# Automotive Body and Related Repairers

> Repair and refinish automotive vehicle bodies and straighten vehicle frames.

- **SOC code:** 49-3021.00
- **Canonical URL:** https://singulariki.com/roles/role-49-3021-00
- **Also known as:** 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:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- File, grind, sand, and smooth filled or repaired surfaces, using power tools and hand tools.
- Inspect repaired vehicles for proper functioning, completion of work, dimensional accuracy, and overall appearance of paint job, and test-drive vehicles to ensure proper alignment and handling.
- Fit and weld replacement parts into place, using wrenches and welding equipment, and grind down welds to smooth them, using power grinders and other tools.
- Prime and paint repaired surfaces, using paint sprayguns and motorized sanders.
- Follow supervisors' instructions as to which parts to restore or replace and how much time the job should take.
- Sand body areas to be painted and cover bumpers, windows, and trim with masking tape or paper to protect them from the paint.
- Chain or clamp frames and sections to alignment machines that use hydraulic pressure to align damaged components.
- Cut and tape plastic separating film to outside repair areas to avoid damaging surrounding surfaces during repair procedure and remove tape and wash surfaces after repairs are complete.
- Position dolly blocks against surfaces of dented areas and beat opposite surfaces to remove dents, using hammers.
- Fill small dents that cannot be worked out with plastic or solder.
- Review damage reports, prepare or review repair cost estimates, and plan work to be performed.
- Remove damaged sections of vehicles using metal-cutting guns, air grinders and wrenches, and install replacement parts using wrenches or welding equipment.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mechanical _(knowledge)_
- Arm-Hand Steadiness _(ability)_
- Visualization _(ability)_
- Manual Dexterity _(ability)_
- Repairing _(transferable_skill)_
- Customer and Personal Service _(knowledge)_
- Oral Comprehension _(ability)_
- Problem Sensitivity _(ability)_
- Information Ordering _(ability)_
- Finger Dexterity _(ability)_
- Near Vision _(ability)_
- Visual Color Discrimination _(ability)_

**Skills in demand:**
- Visualization _(Specialized Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Time Management _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Mathematics _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Active Listening _(Common Skill)_

**Tools & technology:**
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Accounts receivable software
- Appointment scheduling software
- Automotive and Accounting Software by R*KOM Invoice Writer
- AutoZone ALLDATA
- Collision damage estimation software
- Collision damage measurement software
- Equipment management information software

## AI exposure & outlook

- **AI task-overlap index:** 14th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 12th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 12th percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 26th percentile (Low) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 81st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 1.6% growth (About average); 14.6k annual openings; 172.6k → 175.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $51,680; 155,220 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

---
_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-49-3021-00_