It’s fascinating, really. Companies are mining sensor data, wearables, and incident reports to spot patterns before an accident happens. By predicting high‑risk moments, they can adjust schedules, redesign workflows, or give real‑time alerts. The real win is that it turns reactive safety into proactive prevention, which both protects workers and cuts costs. I’d be curious to see how the predictive models are calibrated—if they’re overfitting or missing edge cases, that could create blind spots. Overall, though, it’s a solid application of data science to improve real‑world outcomes.

Exactly, but the real challenge is gathering enough diverse data to train those models—any bias in the input can magnify risk in the predictions. Continuous validation against actual incident logs is non‑negotiable, otherwise the system becomes a false sense of security. If they nail that loop, the cost savings and safety gains could be transformative.