The Complete Guide to AI Warehouse Safety

Improving Warehouse Safety With AI Technology

Warehouses are high-risk environments, and managing the hazards takes a lot of effort and vigilance. This guide explores how artificial intelligence (AI) can help reduce hazards and improve safety procedures.

It’s designed for anyone managing warehouse safety, whether you're dealing with ongoing issues or aiming to improve an existing system. You’ll learn how warehouse safety technology can address occupational safety challenges and compliance requirements.

No matter your current knowledge level, this guide will support your goals. You might have existing safety issues you know you need to tackle, or you might have a good system, but you’re looking for continuous improvement. Either way, this guide can help you make your workplace safer and more efficient.

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What is Artificial Intelligence (AI)?

Artificial Intelligence is the ability of machines to perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, or learning from experience.

It is often misunderstood. It has long been imagined as futuristic robots or powerful machines with human-like abilities. 

Today, AI includes technologies like Chat GPT and similar generative AI models, but its capabilities go far beyond that. This guide focuses on how AI and automation support health and safety in warehousing and supply chain logistics.

How Does AI Work?

Traditional software follows a fixed set of instructions. For example, a database or workflow tool can show late orders or upcoming shipments based on rules written by programmers. It can only do what those rules allow.

AI is different. It can learn from data and create new ways to solve problems within a defined area. This type of AI is called narrow AI. It doesn’t think broadly, but improves its performance in a specific task through experience.

With AI, new rules and instructions can be devised to do new tasks that the programmers never considered. It’s narrow AI, because unlike the AI of fiction, it only works within a specific field. 

Warehouse Safety and AI-Powered Analytics

Data analytics tools are designed to look for patterns and meaning in numbers, which could be collected manually through reports, observations, or sensors. 

Computer vision extends this by analyzing patterns in an image to identify behaviors or situations. In warehouse operations, computer vision can be trained to spot when a forklift truck is in a pedestrian zone or when a pedestrian is in a vehicle zone. 

How AI Learns Safety Tasks

It’s designed for specific safety tasks, not general intelligence. One major advancement in this field is machine learning. Instead of manually programming every rule, the system learns by studying large sets of example images, and it devises its own rules.

For instance, searching for a “forklift truck” online quickly returns a variety of images. These can be used to train AI to recognize forklifts of different shapes, sizes, colors, and angles.

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What is Computer Vision?

Computer vision is a form of artificial intelligence that enables machines to interpret and understand visual information from their surroundings.

Initial attempts at teaching computers to recognize images were based on being able to define those images. For example, a cube has 12 edges, six square faces, and eight corners; a regular pyramid has 8 edges, one square face, four triangular faces, and five corners. 

But it was difficult to write an AI algorithm that could distinguish one shape from another at any angle, and in any lighting condition, or when one object was behind another. It’s even harder to define people, vehicles, racks, and conveyor belts with algorithms.

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Warehouse Safety Challenges and AI Solutions

Workplace fatalities in the UK have declined over the past few decades. In 2023/24, 51 workers lost their lives in construction accidents. 

Some of the most common causes of injury and death across all industries will be familiar to those in charge of warehouse safety. 

In particular, falls from a height, being struck by a moving vehicle, and being struck by a moving object account for 70% of fatal injuries to workers. 

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Warehouse Safety Statistics in the USA

Recent statistics in the USA continue to present serious safety challenges in the warehousing sector. According to data from the U.S. Department of Labor's Occupational Safety and Health Administration (OSHA), the average fatal injury rate across all industries in 2024 was approximately 3.5 deaths per 100,000 workers. 

The warehousing and storage sector experienced a higher rate, with 4.4 deaths per 100,000 workers, indicating that warehousing remains a particularly hazardous industry. For material-moving workers, including packers and drivers in warehouses, the leading cause of fatalities continues to be vehicle-related incidents. 

Other significant causes of injury include contact with objects and equipment, exposure to harmful substances or environments, and slips, trips, and falls. These trends show that persistent safety risks still require focus on improving workplace safety measures.

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Importance of Warehouse Hazard Identification and Risk Management

Reported accidents are often only the tip of the iceberg. Minor accidents and near misses happen every day in many workplaces without being reported. 

Even minor accidents and near misses result in loss of time, productivity, and damage to equipment and stock. And they can be an indicator of more serious problems.

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Proactive AI Safety Measures 

Where dangerous situations are allowed to continue, such as vehicles driving at speed near pedestrians, or unstable racking, the regulator can prosecute even if there hasn’t been an accident. 

Responding to prosecutions is expensive and can damage the reputation of an organization.

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Smarter Risk Management with AI

Your first tool for improving warehouse safety is your risk assessment – AI hasn’t changed that! What systems do your risk assessments have in place to monitor hazard controls? 

