Reimagining Safety: Using AI to Predict Near Misses Before They Happen

Artificial Intelligence (AI) is transforming safety management in ways once thought unimaginable. While AI-driven hazard recognition and automated compliance tracking are becoming more common, one of the most forward-thinking applications is the use of predictive AI models to identify near-miss events before they occur.

Unlike accidents, near misses often go unreported, despite offering critical insight into system vulnerabilities. What if we could anticipate them? With AI, we can.

From Reactive to Predictive Safety

Traditionally, safety programs have been reactive—analyzing incidents after they happen to prevent recurrence. Modern safety management systems now collect large volumes of data from inspections, training records, equipment maintenance, behavioral observations, and even wearable tech. The challenge is making sense of it all in real time.

This is where predictive analytics powered by AI excels.

By training machine learning models on historical data—including environmental conditions, shift timing, worker behavior, job type, and even stress indicators—AI can detect patterns that precede incidents. These systems don't wait for something to go wrong; they flag scenarios likely to result in a near miss, empowering safety professionals to intervene early.

A 2025 study by Nowobilski et al. developed an AI algorithm based on the GPT-4o architecture to analyze descriptions of dangerous events and assign detailed causes. (Reference below). The model demonstrated a high compliance rate of 87% with expert assessments, indicating its potential as a tool to support and facilitate incident assessment processes.

Use Case: AI-Enhanced Wearables

Consider a construction site where workers wear AI-powered smart vests. These vests monitor posture, fatigue levels, and exposure to noise or heat stress. Combined with site data (e.g., task type, location, equipment used), the AI system learns to recognize when conditions are converging in a way that historically led to close calls.

When the system detects elevated risk—for instance, an operator showing signs of fatigue while performing a high-risk lift near foot traffic—it can issue a proactive alert to the worker and supervisor.

This capability goes beyond compliance—it enables targeted micro-interventions that adjust workflow, retrain teams, or reallocate tasks before harm occurs.

Ethical and Practical Considerations

Adopting AI in this way requires thoughtful implementation. Privacy, data integrity, and worker trust must be managed carefully. Transparency in how the AI functions, and clear communication about its role as a decision-support tool (not a surveillance mechanism), are essential for successful deployment.

The Future is Predictive

AI doesn’t just streamline processes—it changes what’s possible. Predicting near misses allows organizations to move from lagging to leading indicators of safety performance. It turns passive data into actionable foresight.

As the body of research grows and real-world applications mature, AI's role in predictive safety will only expand. Early adopters not only stand to reduce incidents, but also to build a culture where safety is proactive, personalized, and deeply data-informed.

Want to explore AI-driven safety innovation for your workplace? Visit VanguardEHS.com to learn more about how forward-thinking companies are redefining risk management.

Reference:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5129196