015: The Role of Artificial Intelligence in Safety Management

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Artificial Intelligence (AI) is reshaping safety management across industries, particularly in process safety, where rapid access to accurate information can be life-saving.

Traditional approaches to safety have relied heavily on manual data gathering and analysis, which can be time-intensive and subject to human error.

By contrast, AI can bring efficiency, real-time insights, and the potential to enhance safety significantly.

AI as a Co-Pilot in Safety Workshops

Early in my career as a graduate safety engineer, I worked as a scribe for HAZID (Hazard Identification), HAZOP (Hazard and Operability Study), and ISD (Inherently Safer Design) workshops.

These sessions were foundational in teaching me the craft of safety management, but they could often extend for long hours as teams worked through complex scenarios. I can easily see how an AI Co-pilot in these workshops could offer significant benefits. AI could assist by surfacing patterns that might go unnoticed by the human team, helping to spot early warning signs or recurring risks.

However, this technology wouldn’t replace professional judgement; it would complement it, allowing safety experts to focus on interpreting insights rather than finding them. An AI assistant could enable the team to concentrate on high-impact decisions, making the process faster and more insightful.

Proactive Hazard Detection and Predictive Maintenance in Reliability Engineering

I also worked in reliability engineering, where I witnessed first-hand the importance of predictive maintenance. For example, AI now has the capacity to analyse vibration signatures in turbines, identifying patterns that indicate impending failure.

In the past, recognising these subtle signals was painstaking and heavily reliant on specialist skills. AI, however, can detect these early, allowing parts to be ordered in advance and downtime to be minimised.

This approach not only improves safety but also enhances operational efficiency by reducing unscheduled outages and maintenance costs. In reliability engineering, the use of AI offers a powerful method to bridge the gap between prevention and response, supporting safer and more effective asset management.

Leveraging Historical Data for Better Safety Insights

AI also empowers safety managers to make more informed decisions by providing access to a repository of historical incidents and near-misses from other organisations and sectors.

Previously, sourcing information on past events was challenging and often siloed within individual companies. Now, with AI tools, safety managers can leverage vast databases to learn from others’ experiences and prevent similar incidents from occurring within their own operations.

This collective intelligence approach ensures that process safety standards continuously evolve and adapt to new insights, fostering a safer work environment industry-wide.

Enhancing Safety Training Through AI Simulations

Furthermore, AI can enhance safety training programmes. Through natural language processing (NLP), AI systems can generate incident simulations and virtual training sessions tailored to specific safety protocols.

For instance, workers can experience a simulation of a chemical spill or gas leak and learn the appropriate responses. This immersion creates a more realistic understanding of potential hazards, enabling workers to react effectively in real-life situations.

Challenges and Considerations

While the benefits of AI in safety management are clear, it is essential to address challenges around data quality and ethical considerations.

AI systems are only as reliable as the data fed into them. Inaccurate or incomplete data can lead to flawed predictions, which may compromise safety.

Therefore, ensuring data accuracy and maintaining transparency in AI algorithms is crucial to build trust in AI-driven safety management solutions.

Conclusion: A Safer Future with AI

The use of AI in safety management, particularly within process safety, offers unparalleled opportunities to enhance operational safety and efficiency.

My experiences as a safety engineer underscore the potential for AI to act as a trusted partner in both workshops and the field, assisting teams in making smarter, faster decisions.

By sourcing information on past events rapidly, predicting maintenance needs, and identifying emerging risks, AI enables safety teams to shift from reactive to proactive approaches.

As industries continue to integrate AI into their safety strategies, the future holds promise for a safer, smarter, and more resilient work environment.

For those in leadership positions, now is the time to explore how AI can be harnessed to strengthen safety culture and outcomes in their organisations. There are some brilliant organisations looking into this, and I am sure it will only be time until it is playing an active role in safety management.

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