Building a Strong AI Foundation for Food Safety Compliance

Food and beverage manufacturers are increasingly excited about the possibilities of artificial intelligence (AI) to enhance their operations. Unfortunately, many lack the robust data infrastructure necessary to capitalize on this tech.

Survey Insights on AI Readiness

A recent survey by Barclays involving 100 leaders in the food and beverage sector reveals that while 90% believe AI can expedite decision-making, only 49% have integrated it into their operations or forecasting systems. Notably, the respondents identified three primary obstacles: poor data quality, outdated legacy systems, and insufficient analytical capabilities.

Compliance Challenges Ahead

The 2026 TraceGains survey corroborates these findings, indicating that 40% of food quality, safety, and R&D professionals see disconnected systems and data as significant hindrances. Furthermore, in a Columbus survey targeting IT and technology leaders, 83.4% expressed strong confidence that AI could resolve their pressing business issues. While increased productivity was the most frequently reported outcome, improvements in revenue growth and reduced operating costs lagged considerably.

One of the most urgent data initiatives on the horizon for food companies is the FDA’s Food Traceability Rule, known as FSMA 204. This mandate requires companies handling foods from the Traceability List to gather critical data points at key tracking events and be prepared to provide records within 24 hours of an FDA inquiry. The compliance date has been set for July 20, 2028, primarily due to the readiness of companies' trading partners who may have less sophisticated systems.

Transforming Compliance into an Opportunity

Rather than merely viewing FSMA 204 as a regulatory requirement, food manufacturers should embrace it as an opportunity to build infrastructure. The clean and connected data that can swiftly satisfy FDA requests will also serve as the backbone for demand sensing, quality prediction, and AI-driven decision-making.

Litehouse Foods is currently laying that groundwork. The company is in the midst of a significant ERP implementation, focusing on aligning processes with real-world operations in their factories. They have established a data governance committee aimed at standardizing item, supplier, and product data. Andy Juarez, Litehouse's vice president of operations, emphasizes, “Clean data is essential for future tools.”

Bridging the Organizational Gap

However, even with reliable data, some facilities face internal barriers. AI can rapidly recognize supplier risks or anomalies, but if the response process involves numerous functional silos and layered approvals, the advantage is deferred. Merely automating processes within these silos does not eliminate the delays or friction that can stall decision-making.

To effectively address this gap, a structured approach is necessary. Columbus’ Rapid Adaptation Engine (RAE) offers an innovative organizational design strategy that complements technology. It promotes a cohesive environment where people, decisions, expertise, and work align for improved business outcomes. Ed Kunnary, who leads Columbus' Strategy and Growth team in the U.S., asserts, “Long-term value will stem from redesigning work to leverage the strengths of people, data, and AI.”

Steps to Effective Data Management

• Consider traceability data as shared infrastructure: Establish business owners for lot, item, and supplier records besides just quality assurance or IT teams.
• Refine processes before automation: Clearly map operations related to receiving, transformation, and shipping.
• Set specific value targets early: Align each initiative with measurable outcomes like trace times, avoided waste, and delivery performance.
• Define decision-making protocols pre-alert: Determine in advance who is authorized to make critical decisions when AI issues an alert.
• Focus on areas where coordination is essential: Traceability, recall readiness, and supplier quality should be priority targets for cross-functional efforts.

Companies should not wait for complete readiness. Identifying one traceability or quality issue and developing it into a functioning AI agent through Columbus' Idea to Agent program can provide meaningful insights. For further exploration of building an organizational framework, consider downloading C|The Shift: Redefining Global Manufacturing.