Medium AI Risk Average

Material moving workers, all other

SOC Code: 53-7199

Material moving workers, all other carries a 35% AI exposure score (Medium automation risk), with a median annual wage of $41,690 and +4.3% projected employment growth from 2024 to 2034 (BLS), affecting approximately 27,700 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
35% Medium

Proportion of tasks susceptible to AI automation (O*NET analysis)

Projected Growth
+4.3%
2024–2034 (BLS)
+1,200 jobs
Median Annual Wage
$41,690
BLS May 2024
How wage figures are sourced →
The verdict

At 35% AI exposure, Material moving workers, all other sits 2 points below the 37.2% average across 832 U.S. occupations - more exposed than 53% of them. Most of its core tasks still require human judgment.

35%
AI exposure
Top 47%
most exposed
+4.3%
Job growth 2024–34
$41,690
Median wage

Where Material moving workers, all other sits among all occupations

AI-exposure score distribution across 832 U.S. occupations (O*NET task analysis). This occupation is marked in rose.

AI exposure distribution - Material moving workers, all other marked 1 occupations at 0–10% 1 0 43 occupations at 10–20% 43 10 184 occupations at 20–30% 184 20 332 occupations at 30–40% 332 30 158 occupations at 40–50% 158 40 64 occupations at 50–60% 64 50 29 occupations at 60–70% 29 60 15 occupations at 70–80% 15 70 5 occupations at 80–90% 5 80 1 occupations at 90–100% 1 90 AI exposure (%) →
Top 47% by AI exposure Higher AI exposure than 53% of all 832 occupations.

Material moving workers, all other has ai exposure of 35%. Distribution: 1 occupations at 0-10%; 43 occupations at 10-20%; 184 occupations at 20-30%; 332 occupations at 30-40%; 158 occupations at 40-50%; 64 occupations at 50-60%; 29 occupations at 60-70%; 15 occupations at 70-80%; 5 occupations at 80-90%; 1 occupations at 90-100%.

Source: O*NET task data (U.S. Department of Labor); AI-exposure scores are PlainWorkforce's analysis. As of BLS 2024–2034.

Material moving workers, all other vs. its occupational neighbors

AI exposure (horizontal) vs projected 2024–2034 growth (vertical) for occupations in the same SOC group. Material moving workers, all other is marked in rose; the top-left corner is the most future-proof.

Future-proofGrowing but exposedStable, lower-AIMost at risk18.9%25.2%31.5%37.8%44.1%-6.1%-2.2%1.7%5.6%9.5%AI exposure (%) →Projected growth (%) →Material moving workLaborersStockersHeavyLight truck driversIndustrial truckFirst-line supervisoPackersDriver/sales workersCleaners of vehiclesBus driversShuttle drivers

Source: BLS Employment Projections 2024–2034 (growth) and O*NET (AI-exposure analysis by PlainWorkforce).

Employment Projections

27,700
Employment 2024
28,900
Projected 2034
+4.3%
Change (%)
+1,200
Change (jobs)

Occupation Insight

Material moving workers, all other (SOC 53-7199) carries an AI exposure score of 35%, placing it in the Medium automation-risk tier. This score is computed from O*NET Database 30.0 task-level analysis, where each task an occupation performs is evaluated against current generative AI, robotic process automation, and machine-learning capabilities. A score below 40% reflects tasks anchored in physical dexterity, unstructured environments, or high-touch human interaction that current AI cannot reliably replicate.

The economic context matters alongside the risk score. BLS counted approximately 27,700 workers in this occupation in 2024, and projects a +4.3% change through 2034 - modest growth that keeps the occupation viable even as tasks evolve. Median annual compensation stands at $41,690, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires No formal educational credential, plus None of related experience.

For career planners, this profile should be read alongside the task, skill, and knowledge breakdowns below and the list of employers whose workforce composition includes Material moving workers, all other. Adjacent occupations shown further down offer lateral moves that preserve industry knowledge while potentially reducing exposure. Pair the AI exposure score with the BLS employment projection and wage percentiles above for a complete career assessment.

Education & Entry Requirements

Typical Education
No formal educational credential
Work Experience
None
On-the-Job Training
Short-term on-the-job training

Frequently Asked Questions

Will AI replace Material moving workers, all other?

Material moving workers, all other has an AI exposure score of 35%, indicating a medium level of automation risk. The majority of tasks in this role require human judgment, creativity, or physical presence that AI cannot easily replicate.

What is the job outlook for Material moving workers, all other?

According to BLS Employment Projections 2024-2034, Material moving workers, all other is projected to grow by 4.3% over the decade. Current employment stands at approximately 27,700 workers.

What skills are needed for Material moving workers, all other?

Material moving workers, all other requires a combination of technical knowledge and interpersonal skills. Typical education requirement: No formal educational credential.

How much do Material moving workers, all other earn?

The median annual wage for Material moving workers, all other is $41,690, according to BLS Occupational Employment and Wage Statistics (May 2024). Actual earnings vary by location, experience, industry, and employer. The BLS publishes detailed wage percentiles by region in its Occupational Employment and Wage Statistics program.

What education is required for Material moving workers, all other?

The typical entry-level education for Material moving workers, all other is No formal educational credential. Employers generally expect None of related work experience. On-the-job training typically involves Short-term on-the-job training. Requirements can vary by employer and specialization.

Which companies employ Material moving workers, all other?

Material moving workers, all other roles exist across many industries and employers. Workforce composition is estimated from BLS industry-occupation employment distributions matched to SEC-registered public companies.

AI Exposure Rating

1.8
out of 5.0

Medium automation risk based on O*NET task analysis. Most tasks require human judgment and are resistant to automation.

Data sources: Bureau of Labor Statistics Employment Projections 2024–2034 and O*NET Database 30.0. Employment figures are rounded. Wage data from BLS Occupational Employment Statistics (OES).

Data sourced from official public datasets. See our methodology for details. Retrieved and formatted by PlainWorkforce Editorial

Every figure on PlainWorkforce is rendered directly from BLS/O*NET source data, no number is typed in by an editor. See our editorial standards & corrections policy, the methodology behind these figures, or report a data error. Data current as of BLS Employment Projections 2024-2034.