How to Use AI for Workplace Safety: Tools, Trends, and Industry Use Cases

Occupational health and safety, aka OHS, is never to be taken for granted, no matter the type of workplace. But as industries expand, production lines automate, and operations get smarter and faster, traditional OHS practices become stale and lack efficiency.

This is where AI-powered workplace safety may help fill the gaps, synchronize, and perfect emergency monitoring, incident alerting, and employee safety management at all levels. How exactly? Let’s dive in and find out.

The Value of AI in Today’s Occupational Health and Safety

A common practice in any organization or production establishment is to react to safety issues or concerns. However, the advent of smart technology like Big Data analysis and AI prompted a huge shift to proactive approaches. What is the difference?

• Reactive approach: With reactive safety management, an issue gets inspected and addressed only after a certain incident occurs. An incident, event, or workflow issue is usually analyzed post-factum to determine the main causes and prevent recurrence. The employees are educated afterwards, and other required changes are made to rectify the faulty situation;

• Proactive approach: AI comes in turn to things around a bit, and to provide some headroom for managing safety before the related issues even occur. With proactive safety management, workplace conditions can be evaluated more regularly via automated audits. Hardware malfunctions can be picked out early on and serviced beforehand to prevent bigger accidents.

To give you a clearer picture:

Each philosophy has its place in today’s active and (potentially) hazardous work environments and cannot be interchanged. However, at a closer look, we can see some major limitations to traditional safety approaches, like:

• Delayed response: a reaction can take time, and a hazard will remain there until it is duly identified and investigated, which can cause more legal liabilities, downtime, and expenses;

• Human error: hard-to-see details, repetitive near-misses, and subtle risks can easily be overlooked by human specialists swamped with manual tasks and flows of data;

• Data silos: all the data that you gather during safety tracking often comes fragmented and raw, needing to be structured beyond manual capacities for a comprehensive analysis.

In turn, AI and other related technology can be leveraged to, if not replace, enhance, and expand these practices. In particular, with the help of:

• Real-time data monitoring: AI systems can continuously assess work environments, picking out risks and errors right as they emerge or even beforehand;

• In-depth BI and predictive analytics: By analyzing historical and current data, AI can forecast potential hazards and offer fitting preventive measures;

• Tech integrations: AI can be embedded into existing systems, such as surveillance cameras and wearable devices, synchronizing them and boosting their capabilities.

While there are pros and cons of AI in the workplace, it allows for better centralization, control, and visibility in managing safety risks for both employees and equipment.

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AI Technologies Used for Workplace Safety

Today’s advanced AI models can do a lot to improve workplace safety, but how exactly do they do it, and through what methods? There are numerous technologies and sub-features that can be synchronized and leveraged. For AI workplace safety, these are the following.

Computer vision

Computer vision features enable machines to interpret visual data, which makes it simple to initiate and run advanced tasks for hardware and employee safety, like:

• PPE compliance monitoring: AI can track whether workers are wearing required protective gear and comply with the personal protective equipment regulations;

• Behavior analysis: A CV-enabled AI model can track worker movements and alert to unsafe practices;

• Hazard detection: AI can also be tuned to “see” potential dangers in the environment, such as spills or equipment malfunctions.

Natural language processing

NLP algorithms help AI understand human language, process audio data, and gather feedback, making it possible to:

• Incident report analysis: An AI model can extract insights from textual reports to pick and track common or recurring hazards;

• Feedback interpretation: AI can also gather live employee feedback for data collection and analysis of safety concerns.

Wearable devices

All sorts of wearables, from watches and bracelets, to full-on vests and helmets, to sensors integrated into safety gear, can be used to gather OSH data in real time and either react promptly or prevent injuries via:

• Vital signs monitoring: Integrated sensors or devices can track vitals like heart rate, body temperature, and other indicators to detect early signs of fatigue or heat stress;

• Motion detection: Leveraging both CV and other sensitive features, the sensors can analyze unsafe movements or postures, alerting workers to correct their actions;

• Fall detection: Accelerometers can help detect falls and automatically notify emergency services.

Internet of Things

The Internet of Things allows coupling and synchronizing all the devices and sensors in a single network. You can use simple UI features to manage the collection and transmission of data on environmental and equipment conditions via:

• Environmental monitoring: AI-integrated sensors detect hazardous gases, temperature extremes, and noise levels, and can trigger respective alerts when safety thresholds are exceeded;

• Equipment status tracking: They can also monitor machinery performance individually, delivering the hardware’s live status, predicting failures, and scheduling maintenance;

• Worker location tracking: Going further, real-time location systems or RTLS help monitor employee movements even more scrupulously to prevent unauthorized access to dangerous areas.

Virtual and Augmented Reality

VR and AR technologies are being actively employed (read below) for lifelike training experiences across industries and specialties, allowing workers to practice how they would respond to hazardous scenarios without going far:

• Emergency response training: AI/VR simulations of emergencies like fires or chemical spills enable safe practice of evacuation and other emergency procedures;

• Equipment operation: Workers can dive into the guts of machinery they are about to operate, learn its ins and outs, and operate it virtually at first to avoid physical risks.

Drones and robotics

Drones and various specialized robots can be configured and integrated to perform live inspections and tasks in hazardous or hard-to-reach areas, helping minimize human exposure to danger via:

• Visual and thermographic surveys: Drones and robots can check operating surface conditions and recognize heat anomalies in equipment;

• Confined space inspections: They can reach and inspect areas like the insides of tanks or boilers without endangering personnel;

• Underwater inspections: Specialized underwater drones can also check and help visualize submerged structures.

