Why Your Organization Must Leverage AI and ML to Improve Safety Management

According to a study by National Safety Council, the overall estimated loss caused by workplace safety issues stands at US $163 billion a year. Over 4 million people every year visit a doctor for work-related injuries. And, in 2020, 4000+ people died at the workplace because of a safety incident that NSC believes could have been prevented with a better safety management system.

These numbers are staggering and need the immediate attention of not only the health & safety team but also the commitment of business leaders, decision-makers, and managers to be more proactive in resolving safety risks.

Though there has been growing awareness about the need for a safer workplace, many safety managers and HR professionals are unable to identify risks, and take sufficient mitigative action to prevent incidents from happening.

They need data to accurately predict the occurrence of incidents and implement corrective and preventive actions ahead of time. There has been clear proof that the only way to reduce incidents is by not only focusing on major injuries or incidents but also by documenting and responding to safety observations and near-misses as well.

In a policy briefing published in 2021 by European Agency for Safety & Health at work, the agency emphasizes how H&S teams must embrace emerging technologies like AI and ML to improve safety processes.

How AI and ML can be Used to Enhance Safety Management Processes and Workflows

Here are some direct use cases of AI in Safety Management include:

• Predictive Analytics: Using past safety management data and metrics to predict future incidents.
• Automatic Risk Prioritization: Once safety observations, near-misses or incidents are reported, AI can be used to prioritize which safety risk to tackle with urgency. This can be done by running analytics on similar data (CQ.AI has a framework/model for similarity identification) from the past.
• Simplify Complicated Workflows: Some safety processes can be simplified by using intelligent automation. For instance, let us say someone from the H&S team is conducting a safety inspection and filling in a document. Is it possible to take in certain data points from this safety inspection process into the safety observations module automatically? Then, we’re one step closer to conducting a risk assessment and pushing it to CAPA.
• Next-Best Actions after Audit Findings: Once a safety audit is complete, a good AI model can suggest the “next best steps” by spotting similar audit findings from the past
• Better Analytics in Safety Management Reviews: By using next-generation dashboards, interactive charts, and a tower of data, the process of running management review meetings can be made more efficient (thanks to predictive insights, risk prioritization, etc.)
• Identify Similar Safety Events: ComplianceQuest EHS solution can automatically identify similar safety events and determine root cause reoccurrence
• Reduce Number of Redundant Safety Event Records: Sometimes, the same safety event gets recorded by multiple people creating redundant records. By using intelligent automation, redundant records are automatically deleted, thus saving time and increasing the productivity of the safety team.
• Automatically Recognize Type of Event Being Recorded: Using AI and ML, it is possible to classify and record the type of event being recorded in the self-reporting portal. For instance, if a safety event is being entered in the MyCQ portal, based on the description the tool suggests whether it is a complaint record, safety event record, or a change request. While these are simple value additions, it saves critical time and effort, while also making it easy to filter events by type. It also helps eliminate manual data entry errors.

Overall, an AI-enabled EHS solution can empower safety managers to improve workplace safety by facilitating new forms of monitoring and managing workers using real-time data. It can also be used to get timely warnings about risk factors as well as stress, health problems, and fatigue, as well.

5 Benefits of an AI-Powered Safety Solution

With an AI-powered safety management system, businesses can experience improved safety management, no doubt. But that’s not all. The other benefits include:

• Building an Enterprise-Wide Safety Ethos: Regulatory bodies highly recommend a proactive culture of safety. Not only the senior management and H&S professionals but every employee has a responsibility towards making their workplace safer. An AI-powered system empowers employees to be aware of risks and improve their behavior such that they do not endanger themselves or their colleagues.
• Improved Compliance by Eliminating Bias: AI-powered EHS can improve compliance by reducing bias, unforced errors, and human judgment errors that can result in regulatory penalties
• Better Customer Satisfaction: A safety incident can have a negative impact on the brand’s reputation, especially if it affects the end-user experience. With an AI-backed solution, businesses can protect their brand value and improve customer satisfaction through faster, timely, consistent, and accurate investigations and responses.
• Productivity: Data-driven decision-making can improve workplace safety, minimize incidents, and thereby improve employee engagement, positively impacting worker productivity.
• Cost Reduction: Reduction or elimination of issues through early detection guided by data trends and insights can significantly reduce costs. It also helps identify similar issues and share knowledge/lessons for timely mitigation through proven investigations, root causes, and action plans.

