Artificial intelligence (AI) is gaining traction in environment, health, and safety (EHS) and process safety management (PSM), but AI is only as strong as the quality, completeness, and accessibility of data consumed.
Many organisations still rely on paper-based records, siloed systems, and unstructured data. This impacts AI’s ability to generate meaningful insights for EHS and PSM.
According to research conducted by Wolters Kluwer Enablon and the National Safety Council that surveyed 1,053 safety and operations professionals in the US, only 11% say their safety processes are fully digitalised and integrated. A considerable 55% still rely heavily on manual and paper-based safety systems.
While AI can still be applied in partially digital EHS/PSM environments, use cases tend to focus on productivity improvements. For example, natural language processing (NLP) can support incident reporting, audit analysis, document classification and retrieval, trend identification in historical lagging indicators, and for reporting or regulatory submission drafting.
While valuable, these applications primarily reduce administrative burden rather than fundamentally transform risk management.
A necessary digital foundation
Unleashing the full power of AI requires EHS and PSM processes to become digitally integrated and data driven. This allows AI to support drastic organisational improvements in understanding, managing, and mitigating workplace risk. A digital foundation offers a more dynamic platform for providing continuous, real-time views of operational risk across people, processes, and assets.
When PSM and EHS systems are fully digital, both structured and unstructured data become accessible and useful. AI can then continuously analyse this data to identify patterns, detect emerging risks, and correlate signals across operations to see early signals such as outdated or degraded safeguards, and quickly recognise recurring near misses, permit deviations, training gaps, or changes introduced through management of change.
With this foundation, organisations can move beyond periodic reviews and fragmented reporting towards ongoing risk awareness. Leaders gain confidence that workplace safety is being managed systematically, operators receive clearer guidance at the point of work, and safety professionals can prioritise interventions based on forward-looking indicators instead of lagging outcomes.
Improved and proactive management
In traditional PSM programs, much effort is spent on periodic reviews of procedures, hazard analyses, inspections, and incident records. While essential, these processes are often conducted in isolation, allowing risks to accumulate and go unnoticed between review cycles.
AI transforms PSM by driving continuous analysis and potentially highlighting early signs of weakness across safeguards, human performance, and operating conditions. AI models can ingest data from control systems, maintenance records, safe work permits, training history, management of change requests, and incident reporting systems, then detect patterns that show rising risk. Operational leaders can then be alerted to intervene earlier, particularly when corrective actions might be less disruptive to the business, and far less costly to implement.
In PSM, the integrity of alarms, interlocks, relief systems, procedures, and human actions is critical. AI can analyse test results, bypass frequency, alarm behaviour, and maintenance history to identify degrading barriers or those being relied upon too heavily.
AI can support human factors management, which is a frequent contributor to incidents.
By analysing patterns in permits, training compliance, shift handovers, and incident narratives, AI could identify conditions where human error is more likely to happen, such as during high workload periods, night shifts, complex temporary changes, or inconsistent procedural adherence.
This insight allows leaders to adjust staffing, training, or work planning proactively rather than reacting after an error occurs.
AI-enabled PSM potentially puts ongoing risk awareness into routine operations. Operations managers gain clearer foresight into where hazards are increasing, why controls may be under strain, and what actions will most effectively prevent escalation.
The likelihood of catastrophic incidents could be significantly reduced and there’s potential for significant improvement in factors including operational stability, regulatory confidence, and trust between leadership and the workforce.
What the research says
Research from our 2026 survey of US safety professionals supports the idea that AI has exciting potential as a risk tool for core PSM activities.
We asked the 1,053 survey participants how AI could benefit EHS processes in general. A total of 84% rank risk assessment and hazard identification as a “very high” potential benefit, 83% said AI benefits could be “very high” when applied to predictive maintenance and asset safety, and 77% said likewise for incident reporting and investigation.
Confidence in AI for what might be considered more human-centric domains such as training and workforce safety engagement (76%), health surveillance and ergonomics (74%) and emergency response planning (76%) received fewer “very high” potential endorsements.
Our research reveals respondents are cautious about AI’s role in human behaviour, emergency decision-making, and experiential learning, all of which are critical to PSM, but harder to formalise and validate.
Additionally, 51% of respondents said they worry about overreliance on AI instead of human judgment. A total of 38% said they have concerns over the lack of transparency in how AI makes decisions.
Our research concludes that organisations expect AI to support rather than replace engineering and operational judgment, and that AI trust is highest where it enhances rigour, consistency, and signal detection, rather than human decision authority.
Among AI’s clearest values in PSM appears to be as a risk intelligence layer across processes using highly hazardous chemicals, helping teams see hazard accumulation and barrier erosion sooner.
This content was provided by Enablon
(Photo: Shutterstock)
