Many AI tools rely on generalized patterns: the diagnosis, the date of injury, and a few basic wage-loss inputs. The problem is that Yorkville claim files often include details that don’t fit neatly into a single “average case,” such as:
- Proof gaps tied to shift schedules (missed appointments, late documentation, or follow-ups that don’t align cleanly with treatment timelines).
- Wage impact that isn’t fully captured (overtime, weekend differentials, seasonal hours, or changes in duties).
- Return-to-work friction when restrictions limit what you can safely do, but the employer expects productivity quickly.
When those factors aren’t documented clearly, an estimate—whether AI-generated or otherwise—can undervalue the case.


