AI, PSIFs, and the Future of Proactive EHS Risk Management
Why traditional safety metrics fail to prevent serious injuries and fatalities—and how AI helps EHS teams identify PSIF risks before life-altering events occur.
Every EHS professional knows the job is more than a job. The commitment to getting people home safe each day is a core value that brings many into the field and keeps them there despite high stress, long hours, and persistent worries that follow them home. Protecting people in complex, risk-filled environments requires both technical skills and emotional resilience. Yet even with deep commitment, EHS teams face persistent obstacles: limited time, limited resources, limited organizational support, and inconsistent engagement from employees.
Compounding the challenge, many EHS professionals work with fragmented tools and reactive processes, and their data often lives in five different places and in five different formats. These pressures leave EHS teams carrying the safety management burden largely alone and often without the means of being proactive.
The consequences are visible in the data. Overall recordable injury rates decline over time, but serious injuries and fatalities (SIFs) remain stubbornly flat, year after year, across multiple countries. This gap tells a clear story. Traditional methods, however well-intentioned, are not doing enough to identify and prioritize the most serious risks and are not moving the needle on the events that matter most.
The Risk Picture Is Changing
Part of the challenge is conceptual. The long-standing safety triangle (see image below) suggests a predictable relationship between low-severity events, recordables, and fatalities.
Many EHS professionals still treat the safety triangle as an unquestioned fact, but decades of research show this model is inaccurate. Serious and fatal events do not follow neat ratios with less severe incidents. SIFs are rare, singular, and not predictable from the total volume of lower-severity incidents.
Only a small subset (20% or less) of incidents has the potential for a life-threatening or life-altering outcome. These potential serious injury and fatality (PSIF) events require focused attention. The problem is identifying them early enough to intervene, and being able to identify that subset of PSIF risks within the noise of your incident data
Lagging indicators like TRIR have a place (and are required by regulations like OSHA’s Recordkeeping Standard), but they simply document what has already happened. They tell you nothing about where the next life-changing event might occur. Even worse, metrics like TRIR suffer from inherent statistical unreliability, as demonstrated by studies in recent years. Small operations simply do not accumulate the worker hours needed to produce a statistically reliable rate. In practice, low injury counts often mask high latent risk.
Leading indicators are the answer, but only when designed with rigor. OSHA’s SMART principles call for measures that are specific, measurable, accountable, reasonable, and timely. PSIF identification fits these principles better than almost any other indicator, so why don’t more EHS professionals track PSIFs?
The Data Burden Has Outgrown Human Capacity
The barrier to tracking PSIFs has never been conceptual. It has always been a data interpretation challenge. Most organizations cannot manually sift through thousands of observations, inspections, near misses, work orders, and maintenance events to accurately pinpoint the few that signal elevated risk.
Organizations low on the maturity scale often struggle with having enough data. As organizations mature, they wind up with the flipside of the problem: They have a lot of data, but a high volume of data does not automatically produce better insights. Without analytical horsepower, it becomes noise. EHS teams simply do not have the time or data processing capacity to manually analyze large datasets, uncover consistent patterns, or validate statistically meaningful trends.
However, this is where AI capabilities offer EHS professionals a generational opportunity to get the visibility of PSIF risks they need. AI changes the equation by addressing the structural constraints that hold EHS programs back. It enables pattern recognition at scale, accelerates decision cycles, and identifies risks far earlier than traditional methods.
AI Amplifies the Expertise of EHS Professionals
AI is not a substitute for human judgment. It is a supplement and a force multiplier. AI, and more specifically machine learning (ML) and natural language processing, can evaluate incident data, near miss reports, audits, training records, equipment logs, and environmental data sets far faster than humans. When trained on relevant industry data and guided by subject matter expertise, AI can highlight outliers, identify risk precursors, and uncover patterns that would otherwise likely remain hidden.
This matters because the difference between a routine event and one that becomes life-altering often boils down to a handful of conditions that converge at the wrong time – a “perfect storm.” AI is uniquely suited to detect and amplify these weak signals. It supports better decisions by providing EHS professionals with statistically grounded evidence, scenario insight, and prioritized recommendations. It also reduces administrative burden by speeding investigation workflows, supporting root-cause analysis, and helping teams move to proactive intervention.
Not All AI Is Created Equal
EHS leaders evaluating AI-enabled platforms should apply the same rigor they apply to any risk control. Effective AI must be trained on highly relevant data. It must incorporate domain expertise.
It must be aligned to real EHS use cases such as incident analysis, observation classification, job hazard analysis enhancement, audit optimization, and identification of potential SIF precursors. Generic AI systems cannot deliver these outcomes as well as purpose-built systems overseen by real human EHS experts
EHS leaders should also consider transparency, explainability, and the ability to audit system outputs. Trustworthy AI enables teams to see why a model highlighted a particular pattern and how it reached its conclusions. Without this visibility, adoption and risk management will suffer.
A Generational Opportunity
AI is often described as a technology shift. For EHS, it is more than that. It is an opportunity to realign the profession with its core purpose. For the first time, EHS teams can see the entire risk landscape clearly and continuously. They can identify the conditions leading to SIFs long before they manifest. They can break the cycle of reactive safety management that has defined the field for decades.
Recent surveys show that most EHS professionals feel optimistic about AI. Their optimism reflects a simple truth: AI enables EHS leaders to fulfill the mission that brought them into the field in the first place. It gives them the capacity to do more with less, the clarity to make better decisions, and the insight to protect people from the most severe outcomes.
AI is already part of the EHS landscape, and it’s already having a positive impact. It has become one of the most important tools available to EHS professionals who are committed to sending every worker home safe, every day.