Protein Processors Are Turning Old Operational Data Into a Real-Time Asset

Cargill’s Fort Morgan, Colorado beef plant processes approximately 4,000 cattle per day. Until recently, the data those animals generated on the fabrication floor arrived the following morning. A yield variance on a given line, a cutting technique leaving product on the bone, a manager who needed to intervene: all of it waited for the next shift’s report. CarVe, Cargill’s proprietary computer vision system now operating at Fort Morgan and at its Friona, Texas facility, changes that sequence by delivering yield data to frontline managers while the line is still running.

Jarrod Gillig, senior vice president of Cargill’s North American beef business, described the operational shift in the company’s June 2025 press release: “Before CarVe, yield data was always yesterday’s news. Now, we’re making decisions in the moment and saving product that would’ve been lost.” Gillig told Drovers in March 2026 that Cargill is targeting approximately 0.25 pounds of additional recovery per carcass, particularly around neck bones, an increment that would approach $2 million in annual value at a single facility. According to the USDA, the U.S. produces more than 27 billion pounds of beef annually, and Cargill has stated that a 1% yield improvement across the industry would save hundreds of millions of pounds of meat.

CarVe is one piece of Cargill’s Factory of the Future initiative, which now spans more than 100 projects across 35 protein-processing facilities in North America. Cargill has invested nearly $24 million in technology at Fort Morgan since 2021 and committed to a further $90 million over the next several years, covering automation investments beyond yield recovery. Leon Fletcher, Cargill’s vice president of operations for North American beef, told Drovers that CarVe functions as a training device as much as a measurement tool, using video to show employees where their technique is costing yield.

What Protein Processors Are Deploying AI For Today

Executives from Tyson, Simmons, and Happy Egg spoke at the Bentonville supply chain event, and across all three companies the AI applications with genuine operational traction were not the most forward-looking ones on any roadmap. Demand forecasting came up consistently at each company, not as a new ambition but as a planning problem the industry has carried for decades and is now, with better data infrastructure, beginning to address.

Tyson Foods has been applying AI to predict food trends and generate new product recipe formulations since 2024. In 2025 the company deployed a conversational assistant on its Tyson Foodservice customer search platform, built with the AWS Generative AI Innovation Center using Amazon Bedrock, as AWS documented in an August 2025 case study. Tyson Foodservice had limited direct engagement with more than one million operators purchasing through distributors without any direct company relationship, operators whose demand signals never reached Tyson’s planning systems. That assistant now captures what those operators are asking about, including trending product categories, regional preferences, and seasonal patterns, and routes the information into inventory management.

Jon Swann, Tyson’s vice president of supply chain, described the foundational challenge at the Bentonville event: data fed into AI models must be structured meaningfully before useful outputs follow. He noted that Tyson protects its proprietary data but sees opportunity to share more within the supply chain to achieve better optimization, a position that reflects a tension common across large protein manufacturers, where the data that would most improve shared forecasting accuracy is also the data companies are most reluctant to release. Tyson reported fiscal 2025 full-year adjusted operating income of $2,287 million, up 26% from the prior year with adjusted operating margin of 4.1%, per its SEC filings.

Simmons Foods, which supplies chicken to foodservice chains and retailers and operates as the leading North American private-label and contract manufacturer of wet pet food, is deploying digital twins and statistical process control analytics to monitor production output and reduce downtime. Marcia Reeves, senior vice president of technical services, described the forecasting ambition at the Bentonville event as getting close to the demand visibility that planners have been requesting for two decades, with AI-driven forecasting applied specifically to lower changeover costs and reduce inventory burden across poultry and pet food operations. A review of digital twin applications in the food industry published in Frontiers in Sustainable Food Systems in April 2025 found that most implementations across the sector remain in research rather than full operational deployment, concentrated in processing contexts rather than farm-level production.

The Farm Gate Is a Different Problem

Happy Egg, the Rogers, Arkansas-based egg producer, illustrates what the gap upstream of the processing floor looks like in practice. At the farm level, the company has tested AI to monitor hen health. Its most operationally developed AI deployment is in retail media, where it partnered with an advertising technology firm to optimize spending with Amazon, Target, and Instacart. Blake Klosterman, director of supply chain, described the company’s broader AI posture at the Bentonville event as early-stage exploration.

Large processing facilities are centralized, owned by a small number of companies, and already connected to enterprise data systems that generate the structured inputs AI models require. Farm-level production in beef cattle and seafood is fragmented across thousands of independent operators with variable infrastructure, no standardized data-sharing mechanisms, and limited commercial incentive to contribute to shared information pools. RaboResearch’s global animal protein outlook published in late 2025 projected that pork and beef production will contract in 2026, the first reduction in global terrestrial species output in six years, while seafood and poultry are expected to lead production growth. Integrated poultry processors run standardized, closed production systems that generate continuous data; beef cattle operations involve geographically dispersed ranches, variable genetics, weather-driven feed cycles, and auction-market transactions that interrupt the data record at nearly every stage.

Where This Lands for Retail Buyers and Category Managers

AI-driven demand forecasting at the processor level, when built on reliable data, reduces the inventory volatility that generates out-of-stocks. Better yield recovery from a cattle supply running at historic lows adds product to a market that cannot grow volume by running more animals. Mid-market protein suppliers and smaller producers in categories like grass-fed beef and fresh seafood are working from thinner data foundations and, in many cases, supply chains that do not yet generate the structured inputs those models require, which means fill rates, OTIF, and shelf availability in the protein aisle are where capability differences between large and small suppliers will surface first.

JBS announced in February 2025 that it would spend $200 million to expand U.S. beef production capacity, per Food Dive, positioning for supply to rebound as herd rebuilding progresses. Between Cargill recovering more from constrained current throughput, JBS building for future volume, and Tyson closing the demand signal gap from more than a million indirect customers, the investments these companies are making in data infrastructure now are of a kind and scale that take years to build. Retail buyers and category managers tracking protein availability should treat that timeline, not the technology itself, as the variable most worth watching.

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