AI tools can be tempting because they generate a range fast. Typically, you enter details like your diagnosis, injury date, missed work, and treatment history. The tool then compares your answers to broad patterns.
The problem is that Minnesota cases are fact-driven. Two people can have the same injury label, but Minnesota outcomes can diverge based on:
- How quickly symptoms were documented after the workplace event (delays can trigger disputes)
- Whether treating providers wrote specific work restrictions that match what you actually could and couldn’t do
- The insurer’s view of causation (especially when there are prior symptoms or similar conditions)
- Whether the claim is already at a stage where impairment/future treatment becomes a major negotiation factor
An AI estimate can’t review the records an adjuster will rely on, and it can’t predict what evidence will be treated as credible.


