AI tools typically take the information you enter (injury type, treatment, missed work, age, and sometimes job duties) and match it to patterns they’ve learned from past data. That can create a range that looks reasonable at first glance.
The problem is that workers’ compensation outcomes hinge on details that are hard to capture in a short questionnaire—details that are especially important in Lorain’s workplaces, where claims often involve:
- Industrial/warehouse settings with specific job duties and physical demands
- Construction and trade work where restrictions can affect return-to-duty quickly
- Shift-based schedules that complicate wage-loss calculations
When the record doesn’t clearly describe your functional limits or when wage documentation doesn’t align with your actual schedule, an AI estimate may understate (or sometimes overstate) what the insurer is likely to offer.


