AI tools typically work like this: you enter a diagnosis, injury date, and basic work impact, and the system returns a “likely range” based on patterns from other cases.
The problem is that workers’ comp isn’t a plug-and-play math problem. In Burleson, the cases that end up undervalued usually share a few preventable gaps:
- Work restrictions aren’t matched to your actual duties. If your treating provider writes broad limitations, but your employer’s job description expects something else, the insurer may argue your wage loss is overstated.
- The timeline looks inconsistent. Even a small delay between the incident and the first documented symptoms can become a focal point for Texas insurers.
- Treatment notes don’t clearly describe functional limits. AI can’t interpret whether your medical record supports “work capacity” findings—adjusters do.
- Wage loss inputs are incomplete. Overtime patterns, shift differentials, and variable schedules common in many Burleson workplaces can be left out of your estimate.
A tool may produce numbers that look reasonable—but the insurer’s evaluation depends on evidence, not impressions.


