When most people, including myself, think about Artificial Intelligence (AI), we tend to picture Chatbots like ChatGPT, Microsoft Copilot and other generative AI models that search the internet and generate human-like responses. While this is true, AI holds a far more practical role in day-to-day food safety.  

 AI applications in food safety already extend beyond chat interfaces. They include predictive risk modeling, real-time monitoring of Critical Control Points (CCPs), and streamlined documentation systems. So instead of asking, “When will I use AI in my operations?”, a better question might be, “Where am I already using AI, and how can I get more from it?” 

So, what is AI, really?  

AI isn’t a single tool, or futuristic technology. It’s a broad field that combines computer science and statistics to build systems that can sense, learn and make decisions. AI is a layered field with varying levels of complexity and capability. Understanding these layers can help us understand the potential applications: 

• Artificial Intelligence (AI): Technology that leverages computers to mimic human behavior such as making predictions, recognizing patterns, and making decisions. 

• Machine Learning (ML): A subset of AI that uses algorithms to learn from data and make predictions. Example: predicting crop yield based on rainfall. 

• Deep Learning: A subset of ML that uses artificial neural networks with many layers to process complex data, identify patterns, and make predictions. 

• Generative AI: A subset of deep learning capable of creating new content, such as text, images or code, based on learned patterns from training data. Tools like ChatGPT and co-pilot are clear examples. 

 

 

 

 

 

 

 

In food safety multiple of these AI layers can show up in real, practical ways: 

• Predictive modeling using machine learning can allow us to detect early signs of contamination using historical testing, weather and location data. 

• Computer vision (using deep learning for analyzing images) can be used to assess processing lines for foreign objects, or potential animal activity in fields.

• Natural language processing (Generative- AI) (like ChatGPT) that can sift through audit records or inspection notes to identify patterns, and create plots, tables and analysis that can give us novel insights.

The power of AI lies in the ability of leveraging machines to process more data than any human could ever do and do it in real time.  

From Reaction to Anticipation 

Food safety has long been driven by reaction. A positive result? We disk the field. A contamination event? We issue a recall. These are necessary responses, but they happen after the risk becomes real.   

AI offers something different: the ability to anticipate. 

Imagine “predicting the invisible.” How can you anticipate something you can’t see? AI does this by integrating weather patterns, historical data and seasonal trends to forecast when and where pathogen risks are likely to spike. In essence, AI identifies the conditions that create risk before the risk manifests. 

Is this just theory? Not at all. This isn’t science fiction. At Johns Hopkins, AI models have predicted the onset of sepsis before symptoms appear. The same principle applies in food safety. Closer to home, Dr. Mohit Verma from Purdue University has used machine learning to predict levels of Bacteriodales (a fecal indicator) in the produce field with 77%, just by using weather and farm-specific information.  

The data already exists. The opportunity is to learn how to see the signal in it. 

Why is AI hard to Trust? 

The paradox of AI is that, despite its potential, it’s often hard to trust. Why?  

We (as humans) are trained to trust what we can see, measure, and validate. Audit scores. Pathogen tests. SOP checklists.  

AI doesn’t always provide that level of certainty. Instead, it connects dots we wouldn’t think connect. It identifies weak signals, signals that on their own look meaningless, but that when combined, tell a bigger story. 

This isn’t guesswork. It’s statistics, specifically, inference*. But here’s the kicker: when inference contradicts experience, it creates discomfort. 

So how do I trust AI?  

Trust is earned.  

Trusting does not mean accepting everything AI or statistics give us. It means validating the model’s predictions against reality, understanding the data it was trained on, and knowing its limitations. In practical terms, that could mean using AI outputs to inform our decisions, not replace them.  

Pair predictions with observations and compare model alerts with actual outcomes. If we do this after multiple iterations (repetition) we will start trusting AI. The more we work with it, challenge it and learn from it, the more we’ll stop seeing it as something foreign, but instead seeing it as a tool to maximize our learning.  

Buzz to Value: Start Here 

If you want to move from buzz to value, choose one of these entry points: 

• Take inventory of your data: AI is only as good as the data it learns from. Before investing in models or tools, you need to understand your data landscape:
  • What do I already collect (lab test results, audits findings, weather records, harvest schedules) 
  • Where is it stored? Is it digital, siloed, or sitting in PDFs and spreadsheets? 
• Identify your implementation friction points. Ask yourself: Where do we currently rely on subjective feelings or manual processes? These are areas for AI-supported insight. 
  • Are you manually reviewing inspection notes or audit data for trends? 
  • Is field or water data collected but rarely analyzed? 
  • These operational pain points are opportunities to test AI’s value. You’re not replacing your process; you’re supplementing it with a second set of (very fast) eyes. 
• Start small: You don’t need to change every system to be AI driven. Choose one narrow, high impact use case. 
  • For example, using Natural Language Processing to scan and summarize years of audit findings 
• Iterate and Improve: Here’s where trust is built. Once a small model is in place, you can compare predictions to outcomes, track accuracy, refine the models or questions when wrong, and optimize the data you are feeding to these models.
• Collaborate with experts: You don’t have to do this alone, Western Growers, Universities, Extension and Labs can allow you to tap into the AI world by making sense of your data.  

Takeaways:  

AI in food safety isn’t about futuristic robots, it’s about using the data already flowing through your farms, facilities, labs, and compliance systems to learn faster and effectively. We don’t need to wait for AI to arrive. It’s here. The question is whether we’ll use it well enough to stay ahead of risk. 

*Inference: a conclusion reached on the basis of evidence and reasoning.