AI tools typically work like this: you enter a few details (injury type, treatment timeline, time off work) and the tool returns a rough range based on patterns from other cases.
In Brooklyn Center, that approach can break down when your claim turns on details that AI can’t reliably read—like:
- How the incident happened in a real-world commute or work route. Minnesota claims often require clear documentation of what occurred “out of employment,” and insurers look closely at timelines and witness consistency.
- Whether your restrictions match what your doctor actually wrote. If your work limitations aren’t documented in a way the insurer can evaluate, an estimate may look reasonable but your settlement exposure changes.
- Whether wage loss is provable. For workers in retail, trucking-adjacent roles, maintenance, and industrial settings, payroll can include variable hours or shift differences. AI can’t verify what your pay stubs and employer records will show.
The result: an AI range can be emotionally comforting, but it may not reflect what Minnesota decision-makers will accept once the file is reviewed.


