Every year, hundreds of workers across high-risk industries like construction, manufacturing, oil & gas and mining head to work and never imagine that their day will end in tragedy. Yet, global data suggests otherwise. According to the International Labour Organization (ILO), over 2.3 million people die annually from work-related accidents or diseases, while over 300 million suffer non-fatal injuries.

Among these, Serious Injuries and Fatalities (SIFs) continue to occur at alarming rates — even in industries that have otherwise improved their safety statistics.

This paradox has puzzled safety leaders globally: Why are total recordable incident rates (TRIR) going down, while SIFs remain steady or even rise? The answer lies in how we measure and mitigate risk — and increasingly, how we leverage technology like Artificial Intelligence (AI) to lower serious injuries rate.

Serious Injuries and Fatalities, commonly known as SIFs, refer to life-altering or life-ending incidents that occur in the workplace. While definitions can vary by industry, most classify an SIF as any event that results in:

For instance, SIFs at work can arise in case of any of the following situations:

• A construction worker falling from scaffolding without fall protection.

• A manufacturing operator trapped by a machine without proper lockout-tagout (LOTO) procedures.

• A logistics worker struck by a reversing forklift in a congested loading bay.

• A miner exposed to toxic gases due to ventilation failure.

These aren’t isolated “accidents” — they’re often predictable and preventable.

Despite vast improvements in Occupational Health and Safety (OHS) systems, traditional safety approaches rely heavily on lagging indicators, that is, data collected after an incident occurs. Here’s where the gaps often appear:

In high-risk sectors these limitations create blind spots that traditional audits or manual monitoring simply can’t eliminate.

The rise of AI and smart analytics has given EHS leaders a new toolkit to combat SIFs. It is the one built on real-time visibility, predictive intelligence, and proactive intervention.

By integrating advanced AI tools such as IoT sensors, wearables, computer vision, and digital safety systems, organizations can detect risks before they escalate into life-altering incidents.

Here’s how:

1. Anomaly & Behavior Detection through Computer Vision

Computer vision technology is transforming how EHS teams detect unsafe actions and environmental anomalies. The site cameras equipped with AI can continuously monitor to identify behaviors or conditions that deviate from standard safety practices — such as workers entering restricted zones, forklifts moving at unsafe speeds, or missing PPE.

Unlike manual observation, which is prone to fatigue and human error, vision-based surveillance provides constant vigilance. When a breach is detected, the system can instantly alert supervisors, trigger audible alarms, or even halt machinery through integration with PLC/DCS systems.

This automated feedback loop drastically shortens response times, turning real-time video data into proactive safety control.

2. Preventing Equipment-Induced SIFs with Predictive Maintenance

Equipment failures are well-known to often trigger serious injuries — from hydraulic bursts to electrical fires. AI-driven predictive maintenance minimizes such risks by monitoring parameters like vibration, temperature, and energy consumption to identify signs of mechanical stress or wear.

When an anomaly is detected — such as unusual vibration in a rotating component — AI systems can alert maintenance teams and schedule inspections or safe shutdowns before the equipment fails.

This predictive approach not only avoids unplanned downtime but also prevents accidents caused by sudden breakdowns. In manufacturing plants, for instance, early detection of bearing wear or overheating can avert crush injuries or explosions that might otherwise contribute to SIF statistics.

3. IoT & Wearables for Continuous Monitoring of Worker Safety

Connected safety devices powered by the Internet of Things (IoT) have become a crucial part of modern workplace safety frameworks. From smart helmets, vests, to environmental sensors, they continuously capture data on worker movement, location, and exposure levels.

For example, smart watches monitors the health vitals of workers throughout the day. They alert the EHS teams when abnormal heart rates or temperature spikes occur. Similarly, sensors embedded in PPE can trigger real-time alerts when a worker is exposed to excessive heat, toxic gases, or fatigue levels that indicate overexertion.

In industries such as oil & gas or mining, where confined spaces, poor visibility, and hazardous materials create multiple layers of risk, such continuous digital supervision can drastically reduce the probability of serious injuries or fatalities.

4. Edge Processing for Real-Time Risk Detection

In critical workplace safety operations, milliseconds matter. While cloud-based systems might experience network latency, edge AI overcomes this challenge by processing data on-prem directly at the source, near the machine or sensor itself.

Imagine a worker stepping too close to a conveyor belt. An edge device mounted nearby instantly detects the unsafe movement, processes the signal locally, and communicates with the system to initiate a hard stop.

This happens within milliseconds, preventing injury before it occurs. Such localized, high-speed decision-making ensures that even in the event of poor connectivity, safety responses are never delayed.

5. Root Cause Analysis with AI

Beyond immediate prevention, AI in preventing serious injuries adds immense value in uncovering why incidents happen. Traditional root cause analysis relies heavily on manual inspection and human interpretation, which can miss complex correlations.

