AI Automation Index

Will AI Replace Industrial Truck and Tractor Operators?

Task-level automation exposure for Industrial Truck and Tractor Operators: 9 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

9% weighted task index

9 of 9 tasks scored

Median Salary

$46,420 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Very High

774,420 people hold this job — size, not growth

Projected 2025–35

+1.5% employment change

64,700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$34,870 – $83,720

Highest medians

District of Columbia $83,720

New Mexico $62,150

Delaware $60,350

Wyoming $59,820

New Hampshire $54,460

Lowest medians

Guam $34,870

Virgin Islands $35,230

Puerto Rico $36,090

West Virginia $41,670

Tennessee $42,100

State figures come from the 53-7051 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 9%

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

Physical work 92%

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

Judgment & influence 0%

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 63/100 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) 0%

0 tasks

Medium exposure (≥ 35%) 0%

0 tasks

Low exposure (below 35%) 100%

9 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

#3202 · Core · 10% automatable

Move levers or controls that operate lifting devices, such as forklifts, lift beams with swivel-hooks, hoists, or elevating platforms, to load, unload, transport, or stack material.

physical manipulation — Crane/hoist operation demands real-time physical control

Operate cranes, hoists, or other moving or lifting equipment.

#3201 · Core · 5% automatable

Move controls to drive gasoline- or electric-powered trucks, cars, or tractors and transport materials between loading, processing, and storage areas.

physical manipulation — Hands-on vehicle and equipment operation

Operate vehicles or material-moving equipment.

#15266 · Core · 4% automatable

Manually or mechanically load or unload materials from pallets, skids, platforms, cars, lifting devices, or other transport vehicles.

physical manipulation — Physical lifting and loading of shipments

Load shipments, belongings, or materials.

#3203 · Core · 6% automatable

Position lifting devices under, over, or around loaded pallets, skids, or boxes and secure material or products for transport to designated areas.

physical manipulation — Positioning material handling equipment is physical

Secure cargo. Position material handling equipment.

#15265 · Core · 14% automatable

Inspect product load for accuracy and safely move it around the warehouse or facility to ensure timely and complete delivery.

visual inspection — Cargo area cleanliness check is physical

Inspect cargo areas for cleanliness or condition. Move materials, equipment, or supplies.

#3206 · Supplemental · 19% automatable

Weigh materials or products and record weight or other production data on tags or labels.

physical manipulation — Hands-on weighing of materials

Mark materials or objects for identification. Weigh materials to ensure compliance with specifications.

#3205 · Supplemental · 7% automatable

Perform routine maintenance on vehicles or auxiliary equipment, such as cleaning, lubricating, recharging batteries, fueling, or replacing liquefied-gas tank.

physical manipulation — Physical vehicle maintenance work

Clean vehicles or vehicle components. Maintain vehicles in good working condition.

#3207 · Supplemental · 18% automatable

Operate or tend automatic stacking, loading, packaging, or cutting machines.

physical manipulation — Physical operation of packing/material processing machinery

Operate packing or other material processing equipment.

#3210 · Supplemental · 5% automatable

Turn valves and open chutes to dump, spray, or release materials from dump cars or storage bins into hoppers.

physical manipulation — Hands-on vehicle and equipment operation

Operate vehicles or material-moving equipment.

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Community Sentiment

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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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