AI Automation Index

Will AI Replace Helpers--Extraction Workers?

Task-level automation exposure for Helpers--Extraction Workers: 14 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

13% weighted task index

14 of 14 tasks scored

Median Salary

$47,730 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

6,700 people hold this job — size, not growth

Projected 2025–35

+1.7% employment change

700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 26 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$36,160 – $77,180

Highest medians

West Virginia $77,180

Kentucky $68,020

Utah $55,490

Wyoming $55,240

New York $54,910

Lowest medians

Mississippi $36,160

Colorado $37,450

Oklahoma $38,510

Texas $40,650

Florida $41,090

State figures come from the 47-5081 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 13%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 77%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 8%

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 56/100 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

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) 0%

0 tasks

Medium exposure (≥ 35%) 8%

1 tasks

Low exposure (below 35%) 92%

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

#13699 · Core · 30% automatable

Observe and monitor equipment operation during the extraction process to detect any problems.

visual inspection — Extraction operations oversight is physical, hazardous, human-led

Monitor extraction operations.

#13700 · Core · 10% automatable

Drive moving equipment to transport materials and parts to excavation sites.

situated mobility — Truck driving needs real-world navigation and control

Drive trucks or truck-mounted equipment.

#13705 · Core · 3% automatable

Unload materials, devices, and machine parts, using hand tools.

physical manipulation — Physical loading in construction or extraction

Load or unload materials used in construction or extraction.

#13709 · Core · 8% automatable

Set up and adjust equipment used to excavate geological materials.

physical manipulation — Mining equipment operated in unstructured environment

Operate mining equipment.

#13702 · Core · 55% automatable

Organize materials to prepare for use.

decision support — Construction material selection needs code and site judgment

Select construction materials.

#13698 · Core · 15% automatable

Repair and maintain automotive and drilling equipment, using hand tools.

physical manipulation — Hands-on drilling equipment maintenance

Maintain drilling equipment.

#13701 · Core · 10% automatable

Clean up work areas and remove debris after extraction activities are complete.

physical manipulation — Work site cleaning is physical labor

Clean work sites.

#13708 · Core · 8% automatable

Clean and prepare sites for excavation or boring.

physical manipulation — Work site cleaning is physical labor

Clean work sites. Prepare excavation or extraction sites for commissioning or decommissioning.

#13706 · Core · 3% automatable

Load materials into well holes or into equipment, using hand tools.

physical manipulation — Hands-on loading of construction equipment

Load materials into construction equipment.

#13703 · Supplemental · 6% automatable

Provide assistance to extraction craft workers, such as earth drillers and derrick operators.

physical manipulation — Physical assisting of skilled trades on site

Assist skilled construction or extraction personnel.

#13710 · Supplemental · 5% automatable

Collect and examine geological matter, using hand tools and testing devices.

physical manipulation — Physical extraction of geological samples

Collect geological samples.

#13707 · Supplemental · 5% automatable

Signal workers to start geological material extraction or boring.

physical manipulation — Directing equipment operators via physical signals

Signal equipment operators to indicate proper equipment positioning.

#13704 · Supplemental · 11% automatable

Dismantle extracting and boring equipment used for excavation, using hand tools.

physical manipulation — Hands-on drilling equipment maintenance

Dismantle equipment or temporary structures. Maintain drilling equipment.

#13711 · Supplemental · 3% automatable

Dig trenches.

physical manipulation — Manual digging with tools in ground

Dig holes or trenches.

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Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

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

See the full method →

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