Most AI tools work by comparing your inputs—injury type, body part, treatment timeline, and time missed—to patterns from other claims. That can produce a range that looks reassuring.
The problem is that Minneapolis cases often hinge on details that AI tools can’t reliably measure, such as:
- Whether job duties and work restrictions were clearly documented after your treating provider’s evaluation
- Whether your medical records consistently link symptoms to the work event (instead of leaving room for an insurer to argue “non-work” causes)
- How the insurer treats wage loss when your work includes variable hours, overtime, shift changes, or physically intensive roles
- Whether your claim involves disputes that affect timing and leverage (for example, delays in medical authorization or contested injury descriptions)
An AI result is best viewed as a starting point for questions—not a stand-in for a settlement strategy.


