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Automotive Body and Related Repairers

Occupation · SOC 49-3021.00

Repair and refinish automotive vehicle bodies and straighten vehicle frames.

Also called: Auto Body Man · Automotive Body Technician (Auto Body Tech) · Body Man · Body Technician (Body Tech) · Auto Body Repair Technician (Auto Body Repair Tech) · Auto Body Repairman · Collision Repair Technician (Collision Repair Tech) · Collision Technician (Collision Tech) · Frame Man · Refinish Technician (Refinish Tech) · Auto Body Customizer · Auto Body Detailer

Job family: Installation, Maintenance, and Repair Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-49-3021-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

14th-percentile task overlap — yet about 14,600 openings a year (+1.6% projected, BLS) . What exposure means →

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.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 12th -1.2
LLM task exposure, γ (OpenAI / Eloundou) Low 12th 0.1
AI assistant applicability (Microsoft) Low 26th 0.1

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.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.9 · 81st percentile among occupations · High

Job outlook

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

Outlook About average · +1.6% by 2034
Projected annual openings 14,600
Employment 2024 → 2034 172,600 → 175,400

“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.

18% mean task exposure (2025)
26th percentile of 427 placed occupations
+3 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Motor Vehicle Mechanics and Repairers · 7231 18% Not exposed

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

Work activities

Knowledge, skills & abilities

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

Knowledge

Mechanical 3.6
Customer and Personal Service 3.3
Production and Processing 3.1
Mathematics 3.0
English Language 3.0
Transportation 3.0

Abilities

Arm-Hand Steadiness 3.5
Visualization 3.4
Manual Dexterity 3.4
Oral Comprehension 3.3
Problem Sensitivity 3.3
Information Ordering 3.3
Finger Dexterity 3.3
Near Vision 3.3
Visual Color Discrimination 3.3
Oral Expression 3.1
Category Flexibility 3.1
Selective Attention 3.1
Control Precision 3.1
Multilimb Coordination 3.1
Speech Recognition 3.1
Written Comprehension 3.0
Flexibility of Closure 3.0
Static Strength 3.0
Trunk Strength 3.0
Extent Flexibility 3.0
Auditory Attention 3.0
Speech Clarity 3.0

Transferable skills

Repairing 3.3
Troubleshooting 3.1
Operations Monitoring 3.0
Quality Control Analysis 3.0
Time Management 3.0
Social Perceptiveness 2.9

Essential skills

Active Listening 3.0
Speaking 3.0
Critical Thinking 3.0
Monitoring 3.0
Reading Comprehension 2.9
Active Learning 2.9

Skills in demand

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

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Outlook Electronic mail software Hot technology
Microsoft Windows Operating system software Hot technology
Microsoft Word Word processing software Hot technology
Accounts receivable software Accounting software
Appointment scheduling software Calendar and scheduling software
Automotive and Accounting Software by R*KOM Invoice Writer Point of sale POS software
AutoZone ALLDATA Data base user interface and query software
Collision damage estimation software Analytical or scientific software
Collision damage measurement software Analytical or scientific software
Equipment management information software Data base user interface and query software
Materials management software Inventory management software
Microsoft OneNote Word processing software
Paint mixing and matching software Analytical or scientific software
Swan River Estimiser Pro Analytical or scientific software

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.

Exposed to Contaminants 5.0
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 5.0
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.9
Time Pressure 4.5
Exposed to Hazardous Conditions 4.5
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 4.5
Face-to-Face Discussions with Individuals and Within Teams 4.4
Importance of Being Exact or Accurate 4.4
Spend Time Bending or Twisting Your Body 4.2
Spend Time Standing 4.2
Freedom to Make Decisions 4.1
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 4.1
Indoors, Not Environmentally Controlled 4.1
Spend Time Making Repetitive Motions 4.0
Frequency of Decision Making 4.0
Determine Tasks, Priorities and Goals 4.0
Spend Time Kneeling, Crouching, Stooping, or Crawling 3.9
Impact of Decisions on Co-workers or Company Results 3.9
In an Enclosed Vehicle or Operate Enclosed Equipment 3.8
Level of Competition 3.8
Health and Safety of Other Workers 3.6
Contact With Others 3.6
Exposed to Cramped Work Space, Awkward Positions 3.5
Spend Time Walking or Running 3.5
Exposed to Hazardous Equipment 3.4
Consequence of Error 3.4
Exposed to Minor Burns, Cuts, Bites, or Stings 3.3
Work With or Contribute to a Work Group or Team 3.1
Work Outcomes and Results of Other Workers 3.1
Importance of Repeating Same Tasks 2.9
Physical Proximity 2.8
Coordinate or Lead Others in Accomplishing Work Activities 2.7
Conflict Situations 2.7
Exposed to Very Hot or Cold Temperatures 2.7
Telephone Conversations 2.6
Indoors, Environmentally Controlled 2.5
Deal With External Customers or the Public in General 2.3
Written Letters and Memos 2.3
Exposed to Extremely Bright or Inadequate Lighting Conditions 2.3
Pace Determined by Speed of Equipment 2.3

