Aidan Connolly
AgriTech Capital

Camila Ulloa
Purdue University

Artificial intelligence is entering the feed industry quietly but steadily. Feed formulation software evaluates ingredient substitutions in real time. Mill control systems generate large operational datasets. Sensors monitor animal performance and feeding behavior across production systems. For many feed companies, these technologies still feel experimental but the shift is already underway.  One example is the widescale adoption of feed bin sensors to estimate feed in silos in the feed mill and on the farm shows how, with a compelling financial case, adoption can be immediate. The real transformation is not simply the arrival of new digital tools but a deeper change in how feed companies make decisions.

WHY THE FEED INDUSTRY IS A NATURAL HOME FOR ARTIFICIAL INTELLIGENCE

The feed industry has always operated at the intersection of biology, engineering and volatile commodity markets. Nutritionists balance formulation costs with animal performance. Procurement teams navigate unpredictable grain and protein markets. Mill managers must maintain consistent product quality while controlling energy consumption and throughput. This complexity makes feed production one of the most data-intensive parts of the livestock sector.

Artificial intelligence offers the ability to connect these data streams in ways traditional management systems never could. Every feed mill generates large volumes of information each day: production throughput, ingredient inventories, formulation adjustments, energy consumption and customer demand. Modern automation systems in feed mills increasingly rely on PLC and SCADA technologies to monitor production processes and generate real-time operational data. At the same time, livestock producers generate additional data on animal growth rates, feed intake and health status.

Historically, most of these datasets have remained fragmented. Nutrition software may not connect directly with procurement systems. Mill control platforms operate separately from supply chain planning tools. Data collected on farms rarely feeds back into formulation decisions quickly enough to influence production. Artificial intelligence changes this dynamic by allowing organizations to analyze multiple variables simultaneously and identify patterns that traditional systems often miss. As FAO notes, AI’s strength lies in its ability to detect relationships in complex datasets and support faster and more informed decisions across agrifood systems.

Ingredient markets provide a clear example. Feed procurement teams constantly respond to price volatility in corn, soybean meal and other commodities. Artificial intelligence systems can analyze market signals alongside formulation constraints and ingredient availability to evaluate substitution strategies more quickly.

Production efficiency offers another opportunity. Feed mills operate complex mechanical systems where small inefficiencies accumulate into meaningful cost differences. Artificial intelligence can analyze production data across milling lines to identify patterns related to equipment performance, downtime and energy use.

Yet adoption across the feed industry remains uneven. Many organizations still approach artificial intelligence as a technology upgrade rather than a management transformation. New tools are installed, but the decision structure of the organization remains unchanged. That gap matters.

FROM TECHNOLOGY EXPERIMENT TO MANAGEMENT STRATEGY

Transformational technologies rarely deliver their full value unless the surrounding system evolves as well. History offers a useful analogy. When Japan introduced the Shinkansen bullet train in the 1960s, success did not come simply from building a faster train. The entire rail network had to change. Tracks, signaling systems and operational planning were redesigned to support higher speeds. Artificial intelligence presents a similar moment for the feed industry. Installing AI tools without adjusting management processes is like placing a bullet train on conventional rails. For feed companies seeking to move beyond experimentation, a clear implementation strategy becomes essential.

In our recent work examining artificial intelligence adoption across agri-food systems, we proposed a practical framework for organizations navigating this transition. We describe this approach through the acronym DRIVE.

•  Data first. Artificial intelligence depends on reliable and connected information. Ingredient procurement systems, feed formulation software, mill operations data and logistics platforms must share information across the enterprise.
•  Run purposeful pilots. AI initiatives should begin with clearly defined operational problems such as optimizing ingredient substitutions during price volatility or improving milling efficiency.
•  Internal capability matters. Artificial intelligence changes how expertise is applied. Nutritionists, engineers and procurement specialists increasingly move from performing calculations toward interpreting models and guiding decisions.
•  VIPs are not exempt. Executive leadership must treat artificial intelligence as a strategic priority rather than a technical experiment.
•  Execute now. Organizations that deploy systems, learn from them and scale successful pilots build capabilities that competitors struggle to replicate.

THE STRATEGIC OPPORTUNITY FOR FEED COMPANIES

For the feed industry, the implications extend well beyond operational efficiency. Feed remains the largest cost in livestock production. Even small improvements in feed efficiency can generate meaningful gains across the entire protein supply chain. Artificial intelligence provides new tools to manage this complexity. Advanced models can evaluate ingredient markets, animal performance and environmental conditions simultaneously, helping nutritionists refine ration strategies as conditions change. None of this eliminates the importance of human expertise.

The feed industry has always combined scientific knowledge with practical experience. Artificial intelligence does not replace these capabilities. It amplifies them by identifying patterns that would otherwise remain hidden. The companies making the most progress are not simply adopting new software. They are redesigning workflows and integrating artificial intelligence into how decisions are made.

The question for feed industry leaders is not whether artificial intelligence will influence the sector. The real question is whether organizations are prepared to redesign how they operate to capture its full potential. In railways, the bullet train succeeded because the entire system evolved around it. In the feed industry, artificial intelligence offers similar possibilities. But the companies that benefit most will be those prepared to rethink how information flows, how decisions are made and how technology supports the people running the system.