Applied AI Series: Safety Incident Management Processes Simplified with Intelligent Automation
A few weeks back, McKinsey published an article on the State of AI in 2022, culling out insights from a survey it conducted with around 1,500 business leaders from across industries and regions. Some of the key findings from the survey were:
- AI adoption has seen a real impact on business growth and cost optimization initiatives
- 50% of the respondents said there’s AI adoption in at least one area of their business operations
- AI adoption has more than doubled since 2017
- Robotic Process Automation, Recommender Systems and Reinforcement Learning are some of the key areas where AI adoption is increasing
- Some popular AI use cases include service operations optimization, risk modeling and analytics, the creation of new AI-based product and predictive service/intervention
The data from the survey resonated with us at ComplianceQuest. Over the last few quarters, we’ve focused on introducing intelligent automation and predictive analytics across quality and safety processes. Our approach to adding AI capabilities and features has revolved around the following principles:
- We want to empower your employees with real use cases for AI in quality and safety management that improve efficiency, decrease errors and free up resources.
- You are probably seeing a lot of hype and generic messaging around AI and advanced analytics capabilities without any explanation of how these technologies will be practically applied to improve your quality and safety processes. Our approach has been the opposite. We want to deliver business value and a clear value proposition to our users and customers.
- Our focus is on delivering ‘Real’ AI-powered features that are implemented into day-to-day tasks and processes.
- Our Applied AI use cases are ready out of the box to provide not only quality and safety managers, but also all users with intelligent recommendations, advanced decision-making features and predictive analytics to reduce errors, improve efficiency and boost innovation.
In this blog, we focus on how we use AI to enhance the safety incident management process.
AI-powered Safety Incident Management: Minimize Redundant Work and Streamline Investigations
Properly investigating and managing safety incidents is key to proactively improve safety and protecting employees, customers and valuable assets, which requires complete access to safety data and information.
Let’s say someone slipped in the company parking lot with multiple employees witnessing the event. Each employee reports the safety incident as recommended by company policy. As a result, multiple safety incident reports describing the same event are generated, all of which need to be investigated.
When a Safety Manager receives one of these safety incident reports, they will need to begin an investigation to get to the root cause. But before opening a new investigation, the Safety Manager will need to make sure there isn’t an existing similar incident report with an ongoing investigation. Again, this means sifting manually through previous records, which can be time-consuming and frustrating — unless you have the right EHS Solution.
With a solution like ComplianceQuest EHS, the platform automatically retrieves similar Safety Incident records and allows the manager to catch duplicate entries and merge the reports generated by all the witnesses. In case this isn’t the first time somebody has slipped in the parking lot, ComplianceQuest also shows similar Safety Incident records linked to an existing investigation, allowing the manager to link the new incident report to the ongoing investigation and minimize redundant work.
With this type of support, safety managers can better manage safety-related events, significantly decreasing the risk of non-compliance, reducing redundant work, and driving down the cost of safety.
- Retrieve and select Safety Incident records with semantically similar descriptions to recommend an investigation
- Search for similar Safety Incident records linked to an existing investigation to minimize duplicate entry
- Reduction in labor costs and operational costs for the safety department
- Decreased manual or human errors and associated costs
- Decreased risk and cost of safety incidents with quicker investigation and resolution
Overall, our focus has been on adopting a user-first approach to adding features and capabilities. Our AI models and frameworks have been designed with the same approach.