How to Leverage AI in Incident Management Software?

Artificial intelligence is becoming omnipresent, fuelling growth and augmenting human intelligence for improved problem solving and decision making. Leveraging data, they can identify patterns and trends hidden from the human eye and help discover new risks before they become an issue. This is especially important in safety management as it helps use a risk-based approach to improve workplace safety.

Another key aspect of augmenting human decision making with AI in safety management is that it can reduce human errors and overcome worker limitations, preventing oversights and misjudgements. It can work 24x7, monitoring and processing data even through the night, thereby accelerating the decision making process.

Key Benefits of AI in Incident Management

Some of the key advantages of leveraging AI for incident management include:

• Data-Driven Decision-Making: In the past, decisions related to safety were based on gut feel, observations, and past experiences. This is an imprecise approach with many hits and misses. Even with access to data, silos within the organization limited the effectiveness of decision making. Cloud-based solutions were an improvement over legacy systems, but with AI, the data analysis can be faster, across different data types such as images, text, and videos, and more precise and deeper. This can help identify:

• Recurring patterns
• Root causes
• High-risk issues

• Real Time Data Reading: Today, smart factories use sensors to gather data in real-time. This can be leveraged by AI to analyze data related to the environmental conditions, machinery, and wearables, and alert in case of any deviations that can lead to potential issues. It can detect unsafe behaviors, near-misses, or the unsafe use of PPE. This can help to detect potential hazards, uncover hidden trends, and determine proactive measures to improve safety.
• Predictive Analytics: AI uses machine learning and statistical algorithms to analyze historical data and predict future potential outcomes. AI can assess and review large amounts of datasets collected from different sources such as sensor readings, incident reports, camera feeds, and maintenance logs to find correlations that the humans may not be able to effectively. By uncovering hidden patterns, it can help proactively mitigate risks such as equipment failures. Continuous gathering of new data can help track changing risk scenarios and evolving hazards. This can empower safety professionals with insights needed to mitigate the risks and make the workplace safer.
• Improve Training Effectiveness: The insights gathered through real-time monitoring of safety parameters can also help with improve the training content by including all latest issues and unsafe practices. This can help deliver customized training to each worker based on their work and safety profile to make it more meaningful and effective.
• Predictive Maintenance: The equipment plays a key role in not only the quality of the processes and products, but also safety of workers. It needs to be well-maintained to prevent accidents and other hazards while improving productivity and functionality. AI can help with monitoring maintenance schedules and repair of vital machinery in a timely manner. It can help detect and analyze wear and tear, potential failures, and prioritize crucial maintenance and repairs for greater efficiency.
• Resource Optimization: Not all risks and hazards are equal. Addressing some is more important than addressing others. AI helps with prioritization of risks for optimal utilization of resources and focusing on high risk areas. This can help reduce the cost of safety, improve the lifespan of assets, and enhance workplace safety.
• Compliance: AI solutions can track and monitor the performance of incident management systems and ensure they comply with relevant regulations and standards. In case of any deviations and anomalies, they can alert the authorities for timely action.

Challenges in Using AI-Based Incident Management

While AI bestows many benefits on the safety management system in organizations, it is not without its risks. Some of these include:

• Data Bias: AI depends on the data gathered by the organization for effectiveness. If the quality is bad or there is an inherent bias, AI cannot rectify it or alert users of the potential risks. The insights it provides will also be of poor quality and biased, thereby nullifying the benefits of AI-based safety management.
• AI Algorithm Evolution: AI uses machine learning, which means the algorithms learn from experience and keep evolving. There is no precedence to which direction it can take and therefore, it is important to constantly review the robustness of the solution and course correct as needed.
• Security: AI usage must be governed by privacy laws, respecting the privacy of workers and customers, and even corporate trade secrets. It can only augment human judgment and not replace it. Therefore, automation must include human authorization as a safety net.
• Integrated Workflows: AI is a nascent technology and most enterprise systems may not be able to integrate AI capabilities. Therefore, keeping them updated is necessary for the effective use of safety systems, data collection, and storage.
• Mindset Barriers: Adopting new methods and technologies may face overt or covert resistance. Comfortable with traditional approaches, workers and safety teams may either not be willing to change or sustaining may be a challenge. Creating awareness about the benefits and providing training will be critical to ensure continued use.
• Finding the Right Fit: It is important to find the right solution to leverage AI in incident management. The cost, support, and training services of the vendor will be critical in influencing the decision. Pilot the solution before implementing it enterprise wide.