How to use AI to promote a proactive safety culture

We understand building a proactive safety culture isn’t easy, learn how AI can help.

Strengthening Safety Practices with AI Solutions

Building a proactive safety culture involves learning from near misses and potential hazards before accidents occur. 

Organizations face challenges in encouraging accurate near-miss reporting and handling safety data. AI safety tools offer solutions that improve incident prevention, hazard identification, and proactive risk management.

Seeking Near Misses and Hazards Proactively

It is said that we learn most from our mistakes. Fortunately, most workplaces do not have many fatalities or serious workplace injuries to learn from. A lot of workplaces can operate for weeks without even a minor reportable accident. 

As a result, organizations wanting to improve workplace safety measures don’t wait for accidents. They proactively seek out near misses and hazards, using AI safety solutions to involve the workforce in a learning process.

Overcoming Challenges in Near-Miss Reporting

Near-miss reporting systems are fraught with difficulties, for example:

• How do you get people to report human error? 
• How representative are the reports you get? 
• How do you get middle managers and supervisors to ‘buy-in’ to the culture that when a worker reports their own mistakes, they are dealt with fairly rather than punitively? 
• Having received the reports, how does an EHS manager find the resources to make sense of them all?

AI can provide some of the answers through incident reporting and data analysis.

Promoting reporting with AI systems

Your workers are your eyes and ears on the ground for reporting near misses. Forward-thinking organizations have made incident and hazard reporting easier using mobile reporting systems. 

These allow people to take a photograph on a mobile device, add a few details, and send it to a manager for review. 

• Improving Reporting with Mobile Systems

AI can make such workplace safety technology systems even easier to use and more effective by recognising machinery, vehicles, or tools in a photograph and looking for patterns of problems amongst large numbers of reports.

• Employing AI Vision in Hazard Detection

Now imagine that every CCTV was an AI camera, with an extra pair of ‘digital’ eyes. These eyes never get tired or have to take a meal break. They are consistently reporting every time someone walked in a vehicle zone or an obstacle was left in a walkway. 

• Data Collection for Safety Improvement

Artificial intelligence vision enables CCTV cameras to detect defined types of hazardous situations or behaviors and report each and every one. The data collected will allow you to see where problems are occurring and set about trying to fix them – before they lead to injury.

Promoting Fair and Safe Work Practices

Once you have the data to show how often hazardous behaviors or situations occur, you have an opportunity to show the workforce that the organization has a fair and just culture. 

Identifying the Underlying Causes of Unsafe Behaviors

The aim is not to use AI reporting to identify who is ‘cutting corners’ or ‘breaking the rules.’ Very few workers want to get hurt or cause harm to anyone else, so if behaviors are not as imagined in your written safety procedures, you need to find the underlying causes. 

Recognising Safety Challenges in the Workplace

Are workers taking shortcuts across vehicle routes to save time because an extra job has been added to the schedule this week to meet customer demands? Has the recent recruitment drive led to new employees being unsure when and where to wear PPE? 

Have new procedures reduced the time available for housekeeping, resulting in more trip hazards? Find out why the hazards are occurring, and involve the workforce in creating a positive health and safety culture where people can naturally do the right thing. 

Adopting AI for Proactive Safety Monitoring

When the only data you track is accidents, it is difficult in the short term to show any benefits from safety initiatives. Near-miss reporting can be influenced by factors other than the underlying safety. This is why a proactive approach helps organizations make safety measures that promote a culture of safety.

AI Vision for Better Tracking

When you track near misses using AI vision, you have more reliable data to identify when things are starting to slip – and when investment in safety monitoring is paying off. 

The Value of Safety Training

You can see from the data that non-compliance with PPE wearing increased when new workers were recruited and reduced again once a programme of training and toolbox talks were introduced. 

You can show the value of including information about PPE earlier in the process, perhaps with supervisors given time to show recruits what they need on their first day rather than relying on colleagues. 

Transforming Safety Culture with AI

AI won’t replace the need to provide workers with a way to report safety issues in the workplace, but it will provide better data about the significance of a problem, enhancing safety management. 

Is the omission of a hard hat a one-off, or is it common? Did someone walk across a vehicle path once this week, or was it happening several times a day? 

Instead of spending hours wading through near-miss reports, the AI does a lot of the hard work for you, identifying patterns and trends. Integrating AI algorithms can provide insights into questions we didn’t even realize we should ask. 

From Reports to Safety Engagement

For the EHS manager, this could result in less time spent on reports, and more time to talk with people about safety and health. 

To learn more about how Protex AI is using their workplace safety software to enhance safety systems and detect risk before an accident can occur, encouraging businesses to embrace a proactive safety culture, chat with one of our product experts here 👈🏼

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