AI tools typically generate a range based on generalized patterns: injury type, time off work, and broad assumptions about treatment. That can be useful if your situation is straightforward.
But Cudahy claims often involve practical complications that AI tools can’t reliably measure, such as:
- Shift timing and wage documentation: Many employees work rotating schedules or variable overtime. If payroll records don’t clearly show those patterns, wage-loss assumptions can go sideways.
- Commuting and “return to work” pressure: When employers push for quick returns, some injured workers delay or modify treatment. That can affect how the medical timeline is read later.
- Documentation gaps tied to busy workdays: Missed follow-ups, delayed reporting, or incomplete work-restriction notes can reduce settlement leverage.
In other words, the calculator might “sound reasonable,” while the missing evidence in the real file is what determines the outcome.


