Many AI tools work by comparing your inputs (injury type, treatment dates, time missed, and general work impact) to patterns they’ve learned from other cases.
That approach tends to fail when your claim depends on details that don’t fit neatly into a calculator box, such as:
- How your restrictions are documented by your treating provider (not just that you have restrictions)
- Whether symptoms and treatment progress match the injury date and the incident description
- Whether the insurer disputes causation (common when paperwork is incomplete or the timeline is unclear)
- How wages were actually earned in practice—especially for workers who rely on overtime, shift differentials, or variable schedules
In Columbus, MS, where many jobs run on tight production timelines and frequent shift changes, documentation gaps can happen fast. When the file is thin, the insurer’s leverage increases—and that’s exactly where an AI estimate can become dangerously optimistic.


