Medium AI Risk Declining

Insurance appraisers, auto damage

SOC Code: 13-1032

Insurance appraisers, auto damage carries a 27% AI exposure score (Medium automation risk), with a median annual wage of $76,650 and -8.2% projected employment growth from 2024 to 2034 (BLS), affecting approximately 9,200 workers. Full task breakdown, skills, and employer data are below.

AI Exposure Score
27% Medium

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

Projected Growth
-8.2%
2024–2034 (BLS)
-800 jobs
Median Annual Wage
$76,650
BLS May 2024
How wage figures are sourced →

AI Exposure vs Industry Growth

Workforce demand by occupation Sanctioned bespoke signature viz (@signature-viz, KIZ-799) showing occupation-level workforce demand from BLS OEWS data. Pure SVG, no external dependencies.Projected Growth 2024-2034 (BLS)Technology+12.8%Healthcare+10.2%Professional+7.8%Education+5.8%Construction+4.5%Finance+4.6%Logistics+3.2%Government+1.2%Manufacturing-2.1%Retail-3.4%
National AI Exposure
40%
Average across all occupations
Avg Wage Growth
+3.2%
Median annual wage change
High-Risk Roles
127
Occupations with >70% AI exposure

Total occupations tracked

832

Covering all SOC major groups

Data currency

2024

BLS Employment Projections

AI exposure avg

40%

Fleet-wide median across all roles

Methodology confidence 92.0%
Industry standard

Composite score weighing O*NET task data completeness, BLS projection methodology, and cross-validation with employer risk grades.

Employment Projections

9,200
Employment 2024
8,400
Projected 2034
-8.2%
Change (%)
-800
Change (jobs)

Occupation Insight

Insurance appraisers, auto damage (SOC 13-1032) carries an AI exposure score of 27%, 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 9,200 workers in this occupation in 2024, and projects a -8.2% change through 2034 — a decline that often compounds with high AI exposure to create displacement headwinds. Median annual compensation stands at $76,650, reflecting both skill scarcity and the value employers place on the tasks that remain difficult to automate. Entry typically requires Postsecondary nondegree award, 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 Insurance appraisers, auto damage. 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
Postsecondary nondegree award
Work Experience
None
On-the-Job Training
Moderate-term on-the-job training

Top Tasks (O*NET)

  1. 1. Evaluate practicality of repair as opposed to payment of market value of vehicle before accident.
  2. 2. Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.
  3. 3. Examine damaged vehicle to determine extent of structural, body, mechanical, electrical, or interior damage.
  4. 4. Prepare insurance forms to indicate repair cost estimates and recommendations.
  5. 5. Estimate parts and labor to repair damage, using standard automotive labor and parts cost manuals and knowledge of automotive repair.
  6. 6. Determine salvage value on total-loss vehicle.
  7. 7. Arrange to have damage appraised by another appraiser to resolve disagreement with shop on repair cost.

Key Skills Required

  • Writing
  • Speaking
  • Reading Comprehension
  • Active Listening
  • Critical Thinking
  • Time Management
  • Judgment and Decision Making
  • Social Perceptiveness
  • Service Orientation
  • Active Learning

Knowledge Areas

  • Customer and Personal Service
  • English Language
  • Computers and Electronics
  • Mechanical
  • Administrative
  • Education and Training
  • Law and Government
  • Mathematics
  • Transportation
  • Administration and Management

Frequently Asked Questions

Will AI replace Insurance appraisers, auto damage?

Insurance appraisers, auto damage has an AI exposure score of 27%, 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 Insurance appraisers, auto damage?

According to BLS Employment Projections 2024-2034, Insurance appraisers, auto damage is projected to decline by 8.2% over the decade. Current employment stands at approximately 9,200 workers.

What skills are needed for Insurance appraisers, auto damage?

Key skills for Insurance appraisers, auto damage include Writing, Speaking, Reading Comprehension, and others. Typical entry-level education is Postsecondary nondegree award.

How much do Insurance appraisers, auto damage earn?

The median annual wage for Insurance appraisers, auto damage is $76,650, 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 Insurance appraisers, auto damage?

The typical entry-level education for Insurance appraisers, auto damage is Postsecondary nondegree award. Employers generally expect None of related work experience. On-the-job training typically involves Moderate-term on-the-job training. Requirements can vary by employer and specialization.

Which companies employ Insurance appraisers, auto damage?

Insurance appraisers, auto damage 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

1.4
out of 5.0

Medium automation risk based on 7 analyzed tasks. 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).

Related

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