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
High school diploma or equivalent · 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: Mechanic and Repair Technologies/Technicians . 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.

High School Diploma 42.0%
Post-Secondary Certificate 34.6%
Less than a High School Diploma 23.4%

Interests & work styles

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

Career interests (Holland / RIASEC)

Realistic 7.0
Conventional 3.3
Investigative 1.6
Enterprising 1.4
Artistic 1.4

Interest areas

Physical/Manual Labor 6.0
Mechanics/Electronics 5.9
Engineering 2.7
Construction/Woodwork 2.0
Transportation/Machine Operation 1.7
Mathematics/Statistics 1.5
Applied Arts and Design 1.4

Work styles

Attention to Detail 2.4
Dependability 2.2
Cautiousness 1.7
Perseverance 1.4

Wages & employment

U.S. · annual wages (BLS OEWS)

$36k10th$45k25th$52kMedian$65k75th$87k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
173k2024175k2034 (proj.)+1.6% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $36,390
25th percentile $45,000
Median (50th) $51,680
75th percentile $64,780
90th percentile $87,040
People employed 155,220

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.

Industry Workers National median pay
Other Services (except Public Administration) · Sector 111,450 $52,310
Retail Trade · Sector 28,680 $49,590
Manufacturing · Sector 6,940 $54,390
Wholesale Trade · Sector 3,120 $52,880
Transportation and Warehousing · Sector 1,220 $59,650
Real Estate and Rental and Leasing · Sector 1,080 $48,430
Administrative and Support and Waste Management and Remediation Services · Sector 540 $49,510
Arts, Entertainment, and Recreation · Sector 530 $71,290
Management of Companies and Enterprises · Sector 300 $61,790
Educational Services · Sector 110 $43,360
Temporary Help Services · National industry 100 $33,890
Construction · Sector 80 $44,600

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).

Industry Concentration Workers
Other Services (except Public Administration) · Sector 25.01× 111,450
Retail Trade · Sector 1.83× 28,680
Manufacturing · Sector 0.54× 6,940
Wholesale Trade · Sector 0.51× 3,120
Real Estate and Rental and Leasing · Sector 0.45× 1,080
Arts, Entertainment, and Recreation · Sector 0.2× 530
Transportation and Warehousing · Sector 0.16× 1,220
Management of Companies and Enterprises · Sector 0.11× 300

Part of the Supply Chain & Transportation career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Automotive Body and Related Repairers sits at the 14th percentile of AI task-overlap and the 36th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Automotive Body and Related Repairers Tire Builders Automotive Glass Installers and Repairers Rail Car Repairers Aircraft Structure, Surfaces, Rigging, and Systems Assemblers Grinding and Polishing Workers, Hand AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

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 Automotive Body and Related Repairers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 26th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Automotive Body and Related Repairers show 14th-percentile AI task overlap — and about 14,600 annual U.S. openings

  • Automotive Body and Related Repairers rank in the 14th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 14,600 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+1.6%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $51,680, across about 155,220 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Automotive Body and Related Repairers show 14th-percentile AI task overlap — and about 14,600 annual U.S. openings

• Automotive Body and Related Repairers rank in the 14th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 14,600 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+1.6%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $51,680, across about 155,220 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Automotive Body and Related Repairers". https://singulariki.com/roles/role-49-3021-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

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.

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

Cite this page
Plain

Singulariki. "Automotive Body and Related Repairers." Singulariki: a source-backed encyclopedia of work. Built from 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. https://singulariki.com/roles/role-49-3021-00

APA

Singulariki. (2026). Automotive Body and Related Repairers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-49-3021-00

BibTeX
@misc{singulariki-role-49-3021-00,
  title  = {Automotive Body and Related Repairers},
  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-49-3021-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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