logs into desired lengths.
physical manipulation — Forestry equipment operation is heavy hands-on work
Cut trees or logs. Operate forestry equipment.
#9810 · Supplemental · 24% automatable
Calculate total board feet, cordage, or other wood measurement units, using conversion tables.
physical manipulation — Hands-on measurement of agricultural products
Measure physical characteristics of forestry or agricultural products.
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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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