Can AI Improve Food Safety?

Artificial intelligence (AI) is beginning to reshape how food companies detect contamination risks, monitor sanitation, and trace products through complex supply chains. But as predictive algorithms move into food safety systems, regulators, scientists, and industry leaders are confronting a difficult question: How much decision-making should companies trust to machines when public health is at stake?

Imagine a ready-to-eat product processing plant reviewing its routine environmental monitoring data. Hundreds of swab results have been logged across the facility over the past year. On this day, a predictive algorithm analyzing the data flags an unusual pattern. It’s subtle but statistically significant. Environmental conditions, traffic patterns, and sanitation timing together resemble conditions historically associated with elevated Listeria risk.

No positive test result exists. The product meets all specifications. Production schedules are full. Do you stop the line?

This scenario captures the central tension emerging across the food industry: the shift from verification-based systems that confirm contamination to AI-powered predictive systems that estimate risk before it materializes.

“Do we stop the line? It depends,” says Jeff Varcoe, vice president of quality assurance and food safety at The J.M. Smucker Co.

It’s giving me indicators that we’re headed toward something bad—but before you get to catastrophe, you should have reaction criteria built in.

Jeff Varcoe

The J.M. Smucker Co.

For Varcoe, the real shift AI introduces is not automation, but foresight. “Right now, we’re so reactionary,” he explains. “I take a swab, I get a result—oh my gosh, it’s bad.” By the time a positive appears, the problem has already materialized, Varcoe adds.

AI offers the ability to move upstream, bringing together signals that food safety teams already track, from environmental conditions to traffic patterns and historical positives and interpreting them in ways that reveal when a system may be drifting out of control. “You can start putting all of those inputs in, and all of a sudden the system can say, ‘Hey, you’re trending in the wrong direction,’” Varcoe says.

But that signal is not a verdict. It’s an indicator. For that reason, Varcoe likens AI not to a replacement for human decision-making, but to a more advanced form of statistical process control. “It’s giving me indicators or feedback that we’re headed toward something bad,” he says. “But before you get to catastrophe—before you get to your million-dollar problem—you should have some reaction criteria built in.”

Instead of waiting for a confirmed contamination event, companies can define earlier thresholds—small deviations that trigger targeted actions, whether that’s increased sampling, intensified sanitation, or closer monitoring of specific zones. That’s where AI’s value becomes clear, he adds. “The value is in predicting issues before they happen,” Varcoe says. “Because if I have to shut down because I really did have a catastrophe, I’ve paid for everything already. If  I can prevent that, that’s where the payoff is.”

The question, then, is not whether AI can detect risk but how companies should act on it.

Predictive analytics is really about optimization—I can bias my resources toward where failure is more likely.

Matt Henderson

Land O’Frost

A Smarter Tool

For many food safety leaders, AI is best understood as an extension of tools the industry already uses. That framing helps clarify what AI can—and cannot—do. It can surface patterns, detect anomalies, and point to areas of concern. But it does not replace the systems that determine how companies respond, say industry experts.

Matt Henderson, vice president of technical services, Land O’Frost, describes AI-powered predictive analytics as an evolution of existing practices rather than a departure from them. In a facility where AI flags a potential Listeria risk such as in the scenario above, “food safety is not yet compromised, but we’re getting some information that it might be headed that direction,” Henderson says. The appropriate response is not immediate shutdown, but targeted action—much like existing early warning sampling programs.

This is where AI’s operational value becomes clearer, Henderson notes, because it helps companies allocate attention and resources more effectively. “Predictive analytics is really about optimization,” he says. “I’m still doing my risk assessment … but now I can bias my resources toward those areas that are showing a greater likelihood of failure.”

Proper data format is key—without it, you can’t trust what AI is telling you.

Hal King

Active Food Safety

That ability to focus effort—rather than spread it evenly—has both safety and business implications. “I’m achieving the same food safety outcome while optimizing my resources,” Henderson adds. “That’s a win for the organization and for the consumer.”

AI-assisted technology arrives at a moment when the food industry is already under pressure to modernize safety systems. The Food Safety Modernization Act (FSMA) shifted the regulatory framework toward prevention, requiring companies to identify risks before they lead to outbreaks. At the same time, manufacturers are grappling with labor shortages in food safety and quality assurance roles and managing enormous volumes of environmental, operational, and supply chain data.

That data challenge is one reason AI is becoming more viable today. Hal King, managing partner of Active Food Safety and former director of food safety at Chick-fil-A, explains, “Food businesses are developing better systems to enable collecting and formatting data for harmonization to enable use in AI, because proper data format is key to being able to use AI for data analytics and reporting.”

We’re moving from asking, ‘Do we have contamination?’ to, ‘What is the probability that risk is increasing?’