Thanks to advancements in technology, monitoring systems that were once too complex or expensive are now more accessible. Many companies are now using warehouse safety solutions to automate risk monitoring and simplify compliance.

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AI-Powered Hazard Monitoring in Warehouses

Artificial intelligence is transforming how warehouses detect and manage safety risks.  With tools like computer vision and smart analytics, safety teams can quickly identify hazards and respond before they lead to workplace accidents. 

Below are seven areas where AI strengthens hazard monitoring:

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1. Slips, Trips, and Falls

The majority of reported injuries arise from slips and trips, many of which could be reduced by improved housekeeping and inventory management. 

• When a delivery arrives at a busy warehouse, it might not be possible to store the delivery immediately. 
• Areas need to be defined for temporary storage, away from walkways. 
• Computer vision in warehouse management monitors high-traffic areas and flags when paths are obstructed or people cut through unsafe zones. 

While detecting slippery surfaces is still developing, AI can already recognize minor falls that often go unreported. 

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2. Inventory Tracking

Blockchain technology is an emerging technology that may improve inventory tracking in the future. Blockchain enables all your warehouse inventory to be tracked more accurately. 

It makes it easier to plan for deliveries and collections, and to be sure of the location of goods within your warehouse.

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3. Racking Safety

Poorly stacked items can shift or fall, especially when a lift truck hits the racking. These incidents may not happen immediately, but can pose risks later. Although you might require drivers to report all collisions, they might forget, or the time delay before they report means that someone has already been injured. 

Computer vision can send you an alert as soon as it detects racking collisions in real time, so that you can review the video surveillance and check the stability of the stored objects.

Some platforms use QR codes to confirm items are shelved correctly based on their weight and size. Future technology, such as the Internet of Things (IoT), might provide shelves that assess their own loading and send you an alert if they think they are overloaded!

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4. Working at Height

The ideal is to eliminate work at height in a warehouse. For example, rotating racking systems (also known as carousels) and vertical conveyors help by bringing items down to waist level, but complete removal of this work is often too costly.

AI can help manage this risk. Computer vision detects unsafe behavior, such as workers overreaching from ladders or climbing into restricted areas. It also spots when someone enters elevated zones that should only be accessed by a forklift.

These alerts help correct risky actions and reinforce safe procedures. One common work at height accident occurs when people try to reach too far from a ladder or other access equipment, rather than descending and moving it. Computer vision in warehouse management can now detect when someone reaches beyond a safe limit.

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5. Forklift and Vehicle Movement

Segregation of vehicles and people will reduce the severity of the outcome when a load falls from a vehicle or topples over inside a vehicle. But if a badly managed load causes a forklift truck (FLT) to overturn, the driver is likely to be harmed. 

AI tools can track forklift activity, check for raised loads, and confirm routes are followed, especially on inclines where tipping is more likely. 

AI integration with existing CCTV can monitor vehicle speed and movement even without built-in software. It also detects when pedestrians enter vehicle-only zones or when high-visibility gear isn’t being used.

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6. Manual Handling and Posture

Manual handling controls should always start with the design of the work environment to eliminate or reduce manual handling. Musculoskeletal injuries are also often overlooked. 

Workers don’t always complain about handling tasks that cause aches and pains. This can be because of a macho culture, where no one wants to admit a perceived weakness. Or it can be from the experience that ‘nothing will change.’ 

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Ergonomic Risk Detection

Wearable solutions to this have been around for a while, but the most accurate ones require the worker to have sticky pads attached to their skin to detect muscle activation. While this can be useful for a limited trial to assess new work methods, it is unlikely to be acceptable to most workers in the long term. 

Computer vision offers a non-intrusive way to monitor posture. It uses regular CCTV footage to identify unsafe lifting or bending. The findings can help you identify where environmental or work design changes are needed to support improved manual handling.

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7. Hazardous Substances and Flammables

Storing hazardous materials safely is essential for both fire prevention and health compliance. Inventory control systems help track quantities and ensure substances are stored in the correct locations.

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Fire Safety Measures

If there is a fire, the fire service needs to know the location of pressurized cylinders and flammables. This data should be available on mobile devices to support quick response. 

When there is an emergency, everyone needs to know what to do quickly. That means practicing with spill kits for any hazardous substances you store or handle, and regular fire drills. 

In between, computer vision provides an extra set of eyes to monitor safety zones and identify non-compliance in real time. AI-enhanced monitoring ensures fire exits stay clear and doors remain closed. It also checks that workers wear the correct PPE when handling chemicals. 

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Developing and Optimizing an AI Safety Model

Imagine you have a system that can detect a forklift truck (FLT). But you don’t want it to create an alert every time it sees an FLT – it needs to know something about the context of use. 

Are there zones it should drive in, and zones it should avoid? Does the direction of movement matter, for example, in a one-way system? 

