Not all organizations experience AI value applied to environment, health, and safety (EHS) processes and practices at the same pace.
Perceived benefits shift significantly, depending on an organization’s cultural readiness for AI. We discovered this in the analysis of data from a recent survey project Wolters Kluwer Enablon conducted with the National Safety Council (NSC). Overall survey results are contained in a recently published report you can download here.
Among the many questions we asked 1,053 safety leaders and professionals in the U.S. was to assess their organization’s cultural “readiness” for adding AI workplace tools to improve their EHS operations. We followed with additional AI questions that asked them to:
- Describe their organization's current use of AI in EHS programs,
- Rank their level of investment in technologies that feature or include AI functionality for EHS over the next 12 to 24 months, and,
- Rate EHS functions at their companies that could benefit from AI.
Applying cross-tabulation to these queries, we developed a basic maturity model that shows how AI in EHS expectations evolve from abstract efficiency gains toward perceived outcome‑driven operational advantages. This discovery forms the foundation of a simple, four‑stage AI maturity model, outlined below, that shows how organizations move from skepticism about AI in EHS to strategic impacts it can deliver.
Level 1: Not Ready (Skepticism and unclear value)
Respondents from organizations who say they are not yet ready to adopt AI represent a small (3%) but important segment. This group expresses uncertainty about AI’s current relevance to EHS and is, not surprisingly, the highest proportion of respondents saying they “do not see any major benefit” from applying AI in EHS at all.
At this stage, AI may be viewed as more theoretical than real, disconnected from daily safety and compliance challenges, or perhaps misaligned with existing EHS processes and skills. Delving deeper into responses from this smaller group reveals they also have limited awareness, competing priorities, or a lack of trust in their data quality. Without a foundation of confidence in digital systems or analytics, AI for this group would appear to be abstract and unnecessary rather than enabling.
Level 2: Partially Ready (AI as a data tool)
Respondents from organizations partially ready say the top perceived benefits of AI include the ability to predict and prevent incidents (30%), process large volumes of data (30%), and improve reporting and compliance efficiency (26%). Notably, however, the benefit of faster decision‑making drops to just 12% among respondents, suggesting AI may be seen primarily as a back‑end analytics engine rather than a driver of real‑time action.
This stage reflects early or siloed AI adoption. Organizations seem to recognize that AI can help manage complexity by sorting data, identifying patterns, and automating reporting, but they have not yet embedded it into operational decision‑making. AI in EHS value is acknowledged, but mostly in support functions.
Level 3: Mostly Ready (Predictive and analytical value)
Mostly ready organizations see predicting and preventing incidents becoming the dominant benefit (34%) – the highest response across all segments. Improved reporting and compliance efficiency (26%) remains important, while the ability to process large volumes of data (20%) rounds out the top benefits.
This pattern of responses suggests these organizations are in an AI scale‑up phase and it is starting to deliver tangible improvements in risk anticipation and insight generation. Safety professionals here are increasingly confident using AI‑driven analytics to identify emerging hazards and prioritize actions but are not yet applying it to fully support how decisions are made.
Level 4: Fully Ready (An operational and strategic asset)
At this highest maturity level, fully ready organizations view AI not simply as a tool, but as means to achieve better EHS outcomes. Their top perceived benefits are more evenly distributed across predicting and preventing incidents (28%), faster decision‑making with better data (26%), and improved reporting and compliance efficiency (27%).
What distinguishes this group is how AI is used. It is embedded into workflows, supporting proactive risk management and accelerating decisions. AI is helping these leaders and frontline teams act earlier, respond faster, and manage safety performance with greater confidence. At this stage, AI is clearly recognized as an operational necessity and provides a strategic advantage.
What the maturity model reveals
It’s clear most organizations do not immediately start their AI journey by seeing predictive prevention and faster decisions. They grow into those benefits as AI trust, skills, and use cases mature.
Advancing AI maturity is not about skipping stages, rather it is deliberately building toward an outcome‑driven use of data. When organizations move from simply managing information to actively shaping decisions, AI shifts from an interesting concept to a powerful force for safer, smarter, and more resilient operations.
Get your copy of the Wolters Kluwer/NSC full report – The Safety Shift: EHS Readiness in 2026.
