60
/100

SOC 49-3021

Automotive Body and Related Repairers

ElevatedFrey/Osborne: 91.0%

Risk Score

โš ๏ธ

60/100

Elevated

US Employment

๐Ÿ‘ฅ

155,220

Total workers

Median Wage

๐Ÿ’ฐ

$52K

$36K โ€“ $87K

Projected Growth

๐Ÿ“ˆ

+1.6%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

36/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Automotive Body and Related Repairers face a risk score of 60/100 โ€” 16 points above the national average of 44. With only 36/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $52K ($5K above the national median). The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 60/100, Automotive Body and Related Repairers faces moderate automation pressure. While tasks like augmented reality-guided remote diagnostics are increasingly handled by AI, the role retains significant human elements. The 155,220 workers in this occupation should focus on strengthening skills in diagnosing novel equipment failures through physical inspection and working in confined, elevated, or hazardous spaces to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Automotive Body and Related Repairers?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Augmented reality-guided remote diagnostics

2

Predictive maintenance AI reducing reactive repair needs

3

Automated fault detection via IoT sensor networks

4

Robotic inspection of hard-to-reach equipment

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Diagnosing novel equipment failures through physical inspection

โœ“

Working in confined, elevated, or hazardous spaces

โœ“

Customer communication about technical issues

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

File, grind, sand, and smooth filled or repaired surfaces, using power tools and hand tools.

โ€ข

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.

โ€ข

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.

โ€ข

Prime and paint repaired surfaces, using paint sprayguns and motorized sanders.

โ€ข

Follow supervisors' instructions as to which parts to restore or replace and how much time the job should take.

๐Ÿ’ป Technology Skills

โ€ข

Accounting software

โ€ข

Calendar and scheduling software

โ€ข

Point of sale POS software

โ€ข

Data base user interface and query software

โ€ข

Analytical or scientific software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mechanical

โ€ข

Customer and Personal Service

โ€ข

Production and Processing

โ€ข

Mathematics

โ€ข

English Language

๐Ÿ“Š vs National Average

Median Wage$52K
+$5K

National avg: $46K

Risk Score60/100
+16

National avg: 44/100

GenAI Exposure36/100
-2

National avg: 38/100

Projected Growth1.6%
-2.1%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K65%
Electrical Power-Line Installers and Repairers28$93K79%
Supervisors of Installation, Maintenance, and Repair Workers33$78K84%