AI Automation Index

Will AI Replace Insurance Appraisers, Auto Damage?

Task-level automation exposure for Insurance Appraisers, Auto Damage: 7 O*NET tasks scored against their work activities, plus the simulator.

E-E-A-T Verified

Developed & audited by Gini Loh

Automation Exposure

62% weighted task index

7 of 7 tasks scored

Median Salary

$78,240 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

11,560 people hold this job — size, not growth

Projected 2025–35

-8.7% employment change

700 openings a year · BLS line item

Pay by state

BLS OEWS publishes 39 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$46,520 – $97,950

Highest medians

Kentucky $97,950

Pennsylvania $95,720

New Jersey $94,280

Connecticut $89,740

South Carolina $86,680

Lowest medians

Puerto Rico $46,520

Florida $61,320

Nebraska $62,350

Ohio $63,470

Arkansas $63,960

State figures come from the 13-1032 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 62%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 0%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 60%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 16%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 16%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 0/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

7 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 75%

5 tasks

Medium exposure (≥ 35%) 9%

1 tasks

Low exposure (below 35%) 15%

1 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

New 07/2016 · Incumbent

Contact vendors to locate replacement parts for vehicles.
New 07/2016 · Incumbent

Discuss insurance claims with customers or damage claimants.
Revision 07/2016 · Incumbent

Review repair cost estimates and negotiate with automobile repair shops to secure agreement on cost of repairs.

Replaced: Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.

Constituent Tasks

#7249 · Core · 78% automatable

Evaluate practicality of repair as opposed to payment of market value of vehicle before accident.

data analysis — Cost estimation from given inputs

Estimate costs of goods or services.

#7247 · Core · 65% automatable Revision

Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.

data analysis — Examines records for anomalies; human validates findings

Examine financial records.

#7248 · Core · 19% automatable

Examine damaged vehicle to determine extent of structural, body, mechanical, electrical, or interior damage.

visual inspection — Vehicle inspection is physical on-site work

Inspect motor vehicles. Inspect mechanical components of vehicles to identify problems. Inspect structural components of vehicles to identify problems.

#7251 · Core · 65% automatable

Prepare insurance forms to indicate repair cost estimates and recommendations.

text generation — Draft transaction docs; human owns legal risk

Prepare contracts or other transaction documents.

#7246 · Core · 78% automatable

Estimate parts and labor to repair damage, using standard automotive labor and parts cost manuals and knowledge of automotive repair.

data analysis — Cost estimation from given inputs

Estimate costs of goods or services.

#7250 · Core · 70% automatable

Determine salvage value on total-loss vehicle.

decision support — Valuation aided; human owns final value judgment

Determine the value of goods or services.

#7252 · Core · 55% automatable

Arrange to have damage appraised by another appraiser to resolve disagreement with shop on repair cost.

interpersonal — Cross-staff problem resolution involves politics and trust

Communicate with management or other staff to resolve problems.

Embed this calculator on your site ↓

Bloggers & authors: Embed this interactive calculator directly in your articles or comparison guides. It loads dynamically via a responsive iframe (auto-resizing to prevent double scrollbars) and provides a direct, high-value tool for your audience.

<div class="giniloh-calculator-embed" data-calculator="insurance-appraisers-auto-damage"></div>
<script src="https://giniloh.com/embed.js" async></script>

Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

Best AI Proof Jobs in a Changing Market

Worried robots are coming for your paycheck? You’re not alone—but the real story is far more hopeful. This guide cuts through the scare headlines to reveal exactly where smart, sustainable career opportunities exist right now. We’ve curated a list of true ai proof jobs—roles in healthcare, skilled trades, and cybersecurity that not only survive automation but thrive because of human judgment and empathy. You’ll get specific, money-making data on projected growth (the BLS says wind turbine technicians, for instance, will grow 44% by 2032), real salary ranges from active job listings, and a simple self-assessment quiz to identify your own most resilient path. Plus, we name companies hiring today and the niche job boards they use. Stop worrying and start planning—your secure career is closer than you think.

Read Article ->