AI tools typically generate an estimate based on patterns from past cases. That’s not the same as evaluating your file. In Broomfield, the biggest problem is usually not the math—it’s the missing context.
Common ways AI estimates go wrong include:
- Work restrictions that aren’t clearly written (or don’t match job demands). If your treating provider’s restrictions are vague, the insurer may argue you could return with minimal limitations.
- Gaps in treatment. Even short delays can create uncertainty about causation or whether symptoms are improving.
- Wage loss that isn’t fully captured—for example, overtime, shift differentials, or inconsistent schedules tied to job sites around the Denver metro.
- Unresolved disputes (notice, incident details, compensability, or impairment). An “instant range” won’t account for how strongly the insurer intends to contest issues.
The result: an AI range may look plausible, but it can still be too low—or sometimes too high—depending on what the insurer can argue using your documentation.