Martin Wiedmann

Cornell University

From Detection to Decision

Across the food industry, AI is beginning to change how companies think about risk, not by replacing existing food safety systems, but by reshaping how data informs decisions. At its core, AI’s value is not in automation, but in timing and prioritization, say experts.

“AI is generating the most measurable value in areas where data is continuous and structured and directly tied to decisions,” says Willette Crawford, owner of Katalyst Consulting. “The real impact isn’t automation, it’s prioritization, because it helps teams recognize meaningful signals earlier and act before a loss of control occurs.”

According to Martin Wiedmann, professor of food safety at Cornell University, that shift from reacting to confirmed contamination to anticipating elevated risk is one of the most significant changes underway. “We are moving from answering a reactive question—‘Do we have contamination?’—to a more proactive one: ‘What is the probability that contamination risk is increasing?’” he explains.

The real impact isn’t automation—it’s prioritization, helping teams act before a loss of control occurs.

Willette Crawford

Katalyst Consulting

But that transition is still incomplete, he notes. While predictive models can flag elevated risk, Wiedmann cautions that they are not yet reliable enough to function as stand-alone early warning systems. Their performance remains heavily dependent on data quality, relevance, and validation—limitations that become especially pronounced when dealing with rare contamination events such as equipment failures or atypical traffic patterns that are not well represented in historical data.

In predictive food safety systems, algorithms combine multiple variables that may influence pathogen growth or transfer inside a facility, chemical contamination, or the presence of physical hazards. When analyzed together, these factors can reveal subtle correlations that would be difficult for human analysts to identify. The goal is not to prove contamination has occurred, but to forecast when conditions resemble those historically associated with elevated risk.

Industry leaders say this shift represents a fundamental evolution in how food safety systems operate. Land O’Frost’s Henderson agrees that the industry is moving in that direction. “We aspire to the predictive state, implementing controls before the likelihood of failure presents itself,” he says. “AI will make that much more attainable because there’s so much data analysis required.”

Increasingly, companies are integrating these capabilities into broader digital systems. “Some businesses are successfully using trained AI systems as a ‘source of truth’ across their enterprise,” King says. “They’ve trained AI on operational SOPs and corrective actions so employees at any level can prompt the system to enable the proper response.”

Proving the Algorithm

Despite the growing interest in AI-powered safety tools, many food safety leaders remain cautious about integrating algorithms into critical decision-making processes. One of the central concerns is validation. In traditional food safety systems, validation is a well-defined process used to demonstrate that a control measure effectively reduces hazards. Validating an algorithm, however, is far less straightforward.

For example, when companies validate a thermal process or sanitation procedure, they can measure performance directly, demonstrating that the process consistently reduces a hazard. AI models, by contrast, rely on statistical relationships within large datasets, making their outputs less transparent. To ensure reliability, companies typically evaluate predictive models using historical datasets, testing whether the model accurately identifies known patterns or contamination events. Some organizations also conduct pilot programs, running the algorithm alongside existing monitoring programs to compare predictions with real-world outcomes.

The validation challenge is compounded by how AI is often misunderstood. “The hype exceeds the science when AI is positioned as a detection tool rather than what it actually is,” Crawford says. “It doesn’t detect pathogens—it interprets signals that correlate with risk, and without strong data quality and context, it can create a false sense of precision.”

Effective validation, she adds, must be continuous rather than static. “Validation has to go beyond a one-time exercise,” Crawford says. “It should mirror process validation, with continuous performance monitoring, testing against real-world variability, and reassessment as conditions change.”

Documentation is a critical component of this process, Crawford adds, noting that companies in the food supply chain will likely need to demonstrate how models were trained, what data sources were used, and how predictions are interpreted operationally. One reason for this is that regulatory expectations are likely to evolve at a more rapid pace and regulators will expect familiar forms of documentation. “They’ll want traceability of data sources, evidence of validation, and clarity on who remains accountable for the decision,” she says.

Crawford, who helped write the FSMA regulations during her tenure at the U.S. Food and Drug Administration, says transparency will be key. “Companies shouldn’t present AI as a black box,” she says. “They need to explain its intended use, the inputs and outputs, decision thresholds, and the human oversight and validation behind it.”

King echoes the need for structured oversight. “Many businesses try AI without ‘integrity gates’—human or another system to validate the outputs,” he says. “And they end up doing more work to manage the system.”

I don’t need to prove whether you used AI—I just have to prove the product caused harm.

Bill Marler

Marler Clark

Legal Liability?

For all of AI’s promise, legal experts say one thing is clear: It does not reduce accountability; it clarifies it. For manufacturers in particular, liability remains largely unchanged, regardless of how advanced their systems become.

