Industrial Truck and Tractor Operators

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.0), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Mixed signals. Today's AI/LLM studies show relatively low exposure for this job, but the older (2013) Frey–Osborne work rated it higher for computerization and robotics. Different eras, different technologies — the AI measures above reflect the current state.

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 11 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

• 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. Core · importance 4.6
• Move controls to drive gasoline- or electric-powered trucks, cars, or tractors and transport materials between loading, processing, and storage areas. Core · importance 4.5
• Manually or mechanically load or unload materials from pallets, skids, platforms, cars, lifting devices, or other transport vehicles. Core · importance 4.5
• Position lifting devices under, over, or around loaded pallets, skids, or boxes and secure material or products for transport to designated areas. Core · importance 4.5
• Inspect product load for accuracy and safely move it around the warehouse or facility to ensure timely and complete delivery. Core · importance 4.3
• Weigh materials or products and record weight or other production data on tags or labels. Supplemental · importance 4.3
• Perform routine maintenance on vehicles or auxiliary equipment, such as cleaning, lubricating, recharging batteries, fueling, or replacing liquefied-gas tank. Supplemental · importance 4.1
• Operate or tend automatic stacking, loading, packaging, or cutting machines. Supplemental · importance 4.1
• Turn valves and open chutes to dump, spray, or release materials from dump cars or storage bins into hoppers. Supplemental · importance 3.6
• Hook tow trucks to trailer hitches and fasten attachments, such as graders, plows, rollers, or winch cables to tractors, using hitchpins. Supplemental
• Signal workers to discharge, dump, or level materials. Supplemental

Work activities

• Inspect cargo areas for cleanliness or condition.
• Move materials, equipment, or supplies.
• Load shipments, belongings, or materials.
• Operate vehicles or material-moving equipment.
• Operate cranes, hoists, or other moving or lifting equipment.
• Secure cargo.
• Position material handling equipment.
• Clean vehicles or vehicle components.
• Maintain vehicles in good working condition.
• Mark materials or objects for identification.
• Weigh materials to ensure compliance with specifications.
• Operate packing or other material processing equipment.

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Abilities

Transferable skills

Knowledge

Essential skills

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

• Depth Perception Common Skill
• Visualization Specialized Skill
• Information Ordering Specialized Skill
• Deductive Reasoning Common Skill
• Mathematics Common Skill
• Time Management Common Skill
• Microsoft Word Common Skill
• Microsoft Outlook Common Skill
• Microsoft Excel Common Skill
• Finger Dexterity Common Skill
• Equipment Maintenance Specialized Skill
• Reading Comprehension Common Skill
• Active Listening Common Skill
• English Language Common Skill
• Speech Recognition Specialized Skill
• Inductive Reasoning Common Skill
• Critical Thinking Common Skill
• Social Perceptiveness Common Skill
• Writing Common Skill
• Complex Problem Solving Common Skill
• Learning Strategies Specialized Skill
• Active Learning Common Skill
• Systems Analysis Specialized Skill
• Negotiation Common Skill
• Instructing Specialized Skill
• Equipment Selection Specialized Skill
• Telecommunications Specialized Skill
• Biology Specialized Skill
• Chemistry Specialized Skill
• Geography Specialized Skill
• Psychology Specialized Skill
• Physics Specialized Skill
• Foreign Language Common Skill
• Installation Specialized Skill

Tools & technology

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

How to get in

Job zone

Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed

Education

Usually requires a high school diploma or GED, though some occupations may not.

Typical entry-level education

No formal educational credential · BLS, the typical path — not a requirement

Related experience

Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.

Preparation level

SVP (Below 6.0) — total schooling plus on-the-job experience.

What to study: Transportation and Materials Moving . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.

Education of current workers

Share of people in this occupation at each level of education.

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Interest areas

Work styles

Wages & employment

U.S. · annual wages (BLS OEWS)

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Related occupations

Part of the Supply Chain & Transportation career cluster.

• Hoist and Winch Operators · 53-7041.00 Compare →
• Crane and Tower Operators · 53-7021.00 Compare →
• Tank Car, Truck, and Ship Loaders · 53-7121.00 Compare →
• Loading and Moving Machine Operators, Underground Mining · 47-5044.00 Compare →
• Operating Engineers and Other Construction Equipment Operators · 47-2073.00 Compare →
• Laborers and Freight, Stock, and Material Movers, Hand · 53-7062.00 Compare →
• Pile Driver Operators · 47-2072.00 Compare →
• Excavating and Loading Machine and Dragline Operators, Surface Mining · 47-5022.00 Compare →
• Riggers · 49-9096.00 Compare →
• Mobile Heavy Equipment Mechanics, Except Engines · 49-3042.00 Compare →
• Heavy and Tractor-Trailer Truck Drivers · 53-3032.00 Compare →
• Conveyor Operators and Tenders · 53-7011.00 Compare →

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Industrial Truck and Tractor Operators — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
• BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
• Microsoft “Working with AI” working-with-ai Microsoft Research
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
• AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
• ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
• IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
• Frey & Osborne (2013) frey-osborne-automation academic
• Dingel & Neiman (2020) dingel-neiman-workathome academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-role-53-7051-00,
  title  = {Industrial Truck and Tractor Operators},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-53-7051-00}
}