Using AI to Move from Lagging to Leading Safety Indicators

briq Product Team · Field Solutions · Nov 2025

How construction companies are using AI to predict safety incidents before they happen — not just report on them after.

Construction safety metrics have historically been lagging indicators: TRIR, DART rates, near-miss counts. They tell you what already happened. By definition, they can't prevent the next incident.

AI enables a shift to leading indicators — patterns and signals that predict elevated risk before an incident occurs. Otto analyzes field data from multiple sources to identify these signals.

Data sources include: daily field reports (natural language analysis for safety-relevant observations), weather forecasts (correlating conditions with historical incident patterns), schedule data (identifying periods of compressed timelines that historically correlate with increased risk), workforce data (new worker ratios, overtime patterns, trade density), and equipment logs.

The output isn't a generic 'risk is elevated' alert. It's specific: 'Elevated fall risk on Building C, Level 4 this week due to compressed concrete pour schedule, 3 new workers on crew, and forecasted wind speeds above 25 mph.'

Early adopters report that AI-generated safety intelligence is changing pre-task planning conversations. Instead of generic safety talks, foremen are briefing crews on specific, data-informed risks for that day's work.

We're cautious about claiming lives saved — the data isn't there yet to make that assertion. But the leading indicator framework is sound, and the early results are encouraging.