Food manufacturers have opportunities to use advanced technologies, such as artificial intelligence, to improve the quality and safety within their operations, Drew Gaputis, a principal at Deloitte, said during the virtual Food Manufacturing Summit on Wednesday.

Gaputis was joined by Shay Luo, co-founder at a stealth AI Startup, to discuss how food manufacturers are investing in ways to make their operations more efficient using these advanced technologies. 

“We’re seeing tools such as computer vision, automation [and] anomaly detection,” Gaputis said. “These are things that can be implemented in the manufacturing process to really help companies be more proactive in terms of potential safety pitfalls.” 

Gaputis added that companies’ ability to implement AI and automation to manage food safety proactively will help contain issues as soon as they arise and before they become widespread.

“I think that's something that we see companies investing in that will continue to occur,” he said.

Improving food safety and compliance

Advanced technologies can also help food manufacturers comply with U.S. traceability regulations, Gaputis said. A traceability rule under the U.S. Food and Drug Administration's Food Safety Modernization Act requires the agency to label certain foods that necessitate additional recordkeeping to protect public health, according to the FDA’s website.

The rule aims to speed identification and rapidly remove potentially contaminated food from the market to reduce foodborne illnesses and deaths. 

The traceability rule’s compliance date was initially set to begin Jan. 20. However, the FDA proposed extending the compliance date to July 20, 2028. Additionally, an appropriations law passed in November 2025 ordered the FDA not to enforce the rule before the new deadline.

Food manufacturers that take advantage of AI and other digital technologies will gain a “cleaner view” of the end-to-end supply chain, Gaputis said.

“That degree of visibility, I think, helps not only provide accountability, but [also to] identify issues and address those issues again in a very proactive manner,” he said.

As food manufacturers approach the traceability rule’s 2028 implementation date, Gaputis said he expects they will continue using the advanced technologies as well as “better managing” their supply chain “from an organizational standpoint.”

Luo added that the technology would help with predictive safety management, as AI is “extremely good” at considering many factors, “probably more than what human brains can process.”

“This can be leveraged for safety management and predict what’s coming, what would be a risk before it materializes.” 

Technology challenges foodmakers face

As food manufacturers implement AI and other technologies into their operations, they may overlook a few things and encounter challenges, speakers said.

Gaputis said some of the issues he’s seen involve data and “articulating” return on investment from an executive level.

“You can realize quick savings and productivity on an individual or maybe a functional level quite easily,” Gaputis said. “But thinking about large-scale investments in the ROI required for full-scale process transformation and organizational changes may be a little bit longer than your typical technology implementation.” 

His clients have also needed to design new or reimagined processes and operate models “holistically” to get the best use out of AI.

“We've seen a lot of a lot of large-scale CPG companies start to think about, ‘How do I define major processes by which our organizations are run? Create clarity around those processes and even owners from an end-to-end perspective across functions?’” Gaputis said. “And then using that as a mechanism to really reimagine the work, so that companies can gain full maximum leverage from the application of technology.”

He added that manufacturers need to consider shifting from applying technology to reinventing the organization now, a move that “leading companies” are quickly making.

Companies also overlook their employees’ concerns about working with AI. Luo said change management and gaining floor-level buy-in are “critical to the success of any of the digital transformations.”

“I think let's just be realistic about that and be very upfront with our workers and be radically transparent about the roadmap ahead and how you plan to use it,” Luo said.

She added that it’s more about helping people understand how AI can enable their work, rather than feeling threatened. 

“The current models are not as good as human judgment, so still, governance and human oversight are extremely important,” Luo said. “How to design a system that humans are in the loop for necessary override is more important than just letting the system run automatically.”

Luo said she doesn’t think using technology is the solution to replace human workers. Rather, it’s about how companies can use technology to address issues such as safety and tasks humans do not want to perform.

While AI is “very good” at performing repetitive and routine tasks, she added that she doesn’t think the technology is at a level where it can replace human workers.

“Those are the more valuable use cases of AI than just simply replacing people,” Luo said.