Key Components of AI-Backed Systems: What Lies Under the Hood?

Based on the application, AI systems can help with workplace safety in two ways. AI-embedded robots and bots can replace a worker in a hazardous situation and handle repetitive tasks, thereby reducing risks due to human errors. They can also assist human workers in a shared workspace, especially aiding aging and disabled workers in completing their tasks. These machines have to be trained to perform the tasks and make decisions based on real-time data.

AI-based monitoring and management systems can also leverage data from a variety of devices and enterprise systems to make automated or semi-automated decisions based on algorithms or more advanced forms of AI to improve workplace safety. They can help identify risks and implement mitigation strategies to prevent hazards.

The three main components of an AI solution may include:

• NLP (Natural Language Processing) algorithms can take in unstructured data and convert it to something that can be analyzed better. For instance, NLP can be used to understand the description of a safety observation entered by an employee and classify it under a particular category and escalate it to the relevant team
• Machine learning to analyze data, identify patterns or trends, and constantly make the AI model better as more and more data is analyzed
• AI-embedded robots or semi-autonomous bots that can reach locations where it is unsafe for humans, and capture images or videos for further analysis

ComplianceQuest’s AI-powered Safety Solution

ComplianceQuest’s cloud-based EHS solution built on Salesforce leverages AI to empower safety managers. It integrates with all the enterprise systems to gather data and identify risks and potential hazards. To improve safety management, CQ.AI includes features such as:

Natural Language Processing (NLP): Leveraging NLP to detect, analyze, and convert unstructured data into classifications for safety events to help drive consistency by removing bias.

Intelligent Chatbots Powered by Conversational CQ.AI: Highly interactive chatbots help users engage with the system effortlessly as needed. Integrating CQ.AI in these chatbots helps users perform complex actions through conversational CQ.AI.

Machine Learning (ML), Trending Insights, and Signal Detection: Use Machine Learning, trending, and insights to monitor, detect and predict potential high-risk issues to automate the required actions to be taken to promptly mitigate through proven and consistent workflow steps.

Vision and Image Processing: Drive tremendous improvement to productivity with AI-powered vision and image processing to identify safety risks.

Predictions and Decision Making: With growing product and process complexity, quality and safety teams can identify and prioritize critical quality and safety issues. CQ.AI’s powerful, AI-assisted recommendations automatically classify, categorize, and streamline issues. This helps with triaging safety event handling.

Email Discovery and Unstructured Data Processing: Integrated email content discovery helps to intercept, process, analyze and recognize issues and automatically trigger the creation of issue records and auto-populate (where possible) with field values to drive increased productivity.

Next Best Action/Recommendation (NBA): Fully leverage the Next Best Action capability for recommendations to the users on what is the most logical next step in the workflow. NBA helps users take action in a consistent manner by knowing what to do next.

Classification/Categorization and Prioritization: The ability to automatically, accurately, and systematically classify events early on in processes helps safety teams to quickly triage and ensure a timely and appropriate action to minimize recurring safety issues.

Similarity Identification: Avoid unnecessary investigations by improving “first-time issue” classification, reportability, and risk. Identify similar issues and re-use previous investigations to eliminate the same or similar issues repeatedly and share the lessons across the enterprise.

To know more about ComplianceQuest’s AI-powered EHS solution and how it can help safety management in your organization, ask for a demo: https://www.compliancequest.com/online-demo/