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By Phil Molé, MPH
We talk with a lot of EHS professionals, and we hear a wide range of opinions about artificial intelligence (AI). Many of them say they hear a lot about AI, but don’t know what to make of it. Some have told us they want to learn more about AI and its relevance to what they do. Others say they’re already exploring AI tools but are curious to learn more about AI use cases in EHS management. At VelocityEHS, we’re focused on understanding and meeting the needs of EHS pros like you, and that’s why we’re rolling out to give you the information you’re looking for, no matter where you are on your AI journey.
This blog is the first in an ongoing series we’ll have about the intersection of AI and EHS. In this inaugural post, you’ll get a brief introduction to AI and the ways it can support more effective EHS management.
What is AI?
Let’s start first by establishing basics with a working definition of AI itself.
AI is the broad name for creating systems that can perform tasks which have historically required human intelligence (e.g. reasoning/analysis, perception, decision-making).
If AI were just a system for taking inputs and applying some internal logic to produce an output, it would be no different from what computers have been doing for decades. For example, if you’ve ever taken a computer programming class, you’re probably familiar with the concept of an if/then statement. In if/then statements, the computer performs the same operation (the “then”) given a specific user input (the “if”) part. The computer performs the operation quickly because of processing speed, but it’s doing the same things the same way, based on a firm rule.
AI is different because it can learn and adapt as it processes more data. An AI model is the “engine” that takes in data and produces outputs, and machine learning (ML) is the method of training algorithms to learn from datasets and provide useful feedback to improve performance, without being explicitly programmed to do so. You’ll learn more about ML and its applications in a future installment of this blog series.
The key takeaway is that AI simulates human thinking patterns and “learns” through an iterative process in much the same way people do, only AI “learns” at a much faster rate than humanly possible.
How Can AI Help EHS Professionals?
As you read this, you may be thinking “That’s all well and good, but I’m an EHS professional and my job is to prevent injuries and make sure everyone goes home from work safe and healthy every day. How is this AI stuff relevant to me?”
Each of the subsequent blog posts in this series will collectively answer that question and provide several relevant use case examples. For now, the short answer is that AI can help you identify and address risks much faster, so you can prevent injuries instead of just documenting them. It can also help you reduce administrative time and costs, so you can focus your efforts where they matter most.
For example, the rates of serious injuries and fatalities (SIFs) have proven stubbornly persistent over the past 20 years, even while rates of less serious injuries have fallen. That’s counterintuitive, because if we were being truly proactive about identifying risks, we’d be eliminating the serious risks first and foremost. Instead, those risks persist, and SIF rates remain flat.
This may all sound painfully familiar to you, and many EHS professionals are in the same boat. The percentage of SIFs in relation to all workplace injuries and illnesses is actually bigger than it used to be, because the overall number of all injuries has dropped while the numbers of serious injuries, where someone went to the hospital, missed days of work, or required restricted duty, has stayed the same. The reason that rates of SIFs are staying flat isn’t that you aren’t trying to prevent injuries. The reason is that mining all of your data for hidden indications of serious risks is difficult, time-consuming work.
EHS software with AI/ML changes that. For example, you can have incident management software with AI-driven ability to detect potential for serious injury and fatality (PSIF) risks buried in the details of less severe incidents, like near misses/close calls. These are exactly the risks that often go unidentified, and lead to flat rates of SIFs. Faster, easier detection using AI can break that cycle and help you finally get out ahead of risks.
Looking for More Information About AI and EHS?
Stay tuned for future installments of our AI and EHS blog series, where you’ll learn more about ML, specific use cases for AI in EHS management, considerations when evaluating EHS software and vendors, and more!
In the meantime, you can visit our AI Glossary & Learning Hub to continue learning on your own. There, you’ll find a curated list of resources covering various aspects of AI and EHS, as well as definitions of common terms.
We also invite you to download and read our new white paper, “Why EHS Professionals Can’t Afford to Ignore AI.” You’ll get a deep dive into all of the reasons why EHS pros like you have a generational opportunity to use AI to pivot from a reactive safety management approach to a proactive approach that reduces injury rates and fosters a positive safety culture.
Let VelocityEHS Help!
If you’re ready to jump to the part about how Velocity can help, we’re standing by to talk!
We’d love to tell you more about how our VelocityEHS Accelerate ® Platform powered by Velocity AI and its interactive assistant Vélo can help you to end the cycle of struggling to keep up and start the cycle of staying a few steps ahead of hazards and risks.
We’re looking forward to talking about our PSIF Insights, our machine-language powered chemical ingredient indexing, our AI enablement supporting better root cause analysis, better incident descriptions and JSA job task descriptions, better controls selection in ergonomics assessments and JSAs, and auto-processing of contractor documents, just to name a few.
In fact, why not see for yourself how we can help? Get in touch today to set up a meeting so you can see our software in action.