The Future of AI in Safety Management: Trends and Predictions

ChatGPT and Large Learning Models (LLM) are the flavors of the season. There is much discussion on the unlimited potential of how OpenAI’s ChatGPT is changing the way businesses work. Despite some concerns about integrity, bias, and unexplored risks, ChatGPT and LLMs have become part of business decisions, unfettering businesses to overcome resource limitations and generate innovative ideas to address their business problems. The use cases vary and range from being a virtual assistant and content generation resource to providing coding help and automated customer support.

The use of chatbots and artificial intelligence is not a novelty in safety management either. With the right AI models, it is possible to spot trends/patterns from large safety datasets, identify risks and potential hazards, analyze incident data faster, uncover gaps in safety management processes, etc. Chatbots can act as personal assistants to alert workers of potential hazards in their tasks. There is no doubt - the right AI-enabled EHS tool can improve the reactive, proactive, and predictive safety management capabilities of businesses across industries such as manufacturing, construction, and logistics. While the usage of LLMs is still nascent in the world of safety management, the potential is massive.

AI in Safety Management

Safety regulations require businesses to continuously monitor and improve workplace safety and empower employees to contribute to the process. For this, businesses need access to enterprise-wide data and tools to identify and implement safety controls. They need to provide employees with tools that can help them record observations, give feedback, and monitor progress.

AI-based safety management systems can help businesses improve in the following ways:

Reactive Processes

Incident Management: When an incident starts unfolding, safety leaders and managers need to be able to contain it in the most effective way possible to minimize damage. AI-based systems can send alerts and notifications as well as recommend the most effective way to respond to the emergency to achieve the goal.

Incident Investigation: Once the incident has been contained, the next step is to investigate the causes and initiate CAPA based on need. AI-based systems can aid safety leaders in analyzing data to identify the root cause, severity, and frequency of such an event recurring, and recommending whether to implement CAPA. If CAPA is required, the systems can also provide suggestions on the most effective control measures to reduce the chance of recurrence.

Proactive Processes

Risk Management: Past data on events, near misses, and observations provide insights into the risks in the workflows. AI-based tools can be used to uncover hidden scenarios and safety teams can swiftly plan and implement controls to prevent these potential issues before it becomes uncontrollable. This can help make the workplace safer and healthier.

Audits and Inspections: Based on the insights, safety leaders can initiate audits and inspections to ensure that all potential risks are mitigated and the workers are made aware of how to deal with such scenarios.

Training: Data and AI-enabled tools can also help with identifying gaps in skills and knowledge of safe operations and provide customized training to promote a culture of safety.

Predictive Processes

Continuous Improvement: By identifying potential risks and future scenarios, safety leaders can implement mitigation strategies for continuous improvement of safety management.

Future Trends & Prediction: What is Possible?

The use of AI is increasing across functions and industries and safety management will be no exception. Some of the areas where AI will be seen more include:

Computer Vision: Using AI-driven computer vision tools will augment human vision in high-risk zones to prevent accidents such as slips, trips, and falls. The data obtained from the tool will also help improve work environments and implement effective controls to prevent future incidents.

Embedded PPE Gear: Sensors can be used to alert in case of workers not wearing sufficient or the correct personal protective equipment when entering hazardous zones. PPE can also be embedded with sensors and computer-vision software to alert in case of dangers in the zone. The design of the PPE can also be improved to make it more fitting and appropriate. It can capture data regarding the number of times workers entered the site, the work conditions, and the risk factors to help leaders improve safety parameters.

Mobile Equipment with Embedded AI: One of the leading causes of workplace injury is dashing into mobile equipment. Installing vehicles with dashcams with embedded AI models can prevent accidents by alerting vehicle drivers of a hurdle ahead.

Improved Incident Reporting: Despite all efforts, there are times when incidents, observations, or near misses may go unreported. Using AI-based systems will reduce the gap by making available audio, image, or other data that will enable safety leaders to take appropriate action/decisions.

Predictive Maintenance: AI-based systems will also help with proactive maintenance of equipment and machinery based on performance and need. This will bring down unplanned downtimes and disruptions to production schedules, as well as reduce hazards for workers.

Detecting Unsafe Environments Automatically: Even minuscule gas leaks and other potential dangers that may go undetected by humans can be identified by AI-based monitoring systems. This will reduce potential hazards and enable timely action.

Improved Health Monitoring: AI systems will also help monitor workers' health by detecting parameters such as temperature, signs of stress or fatigue, and any other anomalies that can reduce the chances of illnesses, infections, and accidents.

Data Driven: Data is the backbone of AI and as reliance on AI systems increases, the quality of data will become very critical. Integrating workflows end-to-end will minimize human errors.

The underlying foundation of AI-enabled safety will revolve around the robustness of the data layers that can be built.

A cloud-based EHS solution such as ComplianceQuest built on Salesforce integrates with other enterprise systems to access data in real time and leverage AI to provide insights that can help improve safety management.

To know more about CQ’s EHS Solution, request a demo: https://www.compliancequest.com/lp/ehs/