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AI Automation Index
Will AI Replace Continuous Mining Machine Operators?
Task-level automation exposure for Continuous Mining Machine Operators: 15 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
17%
weighted task index
15 of 15 tasks scored
Median Salary
$61,810 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
14,000 people hold this job — size, not growth
Projected 2025–35
+3.2%
employment change
1,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 27 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$46,450 – $90,820
Highest medians
Minnesota $90,820
Nevada $85,590
Wyoming $85,260
Colorado $81,110
West Virginia $77,570
Lowest medians
New York $46,450
Oklahoma $46,980
Missouri $47,280
Arkansas $47,890
New Mexico $48,900
State figures come from the 47-5041 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 17%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 64%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 16%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 0%
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 62/100
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
15 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) 0%
0 tasks
Medium exposure (≥ 35%) 12%
2 tasks
Low exposure (below 35%) 88%
13 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
#20912 · Core · 10% automatable
Hang ventilation tubing and ventilation curtains to ensure that the mining face area is kept properly ventilated.
physical manipulation — Hands-on positioning of safety equipment in field
Position safety or support equipment.
#20913 · Core · 25% automatable
Conduct methane gas checks to ensure breathing quality of air.
physical manipulation — Air sampling requires on-site instrument operation
Test air quality at work sites.
#20914 · Core · 20% automatable
Check the stability of roof and rib support systems before mining face areas.
visual inspection — Installation inspection requires on-site physical check
Inspect completed work to ensure proper installation.
#14956 · Core · 8% automatable
Operate mining machines to gather coal and convey it to floors or shuttle cars.
physical manipulation — Mining equipment operated in unstructured environment
Operate mining equipment.
#14959 · Core · 8% automatable
Drive machines into position at working faces.
physical manipulation — Mining equipment operated in unstructured environment
Operate mining equipment.
#14960 · Core · 8% automatable
Move controls to start and regulate movement of conveyors and to start and position drill cutters or torches.
physical manipulation — Mining equipment operated in unstructured environment
Operate mining equipment.
#14958 · Core · 8% automatable
Reposition machines to make additional holes or cuts.
physical manipulation — Hands-on positioning of heavy construction equipment
Position construction or extraction equipment.
#14957 · Core · 45% automatable
Determine locations, boundaries, and depths of holes or channels to be cut.
decision support — Location decisions need site and regulatory judgment
Determine appropriate locations for operations or installations.
#14961 · Core · 30% automatable
Observe and listen to equipment operation to detect binding or stoppage of tools or other equipment malfunctions.
visual inspection — Extraction operations oversight is physical, hazardous, human-led
Monitor extraction operations.
#14962 · Core · 8% automatable
Repair, oil, and adjust machines, and change cutting teeth, using wrenches.
physical manipulation — Hands-on heavy equipment maintenance
Maintain extraction or excavation equipment.
#14964 · Supplemental · 4% automatable
Install casings to prevent cave-ins.
physical manipulation — Installing safety equipment requires hands-on work
Install safety or support equipment.
#19876 · Supplemental · 10% automatable
Scrape or wash conveyors, using belt scrapers or belt washers, to minimize dust production.
physical manipulation — Equipment and facility cleaning is hands-on
Clean equipment or facilities.
#14963 · Supplemental · 10% automatable
Move levers to raise and lower hydraulic safety bars supporting roofs above machines until other workers complete framing.
physical manipulation — Crane/hoist operation demands real-time physical control
Operate cranes, hoists, or other moving or lifting equipment.
#19875 · Supplemental · 40% automatable
Apply new technologies developed to minimize the environmental impact of coal mining.
decision support — AI advises adoption; humans deploy and integrate
Apply new technologies to improve work processes.
#14965 · Supplemental · 13% automatable
Guide and assist crews laying track and resetting supports and blocking.
interpersonal — Directing crews in the field is human leadership work
Assist skilled construction or extraction personnel. Direct construction or extraction personnel.
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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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