6 Use Cases of Emerging AI Technologies in EHS Management

Making workplaces safe goes beyond compliance and regulatory requirements. In fact, focusing on employee well-being and the health and safety of all workers must be a core part of a company’s overall strategy. Therefore, proactively identifying and mitigating safety risks and working towards a ‘Zero Harm’ journey becomes essential for overall business success.

Digital technology has enabled this to a large extent, empowering safety leaders with data for greater visibility and transparency into processes to identify risks and implement controls. While this improves the decision-making process for safety-related issues, taking advantage of emerging AI/ML and other next-generation technologies can further augment decision-making and uncover issues that may have been missed earlier.

EHS Today suggests that AI-infused Health & Safety Management tools can help leaders with:

• Improving documenting of near misses and observations.
• Ensuring regulatory compliance.
• Unlocking performance gains with EHS datasets.

6 Ways AI Can Improve Safety Management

Among the many uses of AI in safety management, let’s explore 6 major areas that can ensure continuous improvement and effectiveness of safety management systems in the workplace.

Use Case #1: AI-Powered Monitoring Systems

In a workplace, certain areas, or "hot spots," have higher risks than others. By fitting cameras with AI capabilities, organizations can detect and report potential risks and near misses before they escalate into serious incidents. AI-infused monitoring systems can track:

• Air Quality: Detecting pollutants and hazardous substances.
• Noise Levels: Monitoring excessive noise that could harm workers.
• Hazardous Substances: Measuring concentrations to ensure safety standards are met.
• Risky Behaviors: Spot workers who are not adhering to SOPs or indulging in risky approaches to completing tasks
• Faulty Equipment: Automatically spot issues in equipment. For example, if the machine is throwing out sparks, it is not supposed to.

These systems can be set with critical thresholds, automatically sending notifications when these thresholds are breached. This proactive approach helps maintain a safer work environment.

Use Case #2: Risk Management

Safety regulations, including those from OSHA (Occupational Safety and Health Administration), recommend a risk-based approach for safety management and continuous improvement. AI can play a crucial role in this by identifying correlations between datasets to predict future outcomes impacting safety.

Key benefits of AI in risk management include:

• Data Analysis: AI can analyze observations, incident reports, audit findings, and inspection logs, which are rich data sources but challenging to sift through manually.
• Issue Identification: AI can identify compliance issues, serious injuries, fatalities, and other potential risks.
• Risk Prediction: AI can uncover hidden risks that may escalate into minor or major issues.

By leveraging AI, organizations can enhance risk management strategies, ensuring a safer and more compliant workplace.

Use Case #3: Prescriptive Analytics

Preventing incidents before they occur is crucial for achieving zero incidents in the workplace. Identifying risks allows organizations to implement controls that prevent these risks from escalating into major issues. AI-driven prescriptive analytics can significantly enhance this process by:

• Risk Identification: Highlighting potential risks and helping to implement preventative controls.
• Change Management: Monitoring and tracking improvements to assess their effectiveness and making necessary course corrections.
• Predictive Insights: Projecting the path of changes and revealing unforeseen events, enabling safety leaders to make informed decisions.

By leveraging AI for prescriptive analytics, organizations can save time and proactively manage safety, ensuring a safer work environment.

Use Case #4: Compliance

The regulatory environment is constantly evolving, introducing new requirements and retiring old ones. An AI-powered EHS system can help businesses improve compliance by:

• Continuous Evaluation: Using machine learning to continuously assess regulatory updates and their implications for the business.
• Automated Reporting: Generating compliance reports to reduce the administrative burden on organizations.
• Reducing Oversights: Minimizing the need for manual checks and oversights, ensuring adherence to current regulations.

By implementing an AI-powered EHS system, organizations can stay up-to-date with regulatory changes and maintain compliance more efficiently.

Use Case #5: Training and Employee Engagement

Employee participation in safety management, staying updated with policy changes, and having the necessary skills are crucial for workplace safety. AI can enhance training and employee engagement by:

• Personalized Training: Delivering lessons based on individual needs and gaps, ensuring employees receive periodic training tailored to their requirements.
• Immersive Simulations: Creating virtual simulations to train employees in hazardous scenarios, allowing them to learn and respond in a controlled environment.
• Increased Preparedness: Enhancing readiness and minimizing risks by augmenting real-life training with AI-powered training.

By leveraging AI, organizations can ensure employees are aligned with safety goals and equipped to participate proactively in safety management.

Use Case #6: Incident Investigation

Even with the best safety management systems, some risks can persist and lead to incidents. AI can enhance the incident investigation process by:

• Root Cause Identification: Augmenting the Root Cause Investigation (RCI) process with AI-driven insights to identify the underlying issues.
• Triggering CAPA: Automatically triggering Corrective and Preventive Actions (CAPA) where needed based on AI analysis.
• Collaborative Insights: Combining AI insights with input from multiple teams to provide a comprehensive understanding of incidents.

By integrating AI into incident investigation, organizations can more effectively identify root causes and implement corrective measures to prevent future incidents.

ComplianceQuest’s AI-Infused EHS Solution

ComplianceQuest provides a cloud-based safety management system built on Salesforce that is infused with intelligent automation and decision support capabilities. AI plays a key role in enhancing the user experience while improving the effectiveness and efficiency of the entire SMS workflow, providing our customers with eight value propositions:

• End-to-end efficiency: AI eliminates friction and improves resource utilization across your organization.
• Improved decision-making: AI augments human intelligence to improve the safety and creativity of employee decisions.
• Optimized Operations: AI reduces redundancies and ultimately accelerates growth.
• Increases Productivity: It reduces lead time while ensuring superior safety.
• Reduced Costs: Automation and improved safety lower the cost of manufacturing.
• Better Safety Decisions: Captures more data and provides insights for proactive safety management.
• Data Management: Improved data integrity, quality, and searchability.
• Innovation and Competitive Advantage: By freeing up resources to focus on core activities, it facilitates greater employee engagement, innovation, and value addition.

For safety management, the AI solutions from CQ include:

• Safety Agents: : Enables prioritizing workplace safety, finding similar incident records linked to an investigation, and automatically merging and closing duplicate records.
• Decision Support Agents: Improves smarter decisions, delivers embedded insights and predictions to assist in decisions, spots trends and recurrences to flag potential failures and recommend actions, and finds similar issues to recommend starting an investigation.
• Audit/Risk Agent: Reduces manual work in the audit process, helps identify and mitigate risks, automatically categorizes audit finding type, identifies and displays recurring findings, automatically prioritizes risks, and recommends risk levels.