New research shows the next phase of safety management will merge traditional benchmarks with predictive, AI-driven insight.

TORRANCE, Calif., Dec. 18, 2025 /PRNewswire/ — CanQualify has released a new research framework outlining a shift that has been building for years: the move from relying solely on historical safety metrics, to incorporating real-time, predictive intelligence into risk evaluations.

The study is based on analysis of a large, multi‑year dataset combining contractor records, wearable data feeds, and industry benchmarks, alongside lesser‑used metrics such as near‑miss frequency, fatigue indicators, training follow‑through, and payment behavior patterns. When assessed together, these signals allow predictive models to identify potential risk with 25 – 40% more accuracy than using lagging indicators alone.

For decades, TRIR and EMR have shaped how companies understand safety performance. They remain fundamental references, but they also tell only one side of the story. A spotless record can sometimes mask early warning signs that don’t show up in year-end summaries or insurance modifiers.

“Historical metrics are still important, they’re not going anywhere,” said Aaron Harker, VP of Operations at CanQualify. “Anyone responsible for safety knows the toughest challenges often appear long before an incident occurs. Predictive analytics help us spot those patterns sooner, giving us the insight to act before problems escalate. The goal isn’t to replace what we’ve learned from the past but to build on it.”

Key insights from the CanQualify 2026 Predictive Safety Framework:

Leading indicators reveal risks traditional metrics overlook. Patterns in training engagement, near-miss activity, worker fatigue, and financial stability provide a clearer picture of emerging risks than historical rates alone.
Predictive models improve early detection. With the right data inputs, AI systems can surface risk trends weeks or months before they typically appear in incident logs.
Real-time intelligence is becoming standard. More safety tools are now capable of identifying shifts in conditions and sending alerts or recommendations automatically — not as a replacement for professionals, but as another set of eyes on the field.
The most effective approach is hybrid. The research shows the strongest outcomes come from combining historical benchmarks with predictive scoring, not choosing one over the other.
Organizations using both see stronger results. Early adopters report fewer incidents, better planning accuracy, and more stability across their supplier networks.

“We’re not looking at a future where analytics makes the decisions for us,” added Robert Hacker. “We’re looking at a future where safety teams are equipped with clearer, earlier, and more reliable information than ever before.”

The full framework, From Reactive to Predictive: The 2026–2030 Roadmap for AI-Driven Risk Intelligence, is available now at https://canqualify.com/2026-predictive-safety-framework-report/

About CanQualify

CanQualify is a modern prequalification and supply-chain risk platform for organizations committed to bringing safety to the forefront. By combining network scale with predictive AI, CanQualify empowers companies to move beyond paperwork-driven compliance and toward proactive, data-driven safety intelligence that helps protect people, operations, and performance.

For more details visit, https://canqualify.com/2026-industry-trends/

Media Contact:
Robert Hacker
VP Sales and Marketing
[email protected]

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