AI in the EHS Industry: Unlocking New Possibilities

June 2023

Executive Summary

In recent years, the Environment, Health & Safety (EHS) sector has undergone large-scale digital transformation with many firms adopting enterprise EHS technology solutions. [1] This transformation has helped the EHS sector leap away from the traditional approach to EHS management.  The very familiar reliance on extensive human effort and numerous manual processes is in the rear-view mirror but not completely out of sight. Automation & standardization are still needed for more effective EHS management processes.

Meanwhile, AI continues to grow exponentially with advents like the GPT model growing the influence of AI in addressing complex challenges. This blog aims to provide an overview of AI in the EHS industry, highlighting past failures and the latest advancements that have paved the way for exciting possibilities.

Understanding AI in the EHS Industry

AI in the EHS industry refers to the application of intelligent algorithms and machine learning techniques to help mitigate regulatory compliance risks, occupational hazards, natural disasters, employee safety hazards, and risks caused by environmental impact. [2] By leveraging vast amounts of data, AI systems can analyze patterns, detect anomalies, and provide valuable insights for proactive decision-making in EHS management.

Barriers and Challenges

Over the past decade, despite initial optimism, AI implementation in the EHS industry has faced significant hurdles and limitations. Some key reasons for its failures include:

Limited Data Quality: AI systems heavily rely on high-quality and reliable data. In the EHS domain, historical data often lacks completeness, consistency, and accuracy. For instance, in a close call, the significant incident that could have happened doesn’t happen, so AI may have trouble registering it as an event. [3] This inevitably hinders effective AI-driven analysis and decision-making.

Siloed Data and Systems: EHS data is often dispersed across various systems and departments, meaning the information cannot be correlated, cross-referenced, combined or harmonized to give an important end-to-end view of the process [4]. This fragmented approach restricted the potential of AI to deliver comprehensive insights.

Lack of Trust and Adoption: Stakeholder trust in AI solutions has been undermined by a lack of transparency and interpretability of complex AI models. Additionally, useful understanding of machine learning concepts is still quite uncertain amongst EHS professionals, preventing widespread adoption. [5]

Newest Advancements and their Potential

Despite previous setbacks, recent advancements in AI technologies have rekindled hope and opened a universe of possibilities in the EHS industry. Key breakthroughs include:

Big Data Integration: The integration of large-scale and diverse datasets from multiple sources enables AI models to generate more accurate and comprehensive insights. Additionally, the need to analyze large datasets has created practical applications for artificial intelligence and nearly unlimited data for the technology to process [6]. Evidently, processing big data with sophisticated AI models allows for a more holistic understanding of EHS risks and proactive decision-making.

Predictive Analytics: AI-powered predictive models can analyze historical data to forecast potential risks and incidents. By identifying patterns and trends, these models help organizations use this data as actionable information that can improve health and safety outcomes [7].

Real-time Monitoring and Alert Systems: AI algorithms can continuously monitor EHS data streams, including air quality, noise levels, and worker vitals. This real-time monitoring enables immediate detection of deviations from safety standards and AI models can generate suggestions for actions based on relevant regulations and data [8].

Intelligent Compliance Management: AI can assist in streamlining compliance processes by automating routine tasks such as data entry and processing, monitoring regulatory changes, as well as conducting risk assessments [9]. This reduces manual effort, enhances accuracy, and ensures adherence to regulatory standards.

Systemizing Compliance: This is not an area where we’ve seen much discussion but our testing of ChatGPT and other models suggests to us that it may be a super-productive way to support the ongoing workload of EHS managers.   Using AI tools to help find and define key compliance tasks and easily “systemizing” that into your internal processes through automation could be a game changer for EHS managers.  More on this in our next blog post.


AI is poised to revolutionize the EHS industry by enabling proactive risk management, enhanced data-driven decision-making, and improved overall safety and sustainability. While past failures in AI implementation highlighted challenges such as data quality and stakeholder trust, the latest advancements have unlocked tremendous potential. With the continued development of AI technologies and increased collaboration between industry experts and AI researchers, the EHS industry can embrace AI as a powerful tool to tackle its most difficult problems and create a safer, healthier, and more sustainable future.


[1] Consultants, T. (n.d.). The Status of Artificial Intelligence In Environmental, Health and Safety Management - Overview & Use Cases.

[2] BA,  written by: Sergiy T. of, By:, W., Tikhonov, S., & BA, H. of. (n.d.). AI solutions for EHS: Safety first.,language%20processing%2C%20and%20visual%20perception.

[3] Ferguson, M. (n.d.). Artificial Intelligence: What’s to come for EHS… and when? | EHS Today. Artificial Intelligence: What’s To Come for EHS… And When?

[4] Terho, K. (n.d.). Stackpath - EHS Today. Tearing Down Data Silos to Enhance Workplace Safety and Operations.

[5] Hobbs, D. (n.d.). Changing perceptions of AI amongst EHS pro’s key takeaways.

[6] Zharovskikh, A. (2023, May 9). Big Data and Ai - A Quick Overview. Big data and Artificial Intelligence: how they work together.,machine%20learning%20from%20Big%20data.

[7] Ramalingam, S. (n.d.). An introduction to predictive analytics in the EHS field. An introduction to Predictive Analytics in the EHS field.

[8] Duplan, N. (n.d.). Artificial Intelligence (AI) for better EHS compliance. LinkedIn.

[9] Garza, R. (n.d.). Taking time to understand streamlining compliance and ... - linkedin. Linkedin.