EHS Specialist
Investigate incidents and near-misses
What You Do Today
When someone gets hurt or a near-miss occurs, you lead the investigation — interviewing witnesses, examining the scene, performing root cause analysis, and recommending corrective actions.
AI That Applies
AI analyzes incident data to identify patterns and contributing factors, suggests root causes based on similar incidents across industries, and tracks corrective action completion.
Technologies
How It Works
The system ingests incident data to identify patterns and contributing factors as its primary data source. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Pattern identification improves when AI correlates your incident with similar events across the organization and industry.
What Stays
The investigation itself — interviewing people who may be scared or defensive, reading between the lines, and the systemic thinking that identifies true root causes.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for investigate incidents and near-misses, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long investigate incidents and near-misses takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle investigate incidents and near-misses?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with investigate incidents and near-misses, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for investigate incidents and near-misses, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.