Many AI tools are built to work like a generic “inputs in, range out” model. That can be useful for getting a rough sense of possibilities, but it often breaks down for cases that don’t match the tool’s assumptions.
In Sun Prairie, we frequently see workplace injuries tied to commute-heavy schedules, shifting job duties, and suburban work patterns—meaning the gap between “what the doctor says” and “what you can actually do” matters.
An AI estimate may not fully reflect:
- how long it took you to get consistent treatment after the injury
- whether your doctor documented work restrictions in a way insurers accept
- whether your work duties changed (or you were pushed to return before you were ready)
- how wage loss is documented when overtime or schedule patterns vary
If the estimate seems “too low,” that usually isn’t random—it’s often the result of missing or mismatched evidence.


