Food and beverage companies are adopting AI on production lines, yet many initiatives remain stuck in early testing phases. Success depends on whether teams begin with a clear business need rather than the technology itself.
Data challenges slow progress in the industry
Jack Payne, a veteran in food and beverage technology with over three decades of experience, identifies data as the primary hurdle. AI requires consistent, large-scale data, but manufacturing plants typically rely on multiple systems—ERP, OEE, EAM, and WMS—each with different definitions for customers, parts, and product codes. Aligning this information takes time, often causing delays of a day or more.
Informal knowledge, stored in spreadsheets or undocumented procedures, adds another layer of complexity. Payne noted that this issue extends beyond AI, affecting supply chain projects as well.
The food sector presents unique data challenges, including perishability, varying yields, byproducts, and weight-based pricing. Standard AI tools don’t account for these factors automatically. Training models to handle them requires significant effort, and many businesses abandon the process before seeing results.
Projects led by IT teams often fail to scale
Companies that successfully implement AI follow a problem-first approach. They identify a costly, inefficient, or error-prone process and then explore how AI can address it.
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Food manufacturers that succeed with AI often use industry-specific tools. These systems already recognize terms like recalls, co-products, and catch weight—variable weights for products sold by weight rather than fixed units. However, success still depends on business users, not IT, defining the problem. When executives drive projects with clear financial goals, adoption tends to be smoother.
Supply chain and procurement also offer quick wins. AI can detect unusual orders, such as a customer suddenly requesting ten times their usual quantity, or flag suppliers missing required certifications. It also improves forecasting, a persistent challenge in the industry. Payne pointed out that many companies struggle with accurate predictions, despite the proven financial benefits.
The biggest opportunity may lie in prevention. Most AI applications in food safety focus on traceability, speeding up recalls or mock recalls. One customer reduced traceability checks from hours to minutes. Payne believes AI could do more by analyzing shift patterns, training records, and production data to identify high-risk scenarios.
Regulatory pressures are increasing
Compliance is already driving AI adoption. The FDA’s FSMA 204 rule, fully effective in 2026, mandates detailed traceability records, including unique lot codes and supplier information. Many companies are struggling to meet even the basic requirements. Payne explained that the FDA currently requests an electronic, sortable spreadsheet, but the first step is simply having the data available.
AI can help compile this information, though Payne expects requirements to become more demanding. Eventually, the FDA will likely require direct system uploads in a standardized format, submitted regularly—not just during recalls. He compared this to the pharmaceutical industry, where such practices are already standard.
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Europe’s stricter AI and privacy regulations have slowed adoption there, but Payne sees potential advantages. He believes a cautious approach could lead to better use cases, rather than indiscriminate implementation. Meanwhile, North American companies often move quickly but not always strategically. Some treat AI like a novelty, identifying dozens of potential applications and attempting them all at once. Payne described this as a “kid in a candy shop” mentality, noting that businesses often realize midway through that their approach needs adjustment.
For executives feeling behind, Payne recommends starting with forecasting and planning, a well-documented pain point with clear financial returns. Dairy companies, in particular, could benefit from AI in milk costing, a complex, USDA-regulated process for paying suppliers. He called it a significant advantage with substantial benefits.
The most effective approach involves rethinking processes rather than simply automating them. Many companies ask how to speed up a ten-step process, but the better question is how to eliminate most of those steps entirely. Payne said this shift in thinking often leads to realizations about efficiency.
While AI is advancing rapidly, it remains a tool. The businesses gaining the most value from it focus on solving real problems first, rather than chasing the latest technological trends. A recent settlement deal highlighted how trust issues in the food industry can impact operations, showing the importance of reliable data and processes.