AI tools can be useful for getting a starting range, but they often struggle with the details that matter most in real cases—especially in places like Fairbanks where timing, access to care, and winter-specific evidence can affect the record.
Common ways an AI estimate can go off track:
- Winter causation details aren’t captured well. An algorithm may not account for how ice, glare, blowing snow, or reduced traction contributed to a collision or fall.
- Care delays can change the medical story. If there’s a gap between the incident and definitive neurological evaluation, insurers may challenge causation or severity.
- Local functional realities differ. Even when the diagnosis is the same, functional limitations—mobility, transfers, skin care needs, and ability to navigate homes in colder months—can change the damages picture.
For that reason, treat AI results like a worksheet, not a prediction.


