AI for EHS Leaders (Part 1/3): How Do I Actually Use This Stuff?
AI Fundamentals and Practical Tools – What are the core concepts, and how are they being applied in EHS today?
Artificial Intelligence (AI) isn't just a buzzword anymore. It's rapidly becoming a practical reality across industries, and Environment, Health, and Safety (EHS) is no exception. As EHS leaders, we operate at the critical intersection of safety, environmental protection, social responsibility, and governance (ESG) – areas ripe for AI-driven transformation. The shift is already underway, with analysts predicting AI-enabled EHS software will be the standard, not the exception, by 2026.
But beyond the headlines, a fundamental question echoes through our profession:
"How do I actually use this stuff?"
This three-part series, co-authored with Arianna Howard, aims to cut through the hype and provide practical answers for EHS leaders.
Part 1 (This Article): AI Fundamentals and Practical Tools – What are the core concepts, and how are they being applied in EHS today?
Part 2: Data Security & Governance for AI use in EHS – How do we implement AI safely, ethically, and legally, especially concerning data privacy?
Part 3: ROI of AI for EHS – How do we justify the cost and prove that these AI initiatives actually work and deliver value?
The need for this understanding is urgent. While overall injury rates have improved over decades, the frequency of serious injuries and fatalities (SIFs) has plateaued. This isn't due to a lack of effort, but often stems from budget constraints, personnel shortages, and the ever-increasing administrative load of compliance and reporting. AI offers a potential pathway to break through these barriers, moving EHS from a reactive stance to a proactive, predictive, and ultimately more effective function. Let's dive into the fundamentals.
Decoding AI: Essential Concepts for the EHS Professional
To leverage AI strategically, we need a working grasp of its core components, translated into our EHS context.
Machine Learning (ML): Learning from Safety Data
Explanation: ML involves computer systems learning patterns from data without explicit programming for every scenario. Think of showing a system thousands of incident reports; it learns to identify correlations between activities, times, equipment, and outcomes. It gets "smarter" with more relevant data.
EHS Relevance: ML powers predictive safety. It analyzes historical EHS data (incidents, near-misses, inspections, sensors) to spot subtle patterns preceding accidents, enabling prediction of high-risk situations or personalized risk assessments. It can also analyze images from cameras to detect hazards automatically.
Predictive Analytics (PA): Anticipating Risks
Explanation: PA uses ML and statistics to specifically forecast future events. Like a weather forecast, it analyzes historical data and current conditions (equipment sensors, environmental readings, fatigue indicators) to estimate the probability of future incidents.
EHS Relevance: This is key to shifting EHS from reaction to prevention. PA can forecast when and where specific incidents (equipment failures, falls, exposures) are most likely, enabling targeted interventions like preventative maintenance or focused inspections. It helps optimize resource allocation for emergencies.
Computer Vision (CV): AI's Eyes on Safety
Explanation: CV gives AI the ability to "see" and interpret digital images and videos. Systems are trained to recognize objects (people, PPE), actions (lifting), or conditions (spills) from cameras (CCTV, drones).
EHS Relevance: CV enables automated, real-time workplace monitoring. It can verify PPE compliance, detects unsafe behaviors (poor ergonomics, restricted zone entry), identifies physical hazards (spills, obstructions), monitors vehicle-pedestrian interactions, and supports ergonomic assessments and remote inspections.
Natural Language Processing (NLP): Understanding Safety Narratives
Explanation: NLP deals with AI understanding and generating human language (written/spoken). It's behind chatbots and text analysis tools.
EHS Relevance: Much valuable EHS data is in text (incident reports, observations, audits). NLP unlocks this by automatically analyzing text to identify root causes, trends, sentiment, and emerging risks often missed manually. It is the broad field of AI that includes LLMs (Large Language Models). They're characterized by their massive size (billions or trillions of parameters), training on vast text datasets, and ability to perform many language tasks without task-specific training. Chatbots like ChatGPT, Gemini and Claude are examples of LLMs.
Table 1: Core AI Concepts in EHS
Often, the real power comes from combining these concepts. CV might spot poor lifting posture, PA could correlate this with historical injury data to flag high risk, and NLP might analyze observation reports mentioning back strain. This synergy highlights the need for integrated EHS platforms.
AI in the Field: Real-World EHS Applications Today
Let's look at how these concepts translate into practical tools being used now:
1. Predicting and Preventing Incidents (Predictive Analytics)
Risk Forecasting: Analyzing historical and real-time data to predict high-risk scenarios. Examples: Flagging potential gas leaks in oil & gas based on sensor readings; forecasting pipeline corrosion; predicting high fall-risk tasks in construction; identifying patient safety event trends in healthcare.
Predictive Maintenance: Forecasting equipment failure based on performance data to prevent hazardous breakdowns and downtime. Example: Optimizing vehicle servicing.
Resource Allocation: Optimizing deployment of emergency resources based on risk data.
EHS Predictive Analytics Companies (in no specific order, not sponsored):
Serenity EHS – ServiceNow-based EHS system with predictive scoring and AI-powered workflows.
Evotix – Surfaces weak signals from safety data to predict and prevent incidents.
Other EHS Software Vendors (Increasingly adding predictive capabilities): Companies like Enablon, Intelex, Cority, VelocityEHS are incorporating predictive analytics, often focused on worker safety.
Broad Industrial AI & IoT Platforms: C3 AI, Uptake, SparkCognition
Cloud Provider Platforms & Solutions: AWS, Microsoft Azure, Google Cloud Vertex AI
2. Enhancing Monitoring (Computer Vision)
PPE Compliance: Automatically verifying correct PPE use (helmets, vests) via cameras.
