The Risks of Using AI in Risk Assessment: Understanding AI’s Limitations in Determining Warehouse Risks and Safety

October 29, 2026

Warehouse manager considering AI in risk assessment while holding a tablet, with digital charts and chat messages overlaid in front of pallet racking.

Artificial Intelligence or AI can do a lot of things and make our lives easier in many ways. However, AI has considerable limitations when it comes to using it for certain warehouse applications. One of those applications is performing risk assessments and/or creating safety procedures in the warehouse. In this article, we will explain these limitations to help people understand what AI can and cannot do to assess the safety of warehouse facilities.

What is AI? (No, Really, What is It?)

When people refer to AI, they tend to lump it into one grand category that encompasses a variety of computing programs. There are actually many types, or subsets, of AI, some of which we interact with every day. This article will focus on two AI subsets, Generative AI (GenAI) and Large Language Models (LLMs).

Generative AI is defined as “A type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts.”1 GenAI programs are fed large amounts of training data (text, images, music, etc.) which it then uses to create new content for an end user. It does this when usersgive these programs specific prompts or inputs telling the AI agent what to do or what the user wants. When you use AI to create an image or computer code, you are using GenAI.

Large Language Models were the first AI models developed, and they were trained using large amounts of text (books, articles, newspapers, etc.). These models process that information for language generation. LLMs can process text in the following ways: generating, summarizing, translating, and analyzing. LLMs are the basis for many of the AI applications we already use like ChatGPT and Gemini.2

We need to acknowledge that GenAI and LLMs are powerful tools that are changing the way we work and how we develop and implement automated systems. However, we also need awareness of its weaknesses and limitations, especially if using it for warehouse safety and risk assessments.

Using AI for Safety

AI is useful for researching and organizing safety standards, such as OSHA or electrical standards, and putting that information together so companies can draft procedures and develop risk assessments for their facilities. It can also summarize information, which is helpful because some standards are written in technical jargon that can be difficult for lay people to understand.

However, some companies are going beyond research and using AI to create safety manuals and protocols for their warehouses without confirming this information with safety professionals first. Other companies are taking AI even further and using it to generate their own risk assessments. While this can save companies money, it could lead to significant safety lapses.

Caution Ahead: AI’s Limitations

When you understand how GenAI and LLMs work, it is easier to understand the technology’s limitations, so let’s examine how AI functions in order to learn its weaknesses.

Limitation 1: GenAI does not ‘think’

GenAI and LLMs are designed to generate words in a logical order using general information that has already been provided. Put another way, the AI agent takes words and text that it has been trained on and puts them together in new ways depending on the prompts and inputs of the user.

As previously mentioned, AI is trained in a variety of subjects through a variety of materials. While that knowledge is vast, it isn’t necessarily deep. Although it can quickly search for general safety standards, it may not have the training to provide specific answers for specific applications or situations, especially when it comes to fast-changing technologies like warehouse automation. When it comes to AI, the more technical or niche the topic being asked, the more likely it will produce an incorrect answer.

Another example, while AI might have been trained using federal standards, it probably hasn’t been trained on the standards of all fifty states. It also can’t always differentiate between states. If you ask for electrical regulations in Minnesota, AI might give you the standards for Georgia or from a mix of several states.

The lesson: This is why it is imperative to consult with a safety expert or professional, either on staff or from an outside agency, to make sure AI is providing the right information and including all relevant information for your situation.

Limitation 2: Because of how AI is trained, its responses appear confident and authoritative

GenAI and LLMs are designed to generate the next word in their responses based on what other words and texts have been used in their training. If the materials used to train AI were written or spoken in a confident manner (e.g., research papers or encyclopedia entries), the answer AI provides will also be confident. That is why AI answers appear confident and trustworthy to the end user, even when the answer is wrong.

The lesson: Even though AI may provide a confident and authoritative answer, the end user still must confirm the information and verify the sources AI used before accepting its response.

Limitation 3: AI hallucinates

Even the creators of OpenAI admit that there is no way to prevent this. That’s because AI was designed to provide answers. Therefore, when an AI agent doesn’t know something, in an effort to provide an answer, it will make stuff up. Made-up responses are called “hallucinations.” Unlike humans, AI will never say, “I don’t know,” because it wasn’t trained to do that. AI was designed to provide answers, therefore answers, both good and bad, are what the user will get.

The lesson: Once again, end users need to follow up and verify with human experts the answers and sources they receive in an AI generated response to avoid basing their safety initiatives on hallucinations.

Limitation 4: AI is designed to make you happy

It’s true. AI was designed to make users happy, to give us what we want. Think of it as a form of that old saying, “the customer is always right.” Technically, it’s called Reinforcement Learning with Human Feedback. When training GenAI and LLM models, humans provided feedback to the AI agent telling the agent if it did a good job or not with its response. With each new response, the AI agent’s goal is to get more positive feedback from its human reviewer. This effort to please is what most often leads to AI hallucinations.