• Configuring Exclusion Zones

Using the CCTV feed of your workplace, you can ‘draw’ exclusion zones directly onto the image on your screen. Mark areas where vehicle detection is critical and identify pedestrian-only paths. This setup helps AI distinguish between safe and unsafe movement within the warehouse.

• Integrating PPE Detection into AI Protocols

Once basic zone mapping is in place, you can add more advanced rules. People can walk in some locations, for example, but only when wearing high-visibility clothing. If they are not, you want the model to detect this as an anomaly and alert you. 

• Validating and Training AI Safety Systems

Model development works best if you start small, train the model, and check that it is working as you intend before adding further functions. Review flagged incidents to confirm whether they reflect real safety concerns and provide feedback as to which are correct. 

• Optimizing Recognition of High-Visibility Gear

In many warehouses, workers wear different high-vis colors based on their roles. The model might start by recognizing yellow high-vis as acceptable, but not accepting pink high-vis. Over time, you can optimize the model to recognize both.

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Continuous Improvement in AI Warehouse Safety Management

OHS professionals will already be aware of the importance of collecting information about near misses (that is, events in which no injury occurs). If you can see patterns from near misses and use these to enhance safety, you can hope to improve accident prevention measures. 

This approach follows the Heinrich triangle model, which shows that near misses occur far more often than serious accidents. Analyzing these events offers more opportunities to improve safety before harm happens.

Unfortunately, many near misses go unreported. Workers may avoid reporting due to time constraints or fear of blame. AI-powered systems, like computer vision, help fill that gap. They monitor activity in real time, capturing incidents that might otherwise be missed, and strengthen your overall safety management strategy.

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Data Privacy and the Control of AI Systems

One fear that concerns all safety data collection and processing technology, whether based on traditional databases or AI-driven, is what happens with the data. Who can see it? Where might it all go wrong if too much power is given to the technology?

Protecting data involves two things: the security practices of your AI provider and how your organization handles that data. 

Always choose a provider that prioritizes data confidentiality and limits internal access. The data collected should only be used by your organization, not by the vendor or third parties.

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How to Choose a Secure AI Safety Technology Provider

Free AI platforms like ChatGPT or Bard aren’t designed to handle private business data. You’ll often see a warning not to share sensitive information when using tools like these. 

Don’t give it an accident report and ask it to rewrite it. If you do, you are giving someone else the right to use your information in whatever way they see fit.

If you’re buying an AI product to support safety management, you need to know your information and data are only for your organization to use, and that no one, not even someone working for your supplier, can access that information.

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Using Edge Computing for AI Safety Solutions

ProtexAI achieves this using edge computing for its computer vision system. This means that your live CCTV is streamed via a managed Ethernet connection from the CCTV network to an ‘edge’ device. 

This device processes the footage and identifies hazards locally. Video clips are encrypted, and faces are blurred before anything is uploaded to the cloud.

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Securing Warehouse CCTV Networks

Computer vision can’t fix a leaky CCTV system, so check that your existing CCTV arrangements are secure. Keep your CCTV network separate from your company’s main network. Use strong access controls, like login authentication and physical security.

Review and update existing data protection impact assessments (DPIA) for your CCTV system when you add computer vision. If a DPIA isn’t a legal requirement in your location, it’s still good practice to do one, and it can help to get workers engaged in the project.

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5 Best Safety Practices to Implement for AI Adoption

AI workplace safety solutions like Protex AI go beyond traditional safety monitoring. They support real-time incident reporting, proactive risk alerts, and stronger compliance with safety standards.

To use these tools responsibly, organizations should follow five key ethical practices inspired by the US, EU, and UK regulatory frameworks.

1. Be Transparent With Your Workers

Workers need to know where cameras are placed and how footage will be used. Provide this information during induction training and whenever policies change. 

Be open about who has access to the footage and when it might be shared, such as during a regulatory investigation.

When you introduce computer vision to an existing CCTV system through the use of AI cameras, you need to tell people when still images or film clips might be captured and how clips might be reviewed. Sharing examples of the data collected builds trust and transparency.

2. Define a Clear Purpose

Make your intent clear from the start. Are you using AI to monitor traffic flow, identify unsafe behavior, or improve warehouse layout? 

Your aim might be to identify safety concerns with the working environment (such as lighting or layout). Or you might be looking for examples of behavior to identify training or coaching needs. 

If later, you use the technology to compare how long different individuals spend on a task, or as evidence in a disciplinary case, you will be breaching this principle - and the trust of your workforce.

3. Collect and Keep Only What You Need

Information collected should be limited to what is necessary for the task. If the aim is to see how many people are too close to vehicles at a location within a shift, you don’t need the individuals to be identifiable. 