Predictive analytics

Enabled by machine learning, Big Data analysis, AI, and manual techniques, an AI model can be taught to examine data trends and provide business-turning insights via:

• Accident forecasting: An ML-, AI-equipped system can pick out patterns that precede incidents;

• Maintenance optimization: By helping predict equipment failures before they occur, AI also helps redefine maintenance.

One thing connects with the other, eventually allowing for a smart structure of an AI-enabled workflow that can benefit many, if not all, essential industries of today.

How Industries Are Using AI for Health and Safety Management

There’s lots of technological potential and range to smart tech in OHS management, but how is AI used in the workplace exactly? Which technologies and practices do companies and productions use in different niches in the context of safety and protection? Let’s take a look.

Manufacturing

AI-enabled computer vision systems can monitor the space between workers and operating machines, and alert them if they are too close, preventing near-miss incidents and injuries.

This has already been leveraged up to a full-on service where entire manufacturing structures are integrated with AI systems and devices for transparency, control, and overall better OHS.

Construction

AI can outline hazardous zones and send reminders about compliance with safety gear requirements. Predictive analytics can assess the risk of falls by analyzing past incidents and current site conditions.

Shawmut Design and Construction employs AI to check these risks, monitor compliance across its sites, and keep some 30,000 workers protected.

Logistics and warehousing

AI can monitor operator behavior and optimize routes for forklifts to prevent collisions and overloading. Tuned smart algorithms can alert to high-traffic areas and help adjust operations for better safety.

For instance, Amazon uses specialized robotic tech vests to make sure that their warehouse robots efficiently detect human workers and avoid collisions with them.

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Oil and gas

With the help of aforementioned sensors, IoT, and other devices, integrating artificial intelligence in the workplace allows keeping equipment in check, getting early signs of leaks or pressure anomalies, and doing immediate shutdowns to prevent disasters.

For a real-world instance, British Petroleum Corp. uses drones with thermal imaging to detect gas leaks - an innovation that has helped cut down their on-site safety breaches by 50%.

Healthcare and medicine

An AI system can track staff fatigue levels and adherence to hygiene protocols, reducing the risk of errors and infections in the long run. For instance, Northeast Georgia Health System equipped 10,000 staff members with location-tracking badges to boost their safety and communication.

Agriculture

AI can predict weather-related risks and monitor the use of chemicals, suggesting safer farming practices. AI-driven weather prediction tools, like Aardvark Weather, provide quite accurate forecasts and help farmers plan their activities safely.

Aviation and transportation

AI can autonomously assess vehicle conditions and even track pilot/driver alertness to issue respective warnings and proactively prevent accidents. Companies like Optalert have developed fatigue monitoring systems that alert drivers when signs of drowsiness are detected for ultimate road safety.

Pros and Cons of AI in Workplace Safety

With a range of useful applications, potential, and promises, it all comes down to the ultimate choice of AI-powered OHS solutions for your case. To make a long-term, efficient decision, you should weigh out both the advantages and shortcomings of integrating AI for OHS.

The main benefits of AI in the workplace’s OHS practices include:

• Hazard prediction: Rather than purely reacting to the outcomes and occurrences, AI allows us to pre-analyze and predict some of them, helping save tons of costs, reputation, and lives;

• Behavior recognition: Having a good point of reference for the analysis of actual employee behavior enables managers and OHS admins to approach employee monitoring very sensitively, seeing signs of regular fatigue or low-quality execution;

• Data-informed decisions: With all the monitoring, historical, predictive, and other data collected via AI-enabled tools, top managers and specialists responsible for workflow optimization can make individually efficient business decisions.

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On the flip side, the disadvantages of AI in the workplace are:

• Privacy questions: The main challenge of integrating a work environment with AI is ethical: how moral is it to monitor employees at all times? It’s fine if a workplace is a construction site or a production line. For other fields, concerns may arise;

• Data bias: If an AI is trained on a limited or purely structured dataset, it may not perform up to the task, which spawns an additional article of expense for AI model training and optimization:

• Integration issues: Many organizations use their own, good old legacy systems, which may have stood the test of time but are difficult or impossible to integrate with newer solutions, including AI features (which sometimes calls for full refactoring);

• Workforce resistance: A new AI implementation requires proper onboarding for employees. On top of that, fears may arise among workers of being replaced by automation.

Harmonizing AI and Human Oversight

The final choice should be shaped by the specifics of your business or startup, the industry or niche you are operating in, and your particular goals. How does AI affect the workplace in your case? It is for you to find out.

There’s but one thing it all comes down to: you cannot replace all the traditional OHS practices with AI, and vice versa. So your best bet is to marry them. AI is there to help you augment and boost the capabilities of your human specialists, and the same goes for safety.

If you already have an OHS strategy in place, try to see where you can equip it with fitting AI tools and enhance it. This is where you’ll need to consult with a tech provider, but you can already see the range of your possibilities from this article.

Conclusion

The pros of AI in the workforce make it a very attractive tech innovation to consider in all sorts of company structures and organizations. For one thing, all the AI tech allows getting a much tighter control over processes that you could approach only at a distance or intuitively.

If you decide to boost your OHS, or any other range of operations, with personalized AI integrations, turn to qualified specialists at Requestum to consult, make an individual plan, and implement your next AI-powered OHS solution flexibly.