Reducing Workplace Incidents With AI-Powered Workplace Safety Technology

Why Workplace Accidents Are So Expensive

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Accidents are expensive. They damage morale, reduce productivity, and can result in legal action. Most organizations try to prevent major accidents by learning from minor ones and near misses. 

But getting people to report events where there is little or no actual harm can be difficult. Even when they do, sorting through the reports to identify improvements in occupational health and safety (OHS) management is time-consuming. 

Can AI safety technology make this process easier and reduce incidents in the workplace? The health and safety team at Marks & Spencer, a well-known British retailer, believes it can. 

In this article, we’ll explore how AI can support workplace safety and see whether it has helped M&S reduce unsafe behaviors.

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Identifying AI-Powered Workplace Safety Solutions

AI-powered workplace safety solutions, such as computer vision and deep learning systems, are changing how organizations monitor and mitigate risks. 

These technologies enhance safety management by identifying hazards, preventing incidents, and improving overall safety performance.

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Safety Technology Terms You Should Know

To better understand how these technologies improve workplace safety, here are a few key terms and common safety concepts involved.

Artificial Intelligence (AI) 

A technology that imitates human behavior and decision-making. Deep learning techniques enable AI systems to improve their performance over time and handle tasks beyond their original programming.

Computer Vision (CV) 

AI that processes visual inputs like CCTV feeds to identify objects, movements, and interactions in an image. PPE detection using computer vision can detect any physical hazards and hazardous behaviors in the workplace.

Accident 

An unplanned event that results in injury, illness, or property damage. Accidents highlight failures in safety systems and are often preventable with the right controls and monitoring.

Incident 

An event or set of circumstances where harm could have occurred, but didn’t. Often divided into:

• Near Misses 

A specific event, such as an item falling from a shelf without injuring anyone. Identifying and analyzing near misses is critical for accident prevention and reducing potential safety issues in the workplace.

• Unsafe Conditions 

A situation where harm could occur, such as an overloaded shelf. If left unresolved, these can lead to serious incidents. Addressing them early is key to proactive safety measures.

How Small Safety Reports Make a Big Difference

You might be familiar with the Heinrich triangle (Figure 2). It suggests that for every major injury, there are many more minor injuries, and even more incidents where no injury occurs at all. If you investigate the no-injury events, it reflects what workplace safety statistics show: proactive reporting helps lower long-term risk.

Why Don’t People Report Near Misses and Unsafe Conditions?

People often overlook hazards because they don’t see them as worth mentioning. But improving safety means paying attention to the smaller signs before they lead to something worse.

Over time, workers can become so familiar with risks that they stop seeing them. What once felt unsafe can start to seem normal. Some also avoid reporting out of fear they’ll be blamed, even if no one was hurt.

Others don’t see the point if reporting feels time-consuming or leads to no action. When the system seems ineffective, people disengage.

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Overcoming Barriers to Safety Reporting with AI

Sometimes, people don’t recognize unsafe conditions. They get used to overloaded shelves, clutter near fire exits, and even the slightly slow braking on a forklift truck, and don’t see these as things worth reporting. 

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Cultural and Managerial Influence on Reporting

Some managers unintentionally reinforce unsafe behaviors. For example, if one worker skips safety goggles to finish faster and gets praised, while another follows the rules and is criticized for being slow, it sends the wrong message. In that environment, who is going to report when goggles aren’t worn?

Without a strong safety culture, even minor workplace injuries go unreported. A papercut in an office, a prick from a tagging gun in a shop, or an abrasion from a tool in a workshop might happen so often that no one thinks to report them. 

Instead of reporting unsafe conditions, workers often find quick fixes for missing tools or broken equipment.

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Improving Incident Reporting with AI Safety Tools

Even if people want to report unsafe conditions or near misses, reporting systems can be difficult to access or time-consuming. Figure 3 shows common reasons why incidents often go unreported. 

Some organizations work hard to increase reporting by workers, and then realize they don’t have the resources to deal with all the reports coming in. Without a clear strategy for reviewing reports, taking action, and giving feedback, people will stop reporting.

It’s a lot of work to review each incident report. Safety teams must determine whether the issue is isolated or part of a recurring pattern. They also need to identify if it is linked to behavior or equipment, and whether other incidents need to be considered at the same time.

So, how can we get smarter at dealing with this?

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Automating Safety Analysis with AI-Powered Monitoring

Figure 2 shows how investigating no-injury events provides more opportunities to spot ways to prevent accidents. Adding another layer below that in the triangle would reveal even more opportunities to learn. 

But without technology, there will be even more admin for an overworked investigation team.

AI tools help streamline this process with real-time alerts and pattern recognition, allowing teams to enhance workplace safety. It identifies potential risks through algorithms that people might miss. In other words, technology can provide the extra layer, but with less effort.

Figure 4 illustrates this idea. It replaces Heinrich’s no-injury events with unsafe conditions and near-miss reporting, then adds a broader, AI-powered layer using tools like computer vision.

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AI Systems for Near Miss and Unsafe Condition Detection

AI technology can now detect unsafe conditions and near misses automatically. It spots the unsafe condition, and long before anyone fills in an incident report form. Examples of this are shown in Table 1.

