The Role of AI and Machine Learning in Modern EHS Management

Based on data from the National Safety Council, the estimated annual loss from workplace safety issues is $163 billion. In addition, four million people visited a doctor for a work-related injury in that same period. In 2020, more than 4,000 people died due to a safety incident that likely could have been prevented with better EHS management.

These are big numbers and should show you how important it is to prioritize health and safety in your organization. Despite that, some HR professionals and EHS managers aren’t able to identify risks and put actions into place to prevent incidents from occurring. Thankfully, technology is making it easier every day.

Data is needed to accurately predict the incidents that may occur and put preventative actions in place before they do. The evidence that supports this shows that you should focus on major incidents and injuries but also respond to and document safety observations and near-misses at the same time. Machine learning (ML) and artificial intelligence (AI) are the keys to this.

What Are AI, ML, and NLP?

AI and ML are taking the world by storm. Many businesses and organizations are creating ways to integrate AI and ML into their workflows to automate tasks and reduce human error. They also have a huge place in the world of EHS management.

For instance, AI systems can improve workplace safety in several ways. Bots might replace workers in hazardous environments and do repetitive tasks. They can also act as an assistant to humans for quicker task completion. Machines can be trained to do tasks and make decisions on current data.

AI management and monitoring systems utilize data from systems and devices to make decisions based on algorithms and other forms of AI to ensure optimal workplace safety. In addition, the use of this technology can assist with identifying risks and placing mitigation strategies based on hazards.

There are three basic parts to AI solutions, including the following:

• AI bots and robots can be used in areas that are unsafe for humans, and capture videos and images for further analysis.

• Machine learning is used to identify trends and patterns, analyze data, and ensure the AI model is improving as more data is compiled.

• Natural language processing (NLP) algorithms take raw data and convert it so it can be more easily analyzed. For instance, it can “read” a safety observation an employee writes, classify it under the right category, and escalate it to the appropriate leader.

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How Machine Learning and Artificial Intelligence Can Improve EHS Management

Many different solutions are available for EHS management that hinge on AI and ML. A few of the most common and widely used include:

• Best actions – Once you carry out an EHS audit, AI models can be used to suggest what the next best steps are to take. It can look at similar findings from previous audits to help you choose the right strategy.

• Event type recording – AI and ML can classify and record events for later use. They can also look at the description and classify the event based on type. This saves a great deal of effort and time and can prevent data entry errors.

• Identification of similar events – Some EHS solutions powered by AI and ML can quickly and automatically identify other similar safety events. This makes it easier to determine the root cause behind the incidents.

• Improved analytics – With the use of data, charts, and dashboards, management review meetings can be more efficient for everyone involved. This is due to risk prioritization, predictive insights, and other entries on this list.

• Predictive analytics – Metrics and safety management data from the past can be used by AI to predict what sort of future incidents are the most likely to happen.

• Reduced redundant records – In some cases, the same event may be entered by multiple people, which leads to redundant records. Intelligent automation can remove these records to save time and increase EHS productivity.

• Risk prioritization – After incidents, near misses, or safety observations are made, AI can determine which should be tackled first. This can be done using analytics on some of the similar data from the past.

• Simplified workflows – Automation can simplify some parts of safety management. For instance, AI can take data from an inspection and process it where it needs to go. This makes it easier to complete a risk assessment and move forward.

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Conclusion

EHS solutions built on AI are a great choice to empower EHS leaders. Many of them integrate with your current systems to compile data, identify risks, and keep you aware of possible hazards. This includes things like natural language processing, intelligent chatbots, trending insights, image and vision processing, and more.

As more data becomes available, safety and quality teams can find and prioritize the most pressing safety and health issues. AI-assisted recommendations help you streamline, categorize, and classify issues. Many make recommendations about the next steps to take to contain a problem and avoid damage. AI and ML are paving the way to better EHS management and we’re excited to see how they improve in the future.