The global food manufacturing industry is experiencing a significant transformation as major companies deploy advanced AI and large language models to optimize production, reduce waste, and predict consumer demand. Industry giants Land O’Lakes, PepsiCo, and Cargill are at the forefront of this technological revolution, implementing AI solutions across their supply chains from farm to retail shelf.
The stakes are substantial: the $4 trillion global food production industry could generate $250 billion in annual profits from AI’s productivity potential, according to a 2024 McKinsey & Company report. This comes at a critical juncture when global food commodity prices reached their highest level in two years in July 2025, making operational efficiency more crucial than ever.
Cargill has deployed an AI computer vision tool called CarVe that monitors meat processing workers to detect how much beef is removed from animal carcasses. When too much meat is left behind, the system alerts shift managers who can retrain workers for greater precision. This is particularly important given that ground beef prices have recently hit record highs. Additionally, Cargill uses AI to analyze sales data from Walmart, generating production recommendations that help adjust factory output in response to demand swings for products like ground beef or London broil.
Land O’Lakes, partnering with Microsoft, launched a generative AI tool this year that helps agronomists provide data-informed recommendations to farmers about crop production and soil management. The company also uses predictive AI to address a unique challenge in dairy production: cows produce milk consistently year-round, but demand for butter spikes during holidays like Christmas. The AI system helps predict these fluctuations, allowing the company to optimize whether to focus on butter production or retail milk sales.
PepsiCo has leveraged AI’s predictive capabilities over the past two years to develop new oat varieties with naturally higher protein content for its Quaker Oats products. The AI algorithm predicts which parent plant lines should be cross-bred to create varieties requiring less water, land, fertilizer, and agricultural chemicals. This innovation also reduces environmental impact by eliminating the need for whey, a milk byproduct with a higher environmental footprint.
Despite the promising applications, implementation challenges remain significant. Companies must recruit specialized engineers, software developers, and data experts while organizing data uniformly across complex supply chains that span from small family-owned farms with limited resources to large national retail chains.
Key Quotes
It’s a very expensive commodity in the market, and we won’t want waste to get sent down the stream and out the back of the plant
Jennifer Hartsock, Cargill’s chief information and digital officer, explained how the CarVe AI computer vision tool helps reduce meat waste during processing. This quote highlights the direct financial impact of AI implementation in preventing costly commodity loss.
You can’t go to the cows and say, ‘It’s game time, let’s produce as much as we can.’ They are going to do the same thing they do every day.
Teddy Bekele, chief technology officer at Land O’Lakes, described the fundamental challenge of matching consistent milk production with seasonal demand fluctuations. This illustrates why predictive AI is essential for dairy manufacturers to optimize production planning and inventory management.
Our Take
The food manufacturing industry’s embrace of generative AI and large language models signals a maturation of enterprise AI applications beyond chatbots and content generation. What’s particularly noteworthy is how these companies are combining multiple AI approaches—computer vision, predictive analytics, and generative AI—to address specific operational challenges rather than implementing technology for its own sake.
The $250 billion profit opportunity identified by McKinsey suggests we’re witnessing the early stages of a massive industry transformation. However, the implementation challenges mentioned—recruiting specialized talent and standardizing data across fragmented supply chains—reveal that AI adoption in traditional industries remains complex and resource-intensive. The success of these early adopters will likely create a competitive moat, potentially accelerating industry consolidation as smaller players struggle to match their AI-driven efficiencies. This trend also raises important questions about the future of agricultural labor and whether AI-driven productivity gains will translate to lower consumer prices or primarily benefit corporate margins.
Why This Matters
This development represents a pivotal moment in AI’s expansion beyond traditional tech sectors into essential industries that feed billions of people globally. The food manufacturing industry’s adoption of generative AI and large language models demonstrates how these technologies are moving from experimental phases to practical, profit-generating applications.
The $250 billion potential annual profit from AI implementation underscores the technology’s transformative economic impact. As food prices continue rising globally, AI-driven efficiency gains could help stabilize costs for consumers while improving profit margins for manufacturers. This creates a compelling business case for continued AI investment across the agricultural sector.
The applications showcased—from computer vision monitoring meat processing to predictive algorithms for crop breeding—illustrate AI’s versatility in solving industry-specific challenges. These implementations also highlight a broader trend: AI is becoming essential infrastructure for managing complex, multi-stakeholder supply chains. As companies like Land O’Lakes, PepsiCo, and Cargill demonstrate successful AI integration, smaller competitors will face pressure to adopt similar technologies or risk falling behind in efficiency and profitability. This could accelerate consolidation in the food industry while creating new opportunities for AI vendors specializing in agricultural and manufacturing applications.
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Source: https://www.businessinsider.com/land-olakes-pepsico-cargill-predictive-ai-food-manufacturing-2025-8