What this guide is for
Auto-damage appraisal work involves vehicle-damage, repair-cost, and insurance-form context. O*NET describes tasks such as damage examination, estimate preparation, repair-shop discussion, and escalation when appraisal disagreement exists. BLS describes appraisers as estimating repair costs, with estimates then used in claims settlement work. Those are authority-heavy activities—not tasks to hand to a general-purpose model.
Make one review packet, not a synthetic appraisal
needs_review.Bound the scenario and stop conditions.
Name the approved purpose, jurisdiction/procedure owner, human reviewers, and prohibited inputs. Stop for private photos, policy or claimant data, vehicle identification numbers, location data, credentials, or an instruction to make a decision.
Minimize and inventory the approved material.
AI can label what was supplied, its source owner/version, and whether it appears missing, duplicate, unreadable, or inconsistent. It must not read an image as a damage finding or estimate a repair.
Separate source support from unknowns.
Attach each question to a current approved source. If source, version, condition, authority, or permitted use is missing, label it
unknown. Never fill the gap with model memory or inference.Draft questions—not conclusions.
Ask the authorized appraiser, claims reviewer, privacy/security reviewer, and required procedural owner what they must confirm. Do not draft repair steps, estimate lines, prices, total-loss outcomes, claim decisions, or messages.
Hand off, then stop.
Create the review packet and name the reviewer who can approve, revise, reject, or escalate it. The packet does not authorize an external action.
Give AI a clerk job, never an appraiser job
Organizes supplied approved labels and minimum-necessary metadata.
Flags missing source owner, version, approval, or permitted use.
Surfaces duplicates, unreadable items, and inconsistencies for people to resolve.
Drafts neutral questions tied to named reviewers, never a result.
Safe preparation vs. approval-required work
| AI may prepare | Human authority must decide or do |
|---|---|
| Inventory approved labels and metadata | Inspect a vehicle or determine damage, safety, or repairability |
| Flag missing, duplicate, conflict, or unreadable material | Choose repair method, parts, labor, price, estimate, supplement, or repair vendor |
| Produce an unknown list and neutral review questions | Determine market value, total loss, salvage, coverage, liability, causation, or fraud |
| Assemble a local review-only receipt | Negotiate, settle, pay, message a claimant/shop/vendor, upload, or update a claim system |
Insurance AI governance and privacy obligations vary by state, organization, and facts. NAIC material is guidance rather than a universal self-executing rule. Obtain applicable legal, privacy/security, licensing, carrier, and procedure review.
Five ways a useful prep tool becomes unsafe
- Calling photo interpretation an inspection. A model output is not a vehicle examination or safety finding.
- Turning a plausible number into an estimate. Parts, labor, market values, repair methods, and thresholds must come from authorized sources and people.
- Combining stale sources into a false certainty. Preserve each source owner/version and label missing or conflicting support.
- Using more data “just in case.” Minimize inputs; do not upload identifiers, policy/claim data, private photos, locations, or credentials without documented authorization.
- Letting a draft leak into a claim action. The packet ends at
needs_review; it cannot message, negotiate, pay, or write a system record.
Auto Damage Evidence & Estimate Review Desk
Enter only fictional or organization-approved, minimum-necessary text. This form keeps data in your browser and produces a review-only receipt; it does not send or save anything.
What this manual is built on
- O*NET: Insurance Appraisers, Auto DamageOccupation and task context; updated 2026.
- BLS: Claims Adjusters, Appraisers, Examiners, and InvestigatorsContext on auto damage appraisers and claims-settlement role separation.
- NAIC: AI Model Bulletin announcementInsurance AI governance context; check state adoption and carrier policy.
- NIST AI 600-1: Generative AI ProfileRisk-management framing, not an appraisal or insurance rulebook.
- FTC: Gramm-Leach-Bliley Act guidancePrivacy/safeguarding context for covered organizations.
The cited sources and the guide boundaries should be rechecked before real-world use.
Connect this desk without blurring authority
Start at the Insurance AI Guide Shop. For earlier intake/service preparation, use the Insurance Claims & Policy Clerks guide. For the wider occupation shelf, use the Occupation AI Workflow Guide Directory. For least-privilege workflow design, use the AI Agent Workflow Builder.