AI tools are built to recognize patterns. They can be useful when you’re trying to understand what information generally influences value. They are less reliable when your situation depends on evidence that an AI system can’t see.
In Dayton, many injuries arise in workplaces where documentation is fast-moving—think shift changes, tight schedules, and supervisors who want you back on the job “as soon as possible.” Those environments can create common problems for claim files:
- Gaps in the medical timeline (missed or delayed follow-ups)
- Work restriction language that’s vague (or doesn’t match what you actually can do)
- Wage documentation that doesn’t reflect real earning patterns (overtime, inconsistent hours, or changing duties)
- Disputes about whether the work incident caused the condition
When that happens, an AI settlement range may look reasonable on paper but doesn’t reflect the insurer’s real decision points in Minnesota.


