AI tools typically calculate a “likely range” based on typical injury patterns and assumed timelines. That approach can miss the realities that show up in Monroe cases, such as:
- Intersection-heavy crashes where fault can hinge on traffic signals, turning movements, and whether a driver yielded
- Construction and lane shifts that can affect visibility and stopping distance
- Commuter traffic timing (rush hours) that influences witness availability and video preservation
- Road condition disputes (potholes, debris, wet pavement) that can change how insurers frame causation
In other words, the estimate might be mathematically plausible—but if the underlying facts are incomplete, it won’t match what insurers will pay.