Use features like face-blurring to protect privacy. The EU Act and some state-specific laws in the USA are likely to prohibit facial recognition and other biometric identification systems in public spaces, so check what applies to your workplace.

Set a clear retention policy for video footage. Your organization needs to justify how long video is kept. For example, a month is probably long enough for a crime to be detected. If there is an accident in the workplace, relevant CCTV images can be kept for longer if they might be needed for an investigation.

4. Human Oversight

AI tools help filter footage, but final decisions must remain with people. Safety officers should review flagged clips, assess the situation, and decide on next steps.

AI-supported computer vision narrows down the hours of video that you would otherwise need to review, but the final decision on how to interpret the video and what action to take rests with a human.

The person with that oversight has a responsibility to use it fairly. For example, be careful if assigning labels to film clips – describe what you can see, not what you think. As an example, label a clip as “worker appears to drop a load,” not as “careless worker”.

5. Fairness

In the US AI Bill of Rights, this is called ‘algorithmic discrimination protections’, while the EU Act refers to ‘diversity, non-discrimination and fairness’. Fairness starts with how your AI is trained.

If your model is based on a narrow group, such as mostly male workers, it may miss risks affecting others. Make sure your system is inclusive and reflects the diversity of your workforce.

A transparent process with human oversight helps identify and correct bias. It also gives workers confidence that AI is used to support, not punish.

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Case Study -  AI Safety Software in Warehouses

At Marks & Spencer, the safety team used computer vision to pinpoint high-risk areas and focus safety conversations where they were most needed.

In one location, unsafe events dropped by 40% within the first week. After three months, incidents had fallen to just 20% of the original level. 

While there is still progress to be made, especially as new and temporary workers adapt, existing staff see the AI system as a helpful tool. They view it as a way for the safety team to protect everyone on the floor better. 

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5 Steps To Implement AI in Warehouse Management Systems

So what's next? These steps should help you move your warehouse health and safety management to the next level.

1. Prioritize with Purpose

It’s easy to get excited about new technology, but start with the basics. Review your current risk assessments. Identify what’s covered well and where the gaps are.

If controls in your risk assessments state ‘the worker will not overload the shelf’ or ‘vehicles and pedestrians are segregated’, how are these controls monitored? If training is part of your control measures, is there proof that it’s being applied on the job?

Audits, accident and incident reports, inspection records, and safety observations will help you identify target hazards to prioritize.

2. Define the Standards for Relevant Activities

Once you’ve prioritized the hazards, define the goals for tasks where those hazards occur. 

You might need to refer to documents, such as safe operating procedures and method statements, which describe how tasks need to be done, who can do it, and what tools and equipment are needed. 

Ask people doing the tasks about their understanding of the goals. Your activity–goal mapping might look like this:

Activity Goals

• Drive a forklift truck from the loading bay to a storage bay in the warehouse.
• Drive at 10 km/h or slower.
• Drive within the vehicle zone.
• Maintain a greater distance from pedestrians when reversing.
• Stack items on warehouse shelving.
• Stack items on the correct shelf according to weight and size.
• Don’t stack any shelf more than two boxes high.
• Any odd-shaped items should be in boxes.
• Collect stock from the warehouse to deliver to the production area.
• Use a trolley when the load is heavy or awkward.
• Walk within the pedestrian zone.

Investing time to listen to your workers is an essential step in any change. Safety officers might suggest simple solutions without the aid of advanced AI technology! But other problems might be more stubborn, so move to the next step.

3. Choose the Right Technology

It’s better to manage two or three goals well than to manage a dozen badly. Choose technology based on risk reduction, feasibility, and cost.

For example, it may be more effective to use computer vision to monitor vehicle speeds than to track if shelves are stacked perfectly. Start where the technology adds the most value.

4. Run a Pilot

Try the new technology in a limited area. Include worker representatives in the process. Their feedback can highlight practical issues and help build trust.

Use the pilot to understand daily challenges and what makes work easier and safer. This will shape your approach as you scale the technology across the warehouse.

5. Maintain Momentum

Giving feedback at the team level reinforces a safety culture. Tell a team that they are sticking to pedestrian zones 80% of the time, and you would like them to get a higher score next month. 

Reinforce good habits by sharing results with the team. If pedestrian zone compliance is at 80%, challenge the team to reach 90% next month.

Celebrate progress and continue to support safety in the workplace. Make sure that doing the right thing continues to be the best option.

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How Protex AI Improves Worker Safety

Protex AI integrates seamlessly with your existing CCTV system. Its AI safety software detects unsafe events and near misses in real time. This gives EHS teams quick access to video evidence that supports better safety decisions.

EHS managers can monitor high-risk areas more effectively witout needing to watch footage constantly. The system allows you to customize rules based on your own risk definitions. This helps ensure that safety protocols are followed throughout the workplace.

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