Safe Work Practices: Detecting unsafe actions like improper lifting, restricted zone entry, unsafe driving, or even signs of fatigue.
Hazard Detection: Continuously scanning for physical hazards like spills, blocked exits, or fire risks, often using drones for inaccessible areas.
Ergonomic Assessment: Analyzing worker movements to flag MSD risks and inform workstation design.
Collision Prevention: Tracking pedestrians and vehicles (e.g., forklifts) to issue real-time collision alerts.
CV Companies (in no specific order, not sponsored):
Protex AI – Configurable camera-based safety platform with role-based controls and flexible alerting.
Intenseye – Real-time detection of PPE, ergonomics, near misses, and unsafe acts via existing cameras.Voxel AI – Turns existing cameras into proactive safety systems with real-time alerts for near misses, PPE, vehicle interactions, and more.
Solyntek – Offers AI-powered EHS analytics with digital twin modeling to predict and prevent safety events.
3. Automating Compliance and Environmental Monitoring
Compliance Monitoring: Continuously checking operational or visual data against regulatory limits (noise, emissions) and alerting personnel to potential violations.
Permit Deconstruction: AI automatically scans permits to extract actionable requirements and populate compliance calendars.
Reporting Automation: Generating incident reports, compliance metrics, and safety bulletins automatically.
Environmental Monitoring: Analyzing sensor data for air/water quality, emissions, noise, and waste to ensure compliance and support sustainability goals. AI can even help calculate carbon footprints.
Established EHS & GRC Software Platforms (Often Integrating AI/Automation): Enablon (Wolters Kluwer), Intelex, Cority, Sphera, Benchmark ESG | Gensuite
Specialized AI & Data Analytics Platforms for Environment/Compliance: Regology, Enhesa, Libryo
4. Analyzing Reports and Observations (Natural Language Processing)
Incident/Observation Analysis: Processing free text in reports to classify incidents, identify root causes, analyze hazards, detect sentiment, recommend controls and spot emerging trends.
Safety Chatbots/Virtual Assistants/LLMs: Providing employees with instant answers to safety questions or guiding them through reporting processes.
Research Translation: Using generative AI to make complex EHS research accessible.
LLM/NLP/Chatbot Providers (in no specific order, not sponsored):
Benchmark Gensuite – ‘Genny AI’ enhances forms, scoring, and workflows with intelligent suggestions.
ehsAI – Parses complex EHS regulations into actionable tasks using NLP.
Hypertrain.ai – Uses NLP for better incident reporting, root cause analysis, and communication.
General Purpose LLMs - ChatGPT, Claude, Gemini
Check out a curated, evolving list of AI-powered tools being built & used — with a focus on real applications in workplace health, safety, and risk management. (Built by Arianna Howard)
Table 2: AI Application Examples in EHS
A key takeaway here is the dependency on infrastructure. Effective CV needs cameras, PA often relies on IoT sensors, and many advanced features are integrated into modern EHS software platforms. An organization's existing tech maturity is a critical factor in leveraging these tools.
Keeping it Real: AI's Current Capabilities and Limitations
While the applications are exciting, we must maintain realistic expectations.
Where AI Excels Today:
Processing vast amounts of data to find patterns.
Continuous monitoring for predefined conditions (CV).
Automating repetitive, data-intensive tasks.
Analyzing structured and unstructured text (NLP).
Making predictions based on historical patterns (PA).
Generating human like text, audio, and video
Where AI Still Struggles (Limitations):
True Contextual Understanding: AI correlates data but lacks genuine common sense or deep understanding of unique site-specific nuances.
Handling Novelty: AI struggles to predict risks it hasn't seen in historical data (emerging hazards, new processes).
Replacing Human Judgment: Critical thinking, ethical reasoning, complex problem-solving, and building safety culture remain human domains. AI augments, it doesn't replace.
Flawless Accuracy: AI makes mistakes. Outputs need validation, especially for critical decisions. Expecting perfection is dangerous. Alert fatigue from false positives is a real concern.
Nuanced Regulatory Interpretation: AI can track tasks but struggles with interpreting complex legal language across jurisdictions.
Hype vs. Reality (2025):
The AI hype cycle is giving way to pragmatism. Many organizations are still focused on foundational data quality and integration challenges. Success isn't about the flashiest tech, but about solving real EHS problems and demonstrating measurable impact. This reality strongly supports a "start small, scale gradually" approach. Pilot projects allow testing, validation, and building confidence before large-scale deployment.
Conclusion: The EHS Leader as AI Navigator
AI offers powerful tools to enhance EHS performance, enabling a shift towards proactive risk management. Understanding the fundamentals – ML, PA, CV, NLP – and seeing how they translate into practical applications like predictive analytics, real-time monitoring, and compliance automation allows us to identify relevant opportunities.
However, AI is not a silver bullet. Recognizing its current limitations and the significant challenges around data, cost, ethics, and workforce adaptation is crucial. AI is here to augment EHS professionals, freeing us up for higher-level strategic work, not to replace the essential human element of safety leadership.
In Part 2 of this series, we'll tackle the critical question: "How do I do this safely and legally?" focusing on data security, governance, and ethical implementation.
In Part 3, we'll address "How do I justify the cost and prove it works?" diving into ROI calculation and building the business case.
Stay tuned, and let's navigate the future of EHS together.
- Dan and Arianna
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