Processors Push the Envelope on Digital, Data Transformation

As data, digital interfacing and virtual tools continue to work their way into numerous manufacturing and processing industries, companies in the food & beverage industry join the cavalcade of digitization toward a future of integrated automation and the potential for artificial intelligence and machine learning in their facilities.

The industry is beyond pioneer stage, says Jack Payne, solution consulting director for food & beverage and process manufacturing at Aptean (www.aptean.com), and it’s also beyond just the big companies taking on the early risk.

“As with any new technology, there always are the early adopters and the laggards, and the [Covid-19] pandemic really fueled the fire underneath a lot of the people that weren't early adopters to get on board with a digital transformation journey,” he explains. “What I’m hearing now is, ‘The big guys are doing this — we need to stay competitive.’ ”

Indeed, some larger companies initially jumped in with both feet, and Payne says many mid-market food & beverage companies have now finished their initial digital journeys, moving on to the next phase of growth.

Cargill, for one, has been pushing the envelope on digital transformation for several years now. In fact, in March 2026, Cargill was named a winner of the 2026 BIG AI Awards, presented by the Business Intelligence Group, for its use of AI and digital technology in smart manufacturing as well as its R&D innovation.

Last year, it unveiled its proprietary — and now award-winning — CarVe technology, which uses computer vision, proprietary data and AI tools to monitor red meat yield in real time on the fabrication floor and offer instant feedback on employees’ performance and technique. Better meat cutting yield means more meat per animal makes it into the food supply and stays out of the waste stream, with an article on CarVe in The Financial Times saying as much as 0.5% more meat could be recovered per animal. In April 2026, CarVe was awarded a 2026 bronze Edison Award in the Sustainable Industry Solutions category.

Digital, data drive innovation

Abhishek Roy, senior director, Global R&D - Digital and AI, for Cargill, who has worked in his role for nearly a decade, puts it frankly when he says, “Data is a blessing and a challenge too.” Working for a company the size of Cargill, he adds, leaves no shortage of available data. But the quality of the data matters as well: Even if you have a ton of data points, not all of it may apply.

“You feel like a kid in a chocolate factory, since there's so many data sets and challenges you can work with — and that's what excites me and the team every day,” he says. Cargill has had several positive outcomes since embarking upon its own digital transformation journey, and today, the company has built up a strong foundation to build upon, with success stories already coming to fruition.

“When it comes to data-driven decision-making in our operations, that’s not new to us,” he says. “We have been using AI and data for a long time at Cargill. One good example would be how we used data and machine learning to forecast the staffing bottlenecks in our plants.”

Cargill’s team noted the sheer number of critical, highly specialized staff positions needed in various parts of its plants — jobs where unexpected absenteeism would basically shut down the line, because the jobs were so technical and specialized. The company set out to build a model to forecast the probability of someone not showing up to work in advance of a shift.

“We used a lot of different metrics, like the day of the week, past attendance and the weather to just give some sort of a probability of that worker missing that shift,” Roy explains. “And then the staff supervisor can use that information to proactively ask for doubles and have backups for those positions before the shift starts.”

Roy says the success of that program and others helped the team feel “quite comfortable pushing the envelope a little bit more with CarVe,” which was a much more complex undertaking. Cargill’s team was much more confident going in, which was critical because the challenge of data collection and analysis for this project looked to be a monumental task. When it came to this new project, Cargill had to start from scratch.

“We installed cameras and started collecting tens of thousands of images every day,” he says. “That's another reason why we decided to build this ourselves, because a lot of that data is very proprietary, very unique, and we felt like if we build this, it would be our competitive advantage.”

As the images came flooding in from the cameras, the next challenge for the Cargill team was teaching the system how to label what it was seeing, so that it would be able to properly differentiate between meat, bone and other materials on the line. Roy says data collection and modeling were time-consuming but eventually became iterative and led to the payoff of having a truly unique system it can expand as needed.

“It’s a patent-pending application, and the secret sauce is the data and the model, combined with our process and subject matter expertise,” he adds.

The next digital frontier

Roy explains that CarVe can be transferred to other facilities in Cargill’s network, with adjustments catered specifically to facility configuration, layout, lighting and size, among other factors. But the base model, Roy says, can be leveraged.

“We designed everything modular, nicely built up so that you can take them apart and put them somewhere else and retrain the model,” he says. “There are components we can sort of ‘plug and play,’ and the only change is the new site.”

Beyond CarVe, Cargill continues to push the envelope in the digital arena, using generative AI for ideation, concept design, food formulations and scientific experiments and design on the R&D side, according to Roy. But the company continues to explore the digital frontier in operations and supply chain as well, he says.

Payne believes digital technology’s next big step will alter how workers interact with ERP systems and similar technology down the road, where employees won’t need weeks of training to navigate menus and can get answers by simply asking for them.

“Instead of needing to be an expert in the software, you’re an expert in your role and you interact with the system in natural language,” Payne says. “That shift from navigating systems to simply asking questions is going to be a major change.”

As technology gets more complex and pulls in even more data, simplification of those interactions will be needed to keep operations running efficiently and smoothly. Also, the digital tools, AI and machine learning capabilities coming down the pike will need to be next-level to meet demand.