Искусственный интеллект меняет пищевую промышленность: от контроля цепочек поставок до сохранения знаний сотрудников

Andreas Eschbach is the founder and CEO of eschbach, a software company that helps production teams work smarter and enhance collaboration. From contamination detection to workforce resilience, artificial intelligence (AI) is addressing the food industry’s most persistent challenges, but only for manufacturers who lay the right foundation first. Food manufacturing operates under pressures that few other industries face simultaneously: razor-thin margins, stringent and evolving regulatory requirements, complex supply chains and high workforce turnover. At the same time, consumer expectations for food safety and product quality have never been higher, and the consequences of falling short (whether a contamination event, a costly recall or a compliance failure) are immediate and very public. AI is increasingly being applied to address these challenges directly. Adoption is accelerating, but many food manufacturers still struggle to translate AI’s potential into consistent results on the plant floor. Those who are seeing real impact tend to share a common starting point: They’ve identified the specific operational problems AI can solve and built the data infrastructure to support it. Five Ways AI Is Changing Food Manufacturing AI is already making a measurable difference in food manufacturing. Here are the top ways manufacturers are using AI to improve food safety and operational performance. 1. Food Safety And Contamination Detection Food safety is nonnegotiable, and it is one of the areas where AI is delivering the most tangible value. Computer vision systems inspect products at full line speed, identifying defects, contamination, foreign objects, packaging errors and labeling issues with accuracy rates exceeding 95%, without fatigue, shift variation or the limitations of manual inspection under high-volume conditions. Beyond inspection, AI is improving the industry’s ability to respond when problems occur. Machine learning models can detect process deviations in real time before they become safety incidents. When a recall investigation is necessary, AI-powered traceability tools can dramatically reduce the time required to identify contamination sources, which is a critical capability as FSMA 204 requirements raise the bar for U.S. food manufacturers. 2. Predictive Maintenance Unplanned downtime is expensive in any manufacturing environment, but in food production, it carries additional consequences: product loss, temperature excursions, contamination risk and compliance gaps. Predictive maintenance AI uses sensor data and machine learning to identify early signs of equipment degradation, enabling maintenance teams to intervene before failures occur. The operational impact is significant. Industry data shows predictive maintenance contributes to OEE gains of 8% to 12%, while extending equipment lifespan and reducing emergency repair costs—a meaningful combination for plants operating on tight margins. 3. Process Optimization Food manufacturing presents optimization challenges that are genuinely difficult to manage manually. Raw material variability, tight quality specifications and the need for real-time adjustments across complex production environments create conditions where small deviations compound quickly. AI models analyze production data continuously to improve yield, reduce scrap, minimize energy consumption and maintain product consistency across batches and shifts. For manufacturers under pressure to do more with existing assets, process optimization is often where AI delivers its fastest return. 4. Supply Chain Visibility And Traceability Supply chain complexity has increased substantially, driven by global sourcing, ingredient variability and tightening regulatory scrutiny. AI tools help manufacturers manage that complexity through improved demand forecasting, inventory optimization and real-time supply chain visibility. On the traceability side, machine learning accelerates identification of contamination sources and supports the documentation requirements that regulators and retail partners increasingly demand. 5. Knowledge Management And Workforce Resilience High workforce turnover is one of the defining operational challenges of food manufacturing, and one of the least discussed in conversations about AI. When experienced workers leave, they take with them years of accumulated knowledge: process nuances, equipment quirks, troubleshooting approaches that never made it into a manual. In an industry where that knowledge directly supports food safety and quality outcomes, losing it isn’t just an HR problem. It’s an operational risk. AI-powered knowledge management tools address this directly. By capturing information from shift notes, maintenance logs and quality records, these systems make institutional knowledge accessible to everyone on the floor, regardless of experience level or shift. When a process deviation occurs, an operator can surface relevant historical incidents, prior root cause analyses and proven fixes in seconds rather than hours. That can translate to faster problem resolution, more consistent decisions and a workforce less dependent on any single individual’s expertise. Getting Started: What AI-Ready Actually Means For manufacturers considering AI adoption, the most important early decisions are rarely about which AI platform to choose. They’re about whether the underlying operational data infrastructure is in place to make AI work: • Consolidate and digitize your operational data. Machine learning models are only as effective as the data they learn from. That means bringing together information from across systems—MES, LIMS, historian, CMMS—and ensuring that human-generated knowledge like shift handovers, maintenance logs and quality checks is captured in a structured, searchable form. Many manufacturers have years of valuable operational history scattered across disconnected systems or locked in paper records. Making that data accessible is the essential first step. • Start focused. Identify one or two high-value use cases (predictive maintenance, root cause analysis, quality inspection) and build from there. A focused initial implementation allows a plant to demonstrate value quickly and build organizational confidence before expanding scope. It also reduces the integration risk that comes with trying to transform too many processes at once. • Plan for the human side of the transition. AI adoption succeeds or fails at the floor level. Workers need to understand what the tools do, trust the outputs and know how to act on them. Training and change management aren’t afterthoughts, but an essential part of the implementation. • Prioritize transparency and traceability in your AI systems. In a regulated industry, being able to explain how an AI recommendation was generated and audit that reasoning is as important as the recommendation itself. Choose tools and approaches that support documentation and compliance requirements, not just operational efficiency. Manufacturers seeing results from AI today are not necessarily the ones with the largest technology budgets. They are the ones who treated data as a strategic asset, started with well-defined problems and built from a foundation that could support more sophisticated capabilities over time. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?