Workplace hazards have long plagued businesses, their employees, and their profitability. Safety managers and teams can benefit from his AI-driven digital transformation, as can finance and marketing personnel.
The U.S. Bureau of Labor Statistics recorded 5,190 fatal work-related injuries in 2021, the most recent year for which data are available. In addition, more non-fatal accidents occur in hazardous environments. Human loss and pain are unnecessary and avoidable, but the business costs are many and varied. Clearly, industrial safety should be a top priority for companies.
AI tools and techniques can be applied to many safety use cases. AI-enabled safety technologies include drones, robotics, wearables, sensors, smart devices, augmented reality (AR) and virtual reality (VR), computer vision, mobile apps, and analytics software. Hardware components collect data (such as sensors) or perform actions (such as robots). A software component relies on machine learning to analyze patterns and generate insights into safety issues.
Business leaders should appreciate the breadth of applications for AI technology in industrial safety. The culture and risk management policies within the company should also be conducive to adopting his AI technology for safety purposes.
Application of AI to industrial safety
According to the U.S. National Institute for Occupational Safety and Health, the primary way to protect workers is to control their exposure to occupational injuries. His NIOSH hierarchy of controls begins with personal protective equipment (PPE). Next are organizational controls that affect how employees work, and engineering controls that isolate employees from hazards. Finally, there are methods of substitution (replacing the hazard) and elimination (removing the hazard entirely).
While hardware components collect data (such as sensors) or perform actions (such as robots), software components rely on machine learning to analyze patterns and generate insights about safety issues. increase.
Artificial intelligence can help at each of these levels in the following ways:
Compliance with safety regulations and identification of hazards. Workers do not always comply 100% with PPE requirements. This is because they find equipment awkward or take safety for granted. Computer vision solutions such as CCTV systems with AI-enabled cameras and software can monitor designated workplace areas for PPE violations.
Identification of dangerous goods. Hazardous materials, in this case, include debris and spills that can cause injury. Over time, safety patterns and trends can be identified and linked to enterprise data and used to improve safety and operational performance.
Monitoring fatigue symptoms. Workers operating dangerous and heavy equipment or vehicles must be vigilant at all times. On-site or in-vehicle facial analysis can identify signs of fatigue and drowsiness. Employees can receive alerts and be advised to return to work after a period of rest.
Fall detection during construction. Many worker injuries during construction are due to falls. AI-powered fall detection software, often in the form of simple phone apps, is designed for timely detection.
Site survey using drones. Drones and self-driving cars can be used to monitor and inspect construction sites and other hazardous sites instead of putting employees at risk.
Conversational AI for safety. Chatbots trained in safety procedures and manuals can use natural language processing to answer employee safety-related questions.
Incident reporting using voice. Voice makes it easy for employees to report incidents. AI can transcribe spoken incident reports and extract relevant data for further analysis.
AR for equipment repair. Using AR, employees receive maintenance orders with real-time information on diagnostics and repairs.
Safety training in VR. Safety training is a good fit for VR because it simulates various dangerous scenarios. Employees can practice their responses in a controlled environment.
Corporate roadmap for industrial safety
AI and smart technology are just one piece of the puzzle. Organizational culture and operational risk management processes are equally important.
Work accidents have both tangible and intangible costs. Financial costs include fines for violations of the Occupational Safety and Health Administration, commonly known as his OSHA in the United States. medical expenses for the treatment of employees; Payment of Workers’ Compensation Claims and Litigation Costs. Non-quantifiable costs include lost productivity due to medical leave, decreased employee morale, and damaged brand reputation. The case for AI adoption in business safety scenarios is therefore strong. The status quo is unsustainable and AI can help mitigate these problems.
AI can help companies where they need to improve their data practices. A data-driven approach to workplace safety is more likely to be adopted when other parts of the organization also foster a data-driven culture. Many organizations don’t collect the operational and process data they need to keep workers safe. Even if data is collected, it will be used for post-mortem reporting rather than for root cause analysis or proactive remediation.
Enterprise safety and risk managers should assess current safety practices and explore opportunities to move from a manual, compliance-oriented approach to a data-driven, proactive approach.