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Wearable Technology 

Wearable devices like smartwatches or PPE-integrated sensors can track a worker’s exposure to risk as they move through different areas of a workplace. While useful, wearable technology comes with several barriers.

People must remember to wear it and keep it switched on. Workers sometimes switch them off due to a vibrating or audible alert because of false alarms or overly sensitive alerts. 

Simple proximity sensors, like RFID tags attached to a high-vis jacket, are easy to use. But some of the technology for assessing posture requires sensors directly on the skin to measure muscle activity. Despite promises of anonymity, people have concerns about privacy with wearable devices.

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Computer Vision as an Alternative to Wearables

Computer vision (CV) provides an alternative to some wearable or location-based devices. It is less personal than wearable technology, and sees people and machinery only as objects. 

It can detect the following:

• When two objects are too close to each other (e.g., a person is too near a vehicle)
• When an object is moving too fast (e.g., a speeding vehicle) or in the wrong direction (in a one-way system)
• When essential items like high-vis jackets are missing
• When an object is the wrong shape (such as when someone’s posture could cause an injury).

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Smarter Data, Safer Decisions

Unlike wearables, CV doesn’t rely on people to charge it, wear it, and leave it switched on. It runs continuously in the background. The data it collects is also easier to anonymize.

EHS analytics software can review data from sensors, computer vision, and other workplace safety monitoring systems to identify trends and patterns in safety behavior. Critical information is summarized through graphs and tables to improve understanding of workplace risk.

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Critical Safety Detection with AI - Where to Begin?

Choosing the right AI safety technologies can feel overwhelming. The following steps will help you to get the best return on your investment in technology:

1. Identify Hazards with AI When Controls Are Ineffective

Start with your risk assessments. Look for safety measures that rely too much on memory or personal judgment. For example, if a control in a risk assessment states “the worker will not overload the shelf,” how is that achieved? 

If by training, do you have evidence that people apply their learning once they leave the training room? Use audits, accident and incident reports, inspection records, and safety observations to help you identify target hazards.

2. Pinpoint High-Risk Tasks and Weak Spots

Review your safe operating procedures and method statements. These describe how tasks need to be done, who can do it, and what tools and equipment are needed. Within a procedure, there might be some steps that you know work well, and others that you are concerned about. 

For example, you notice that staff tend to wear their safety boots all day, but they often forget their hearing protection in the plant room. Or certain shelving areas might safely hold standard parcels, but others can contain awkward and heavy items that don’t stack well.

3. Set Clear, Achievable Safety Goals for AI Implementation

Create a shortlist of realistic goals and involve workers in the process. Their input can lead to practical, low-tech solutions. For example, providing a trolley that is easier to use might help them to meet a goal without the need for high-tech solutions.

Involving people will also help to create a culture where workers believe the technology is there to serve them, not to police them. Your list might look like Table 2.

4. Determine the Best Technology to Achieve the Goal

It’s better to focus on a few key goals instead of trying to do everything at once. You need to balance the risk and benefit of achieving a goal with the cost of managing it with time, effort, and money. 

For example, monitoring vehicle speed might reduce the risk more than tracking whether vehicles are in the right zone. Using CV to check that people aren’t over-reaching or posture issues is also more efficient in workplace environments that demand high safety for warehouse workers.

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5. Run a Pilot First

Test the new technology in a single area before rolling it out more widely. Keep the worker representatives involved, as they will help you to see whether anything needs to be changed and adjust the approach as needed.

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6. ‍Keep it Going

Giving feedback at the team level reinforces a culture where people support each other to do things safely. For example, let them know that they are wearing PPE correctly 70% of the time, and you would like them to get a higher score next month. 

This builds a team culture where safety becomes a shared priority. Once a team achieves its goal for a whole month, continue to encourage and reinforce the behaviours. Make sure that doing the right thing continues to be the best option.

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Sustaining Long-Term Safety Improvements with AI

Imagine a workplace where everyone is able and willing to do every task safely. They create no unsafe conditions, no unsafe behaviors. Would that mean it's time to switch off the CV and any other technological means of monitoring safety?

Improving safety culture in the workplace is challenging because people want to take the safest option, not the quickest option. It is a lot easier to damage that culture. 

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Reinforcing a Positive Safety Culture

A newly promoted supervisor wants to beat production targets, or a new hire brings ideas from their previous company. Without steady reinforcement, people will revert to shortcuts.

Reducing accidents is not just about people doing the right thing because they know they’re being watched. It’s about a culture where people do the right thing because it creates a safer work environment and is the most rewarding way to work. 

Computer vision can help you identify how to support people to work safely. It also provides the information you need to reinforce and celebrate safe behaviors.

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How AI-Powered Safety Reduced Incidents at Marks & Spencer

Did the M&S investment in AI pay off? Yes, and the results were quick. The safety team identified specific problem areas and targeted safety conversations in those areas. In one location, there was a 40% reduction in unsafe events in just one week. 

After three months, the number of unsafe events had dropped to just 20% of the initial measurement. There is still room for improvement, as new workers and agency workers adjust to safety expectations. 

But overall, employees no longer see AI as surveillance, but as a means of helping the safety team to keep them safe. 

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Get in Touch

Protex offers advanced AI-driven safety solutions designed to reduce incidents and can integrate with existing CCTV infrastructure. 

To find out how our technology can address your safety challenges, watch our demo to learn more.

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