Last week, on November 19th, 2025, we gathered to discuss a shift that is transforming the landscape of Environment, Health, and Safety (EHS): the move from static paper trails to dynamic, data-driven patterns.
If you missed the presentation, the core message went beyond simple digitization. While taking a physical paper and turning it into a PDF is a start, it doesn’t solve the core problem of data accessibility. We are talking about leveraging Artificial Intelligence not just to structure data, but to analyze it, generate solutions, and actively monitor risks in ways that were previously impossible.
Here is a recap of how AI can transform your EHS operations and how you can apply these tools today.
Defining the Terminology: What Are We Talking About?
Before we dive into solutions, it is helpful to clarify exactly what we mean when we say “AI,” as the terms are often used interchangeably.
- AI (Artificial Intelligence): This is the broad discipline of computer science dedicated to creating systems that can perform tasks that typically require human intelligence. Examples include spam filters, shopping recommendations, or detecting tumors in x-rays.
- GenAI (Generative AI): This is a subset of AI that creates new content, such as text, images, audio, or code.
- LLM (Large Language Model): A specific type of GenAI that can “understand” and generate human language. This is the technology behind tools like ChatGPT, Google Gemini, and Claude.
Reality Check: What AI is Good (and Bad) At
To use these tools effectively, you have to understand their limitations. AI is not a “magic wand” that can do everything.
What AI is Good At:
- Processing Massive Data: It excels at taking huge amounts of unstructured info (like text or voice) and organizing it.
- Pattern Recognition: It can spot trends or anomalies (like tumors in x-rays or unsafe behaviors on camera) faster than a human.
- Drafting Content: It is excellent at generating first drafts of text, code, or images.
What AI is Bad At:
- Nuance and Context: It cannot understand nuance and context the way a human does.
- Contextual Blindness: It often lacks the broader situational awareness required for complex decision-making.
- Fact-Checking: It can suffer from “hallucinations,” meaning it can confidently present false information as fact.
The “Sandwich Method”: Keeping the Human in the Loop
Because AI has “contextual blindness” and can “hallucinate,” we cannot let it run on autopilot.
To leverage it safely, we recommend the “Sandwich Method”:
- The Top Bun (Human): You set the context and provide the data.
- The Meat (AI): The AI processes the data and creates the draft.
- The Bottom Bun (Human): You verify the claims and ensure accuracy.
Quick Wins: DIY AI for the EHS Professional
You can start using widely available tools right now to make your daily life easier.
Instant Toolbox Talks: Instead of writing a talk from scratch, copy a segment of a regulation or an SOP. Prompt an LLM to “Write a 5-minute, easy-to-understand toolbox talk script for frontline workers based on this text”.
Custom Safety Visuals: Need a poster on handwashing? Instead of searching for generic clip art, use image generation tools (like Canva or Gemini) to create a specific safety poster that encourages employees to protect themselves and their team.
Categorizing Data with Google Sheets: If you have a massive list of historical safety observations (e.g., hundreds of rows of text), manually categorizing them takes hours. You can now use AI directly inside Google Sheets to analyze a whole list of data, asking it to categorize each observation into specific buckets (like “PPE,” “Slip/Trip,” or “Electrical”) instantly.
Quantum’s AI: Built for EHS
While free tools are excellent for general tasks, they often fall short in an industrial context because they are not trained on EHS-specific data. AI is only as good as the data you feed it.
Quantum’s AI is different because our database is specialized. We don’t just use general models; we apply them to verified EHS frameworks. Here is how we are applying this technology today:
1. Inspection AI: Automating the Analysis
Inspection forms can be incredibly dense, sometimes over 100 questions long. Manually analyzing these to find the one or two failures is time-consuming. Our Inspection AI handles this analysis for you:
- Summarization: It summarizes the inspection form, so users don’t have to manually analyze the document one question at a time.
- Corrective Actions: It automatically suggests corrective actions for any inspection findings.
- Regulatory Alignment: It defines exactly which OSHA regulation a specific finding is violating, removing the guesswork from compliance.
- Risk & Hazard Classification: It automatically generates finding descriptions, classifies the hazard, and assigns a risk level.
2. SDS OCR: From Unstructured to Structured
One of the biggest headaches in EHS is managing Safety Data Sheets. These are complex documents, and if you need to search for “all carcinogenic products,” a standard database of PDF files won’t help you.
Quantum uses a combination of OCR (Optical Character Recognition) and AI to transform this unstructured data into structured data. Instead of spending 15 minutes manually entering data for a single SDS, the AI extracts the relevant information and places it directly into the database fields, making your chemical inventory instantly searchable and reportable.
3. Computer Vision: 24-Hour Intelligent Identification
This is not a future concept; it is available now. We are using AI-enabled cameras to automatically detect unsafe behaviors in the workplace.
The system provides 24-hour intelligent identification and real-time reporting for hazards such as:
- Crossing into forklift areas or restricted zones.
- Detecting smoke or phone usage in prohibited areas.
- identifying a lack of PPE (hard hats, vests) or proper footwear.
- Detecting falls or “man-down” scenarios.
4. Voice-to-Incident Reporting
The “old way” of reporting incidents causes friction: work stops, forms are lost, and handwriting is often illegible. This leads to vague descriptions because people dislike typing long narratives on their phones.
The “AI way” takes only 2 minutes. An employee simply scans a QR code and describes the incident using their voice. The AI converts that voice note into text and automatically extracts the data to fill out the form fields. This removes the barrier to technology, allowing workers to report naturally while ensuring you get detailed, structured data.
The Future: AI Analytics
Looking ahead, we are currently developing Quantum AI Analytics. While our current tools focus on data input and monitoring, this feature will focus on deep conversation with your data.
Imagine having an assistant named “Alice” who has access to all your training logs, incident reports, and inspection data. In the near future, you will be able to ask plain-English questions like “Analyze root causes for Hand Injuries” or “Show me incident trends for 2024 vs 2023” and receive instant, data-backed answers.
Summary
By moving from paper to pattern, we aren’t just digitizing; we are building a system that sees, listens, and analyzes. Whether it is through smarter inspections, automated SDS management, or computer vision, AI helps us build a safer, more efficient culture.
In the coming weeks, we will be sending out emails diving deeper into each of these AI features. To ensure you don’t miss out on these deep dives, be sure to sign up for our newsletter.
