AI tools generally work by comparing your inputs to patterns from other cases. That can be useful as a rough starting point, but it often breaks down when the insurer’s questions don’t match the “average” scenario.
In Azusa, common case dynamics that can distort AI-style ranges include:
- Commuter-style wage impact: If your work schedule is tied to shift timing or overtime, AI calculators may not fully reflect how a restriction affects your actual earning pattern.
- Industrial job demands: Many local workplaces require repetitive lifting, loading/unloading, or sustained postures. If your limitations aren’t tied to those specific demands in the medical record, an estimate may undervalue the case.
- Documentation friction: Injured workers sometimes get treatment notes that are incomplete, delayed, or not clearly linked to functional limits. AI can’t “see” that gap—insurers can.
The result: an AI range may look plausible, but the insurer’s evaluation can hinge on details the tool can’t access.