The lesson: The best way to prevent hallucinations is for the user to provide as neutral and unbiased prompts and inputs as possible. For example, if you ask GenAI a question this way, “I’d like to add more racks to my existing storage facility. Is this design OK?” Even if the design is not OK, AI will tell you it is because the AI agent assumes you wanted that answer.

Avoiding bias in AI prompts can be difficult, but not impossible. Sometimes simply having an awareness of bias before inputting prompts helps. Another technique is creating several different ways to ask your question and examining each for bias before inputting or inputting each question individually to see what kind of responses you get. Avoiding tags, like ‘is this OK’ or ‘should I do this,’ is another way to avoid people pleasing. Another good option is to craft a multiple-choice prompt, where AI has to decide between more than one item. That way you are not forcing it to agree with you.

Limitation 5: GenAI is probabilistic, not deterministic

When you ask GenAI or LLMs a question, they are not looking up information in a database and regurgitating an answer. They are generating their own answers based on the probabilities of the words it chooses going in a particular order. It’s where GenAI gets its name.

That means that two people can ask an AI agent the exact same question and get two different answers. AI models are also static. Even though AI companies do updates and training on a regular basis, the world is dynamic and information changes constantly, which leads to what is called in the industry “model drift.” Also called model degradation, model drift means that as new information and data becomes available, the AI model has difficulty correlating its training data with the new data. Technologies like automatic storage and retrieval systems (ASRS), autonomous mobile robots (AMRs), or lithium-ion batteries, are dynamic and constantly evolving. AI programs might not have the latest information on these systems to provide current safety responses.

The lesson: Knowing how GenAI and LLMs operate allows the end user to provide better prompts, verify the answers and sources given, and then double checking everything with an authoritative person or known field expert to ensure the response given is the one that will best help the company.

How Unverified AI Use Effects Risk Assessments

While the obvious negative effects of unchecked GenAI would be unsafe or even dangerous warehouses, there are other, subtler effects that could impact safety. Even if these issues don’t affect safety, they could affect the bottom line.

1) Getting wrong information

If you trust the answer AI provides you without doing any verification, you could potentially implement incorrect safety measures or overlook significant safety risks in your facility.

2) Not getting enough information

Even if you verified the answer as good information, how do you know that answer covered everything that could go wrong in your warehouse? Unless you are a safety expert, you don’t. This could lead to blind spots in safety measures and programs.

3) Getting irrelevant information

Responses could include information that doesn’t apply to your facilities. For example, AI could include information on cold storage even if your facility doesn’t use it. Information that is irrelevant will lead companies down rabbit holes that take time and resources away from doing what is needed to make the warehouse safe.

Another way irrelevant AI information can lead you astray is in risk probability. All types of equipment and machinery carry some kind of risk, but the probability of a specific risk happening could be high or low depending on how you use the equipment. AI might tell you to mitigate an issue that has a low probability of occurring while ignoring a high-probability factor, causing you to implement protections against things that are unnecessary or not protecting against high-risk situations at all.

4) Getting bogged down in too much information

Like irrelevant information, too much information also requires people to sift through pages and pages of details instead of moving forward.

Case Study: What Can Happen Relying on AI Alone

The following is a real situation involving a real company:

A company installing automated car wash equipment decided to use an AI agent to create their safety programs and procedures. Because of the prompts input by the company, the agent didn’t find any safety standards that were specific to “car washes.” As a result, people at the company assumed that standards for “car washes” didn’t exist. The company continued using AI to create their own risk assessments and safety manual, which included items such as developing procedures for storing hazardous chemicals and where fire extinguishers were located. Then OSHA inspected the facility. The OSHA representative produced a 40-page report of violations and abatements resulting in issues that needed fixing and fines to be paid. In the end the company hired an outside firm to help them get out of this difficult situation. It was an expensive lesson.

Ask AI, then Verify with a Pro

This article isn’t to say you can’t use AI at all. With the right prompts, AI agents can point companies in the right direction for safety regulations and risk assessment areas. AI can be a starting point, especially for small to mid-size companies that don’t have the budgets for in-house safety departments. However, companies should not rely on AI alone.

Companies can look to public organizations for help with risk assessments. They can contact their local OSHA office or state and local government workplace safety agencies. These organizations have agents to help with questions or find areas that AI can overlook. And they can often provide these services for low or no cost. Other outlets include electrician or mechanical unions, especially if you have union members on staff.

If a company doesn’t have an in-house safety department or officer, they can partner with a safety specialist, a company or consultant that has expertise in the type of warehousing that company does. The members of MHI’s Solutions Community have that level of expertise. Contact a member company today to learn your options for having a professional risk assessment done.

Don’t leave the safety of your warehouse and the people working in it to AI alone. That is a risk not worth taking.

Contributor: Nathan Hibbs of Beckhoff Automation LLC

1Harvard University

2IBM

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