AI tools typically ask for basic inputs: injury date, body part, treatment history, and whether you missed work. They may then generate a range based on patterns from other cases.
Where the problem starts is that Glenview cases frequently involve fact patterns that are hard to reduce to a generic model, such as:
- Commuting and schedule disruption: Even when an injury happens at work, wage loss can depend on how your workweek and commute schedules changed—especially for workers traveling between multiple sites.
- Documentation gaps from suburban work routines: Some injuries are reported later because people assume symptoms will fade, or they rely on employer “usual procedures” instead of getting restrictions documented early.
- Light-duty / return-to-work conflicts: Insurers may argue you could have returned earlier or that restrictions were temporary—issues that can hinge on what your treating provider wrote and when.
An AI estimate won’t verify whether your medical records match the timeline, whether restrictions are consistent, or whether wage loss was actually supported with payroll documentation.