“In food cases, liability depends on where you sit in the chain of distribution,” says Bill Marler, managing partner of Marler Clark, a leading food safety litigation firm. Companies that manufacture food products—whether a processor or a restaurant—are typically held to a strict liability standard. “I don’t need to prove that they were careful or careless, used AI or didn’t use AI,” he explains. “I just have to prove that the product caused harm.”

In that sense, AI is legally irrelevant to the core question of liability. If a contaminated product reaches consumers, responsibility still attaches to the manufacturer. Where AI may begin to matter more, Marler notes, is for companies further down the supply chain—retailers or distributors—where questions of oversight and duty of care come into play. Even so, he sees AI as ultimately becoming part of the baseline expectation. Used effectively, it can help prevent outbreaks—and in doing so, reduce legal exposure. “If AI is used as a tool, it can help make food safer,” he says. “And if you prevent outbreaks, you don’t get sued.” Over time, he adds, these systems may become “the standard of care,” particularly for companies that rely on data-driven traceability and monitoring.

But Marler also cautions against overreliance. As AI systems improve, they may produce correct answers most of the time—but not all of the time. “My concern is that people will get lazy and rely on AI for the answer,” he says. Drawing a parallel to recent legal cases involving fabricated AI-generated citations, he warns that blind trust in automated outputs—whether in law or food safety—can create new risks if human oversight is removed.

That concern is echoed by Shawn Stevens, founder of leading industry law firm Food Industry Counsel LLC, who focuses on how AI influences decision-making and legal exposure. “If AI generates a food safety program … and we rely upon that, and it’s wrong, we’re still going to be the ones liable,” Stevens says. In legal terms, reliance and trust are closely linked. If a company accepts AI outputs as correct and acts on them, responsibility for those decisions does not shift to the technology—it remains with the organization.

If AI tells you there’s a risk and you don’t act, that’s where exposure comes in.

Shawn Stevens

Food Industry Counsel LLC

In some cases, Stevens argues, AI may actually increase risk. Once a system identifies a potential issue, that information can trigger a legal obligation to act. Drawing on established enforcement frameworks, he explains that regulators may ask whether a company was aware of a condition that could lead to contamination, whether it had the ability to address it, and whether it failed to do so.

“If AI is telling you there’s a heightened risk, even if there’s no finished-product contamination yet, that could be enough to establish awareness,” he says. “And if you’re in a position to act and don’t, that’s where exposure comes in.”

At the same time, Stevens emphasizes that AI can be used in ways that are both practical and defensible. Lower-risk applications—such as monitoring employee behavior, sanitation practices, or adherence to existing programs—can strengthen oversight without replacing human expertise. “We’re layering it in, in a belt-and-suspenders fashion,” he says. “That’s something I’d be very comfortable defending in front of a jury.”

The distinction comes down to how AI is used. Systems that enhance existing controls and prompt further investigation tend to reduce risk. Systems that replace human decision-making or encourage companies to ignore signals they don’t want to act on can do the opposite. “If AI tells you something and you choose not to believe it, and it turns out to be right, you’re going to be scrutinized,” Stevens says.

The appropriate response, he adds, is not blind action but disciplined follow-through. Companies should treat AI outputs as hypotheses to be tested and conduct the same investigations they would under any food safety concern, analyze the data, and document their findings.

“Where we don’t want to be is complacent or lazy,” Stevens says. “If we can show we were paying attention, investigating, and trying to do the right thing—with documentation to back it up—that’s what ultimately protects you.”

Humans Still Hold the Line

Even the most advanced algorithms cannot fully replace the expertise of food safety professionals, experts say.

“It still relies on an individual at the end of the day,” Henderson says. “The person who understands the system and can put everything into context is the one who should make the food safety decision.”

Varcoe emphasizes that AI lacks operational awareness. “The tool doesn’t have the context of everything else that’s going on,” he says. “You can’t pull humans out of the process.”

Even the most advanced algorithms cannot fully replace the expertise of food safety professionals.

Crawford adds that this is where AI’s limits are most clear. “AI can identify patterns, but it can’t fully interpret operational nuance or accountability,” she says. “The most effective systems are human in the loop, where AI sharpens expertise rather than replaces it.”

Even strong advocates of digital systems draw a firm boundary. “I don’t know any business that is allowing AI output to inform final decisions like this, nor should they,” King says. “No decisions that may affect the safety of food should be made by an AI system alone.”

The real advantage lies in how companies govern these tools. Validation, transparency, and human oversight will remain essential components of any AI-enabled safety system. As the technology continues to evolve, the challenge for the food industry will not simply be adopting AI—but ensuring it is used in ways that strengthen, rather than weaken, the systems designed to protect public health.