If multiple near misses occur during a night shift, for example, AI video analytics might correlate these events with increased worker fatigue levels at night or insufficient lighting. If an increase in forklift collisions is detected in a particular warehouse zone, the system could flag design flaws in that area.

These insights allow safety teams to focus their interventions on high-risk areas backed by evidence rather than assumption and predict potential serious injury or fatality (pSIF) at high-risk industries.

6. AI-Powered Permit to Work (PTW) Systems

PTW systems ensure that high-risk tasks are executed safely — but manual verification processes can miss crucial details. Digital PTW systems enhanced with AI bring automation and continuous oversight to this process.

AI algorithms can validate permits in real time, ensuring that all necessary approvals are in place before a task begins. If unsafe conditions are detected — such as the sudden presence of flammable materials during hot work — the AI system can suspend or revoke permits automatically. This dynamic oversight ensures that safety protocols are not just documented but actively enforced in real time.

7. Data Integration for Holistic Safety Intelligence

Perhaps the most transformative aspect of AI in reducing SIFs lies in its ability to integrate diverse data sources. Traditionally, safety data, operational data, and maintenance records exist separately. AI platforms unify these streams into a single safety intelligence layer.

This integration enables predictive modelling that identifies high-risk situations before they escalate. For example, combining AI-driven video analytics with temperature or gas readings in a chemical plant could reveal that a vessel is overheating, prompting early intervention long before the situation becomes dangerous.

Similarly, cross-departmental insights can link operational inefficiencies, such as equipment overuse, to rising safety incidents. The result is a real-time, 360-degree view of workplace safety that empowers faster, more informed decisions — a crucial step toward lower serious injuries rate across industries like construction, manufacturing, logistics, and mining.

As industries grow more complex and interconnected, traditional safety approaches alone can no longer meet the pace of modern risk.

AI’s integration into workplace safety systems has redefined what’s possible in preventing Serious Injuries and Fatalities (SIFs). Instead of reacting after an event, AI helps organizations anticipate it — analyzing patterns, detecting anomalies, and predicting potential hazards long before they escalate. This transition from hindsight to foresight is what marks the next era of safety innovation.

For EHS leaders, the opportunity lies not just in adopting AI tools but in creating a proactive safety culture — one where technology augments human judgment, enables smarter decisions, and ensures every worker returns home safe. As AI continues to evolve, its promise extends beyond mere efficiency; it represents a fundamental shift toward workplaces that are not only productive but truly intelligent, resilient, and safe by design.

1. How does AI actually detect or prevent a serious injury or fatality on-site?

AI continuously monitors live data from cameras and sensors. When it detects an unsafe act — like a worker entering a restricted zone, or a machine overheating — it triggers automatic alerts or even halts operations. Essentially, it shifts safety from reactive to proactive by intervening before an accident escalates.

2. What types of incidents are categorized under SIFs?

SIFs (Serious Injuries and Fatalities) typically include incidents like:

• Falls from heights or scaffolds

• Machinery entanglement or crush injuries

• High-voltage electrocution

• Exposure to toxic gases or chemicals

• Confined space suffocation

Even one SIF can have a ripple effect — lost life, operational downtime, and lasting reputational damage. That’s why identifying precursors to these events is key.

3. Can AI-driven safety systems trigger automated responses during SIFs?

Yes. Integrated systems like viAct can automatically execute machine hard stops, activate stack lights, or send SMS/Email alerts to supervisors on their mobile devices. This reduces response times from minutes to seconds — a difference that often decides whether an incident remains an injury or becomes a fatality.

4. Can AI predict which areas are most likely to record a SIF?

Yes. Predictive algorithms in viAct analyze:

• Historical incident data

• Shift duration and worker fatigue levels

• Equipment health and environmental readings

The output is a “risk heatmap” that helps allocate supervision and preventive maintenance proactively.

5. How difficult is it for EHS teams to adapt AI for SIF prevention?

Most systems like viAct are designed with intuitive dashboards that show live alerts, incident trends, and heat maps. Training typically takes 1–2 days, and teams adapt quickly once they see how it streamlines reporting.

“We thought AI would complicate things — but it simplified our monitoring. The dashboard highlights the highest-risk zones instantly, no data crunching needed.” — EHS Manager, Automotive Plant, Saudi Arabia.

Still Struggling to Lower Serious Injury Rates (SIFs) at Work?

Explore viAct Now!

Read More:

• AI as Early Warning System for Serious Injuries and Fatalities (SIFs)

• Introducing Safety Score: Where Does Your Business Stand?

• How to Measure AI Impact in Workplace Safety: Metrics That Matter in 2025