Utilizing AI to Generate Actionable Insights From Unstructured Data

The Role of AI and Analytics

Generative AI is transforming the way we approach unstructured data. With the arrival of large language models like ChatGPT, unstructured datasets can now be analyzed and consolidated at a rapid pace in ways that were previously impossible. This has led to a significant shift in how businesses view unstructured data, recognizing it as a critical enabler of innovation.

A substantial advantage of generative AI is its ability to unlock previously inaccessible insights from unstructured data. This data has traditionally been difficult to analyze due to its unstructured nature. With the assistance of generative AI, patterns and trends within this data can be identified that were previously hidden. These patterns and trends lead businesses to clear action and next steps.

This has major benefits for businesses across all industries and is extremely helpful from a health and safety perspective when trying to review large-scale data such as safety observations overlaid with incidents, or identify systemic issues across a workplace or enterprise. With access to these insights, companies can make more informed decisions about critical risk reduction, communicating safety, and focus on work areas/work zones. They can also gain a deeper understanding of their workforce, the employee safety experience, training requirements, and workplace design changes.

Actionable Insights

For businesses, generative AI delivers unthinkable opportunities. Here are a few ways unstructured data can be used to collect actionable insights which drive behaviors, reduce operational risk, and create operational efficiencies:

• Data Mining for Leaders and Operators Behavior: Utilize text mining algorithms on safety observations submitted by workers, close out rates, and incidents and injuries to uncover hidden patterns, trends, and preferences. This can inform/identify work areas that require physical site inspections and operational leaders who require behavioral coaching and support.
• Automated Toolbox/Daily Briefing Generation: Employ generative AI models to create work zone/site-specific safety briefings, based on observations, near misses, audit results, plant inductions, and risk register. This will create bespoke and extremely relevant safety talking points, to drive engagement and relevant conversations.
• Predictive Analytics for Market Trends: Analyze unstructured data from incident and injury reports, and unactioned observations/hazard reports to predict incidents.
• Enhanced Risk Management: Implement risk management analysis on unstructured data like toolbox talks, hazard reports, safety committee meetings, risk registers, SWMS reviews, and training compliance and completion, to help with proactive risk assessment.
• Employee Safety Sentiment and Experience: Use AI to analyze how the workforce feels about the level of safety and the safety culture at the workplace through induction feedback, training feedback, toolbox talks, employee surveys, and hazard observations.
• Operational Efficiency: Apply machine learning models to analyze unstructured operational data (like logs, sensor outputs) for predictive maintenance, reducing downtime, using unsafe equipment, and improving efficiency.
• Procedural Breaches: Apply machine learning models to analyze unstructured operational data (like logs, sensor outputs) for insights on work areas with procedural breaches, near misses, and incidents.

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