AI-powered traceability systems drive consumer confidence in processed foods

Artificial intelligence (AI) is transforming the way consumers interact with food safety systems, according to a new study published in Foods. The research reveals that AI-powered chatbots can bridge the long-standing gap between food producers and consumers by simplifying complex traceability information and driving higher levels of trust and engagement in the rapidly expanding prepared food industry.

Titled "Food Traceability System Design Incorporating AI Chatbots: Promoting Consumer Engagement with Prepared Foods," the study investigates how digital traceability systems integrated with AI assistants can help consumers access transparent and reliable product information without feeling overwhelmed by data. Conducted by researchers from Shandong University and Huzhou University, the study demonstrates that AI-enabled systems not only enhance ease of use but also significantly strengthen consumer confidence and willingness to engage with brands in a market facing rising safety concerns.

Rethinking food safety in the age of information overload

As the global food industry becomes increasingly industrialized, the supply chain's complexity has led to rising concerns over food safety, fraud, and transparency. Traditional food traceability systems, typically accessed via QR codes on packaging, allow consumers to view details about ingredients, production, and testing. Yet these systems often overwhelm users with static, menu-based data that requires effort to interpret.

The researchers argue that such static systems, while informative, create an information overload problem that limits consumer participation. Many buyers lack the time or expertise to analyze detailed reports on preservatives, additives, or contamination risks. Consequently, the full potential of traceability systems to improve trust and food quality remains underused.

The new study introduces the AI Traceability Assistant, a chatbot designed to interact with consumers in natural language. Instead of manually searching through datasets, consumers can now ask simple questions such as whether a product contains certain preservatives or sweeteners, and receive instant, customized responses. This design, based on principles of information overload theory and the Technology Acceptance Model (TAM), aims to streamline data delivery, making food safety information more accessible and meaningful.

The research focused on prepared foods, one of the fastest-growing sectors in China's food market. Despite its estimated value of 485 billion yuan in 2024, market penetration for prepared foods remains limited, with trust and transparency identified as key barriers. By applying AI chatbot technology, the researchers sought to understand how enhanced interactivity could improve consumer attitudes toward these products.

How the AI traceability assistant transformed consumer engagement

To test the system's effectiveness, the team conducted three controlled online experiments involving 747 participants across China. Each experiment compared consumer responses to two types of food traceability systems: a traditional QR code interface and an AI chatbot-embedded interface. The products tested included Kung Pao chicken, fish-flavored shredded pork, and pickled fish, while the traceability concerns covered preservatives, sweeteners, and drug residues, issues that commonly drive skepticism in processed foods.

The results were conclusive. Consumers using the AI-assisted system exhibited significantly higher engagement levels, including greater intent to purchase, repurchase, and recommend products, than those using traditional systems. The research established that perceived system ease of use was a critical mediating factor: when consumers found the system intuitive and effortless, they were more inclined to engage positively with the brand.

Moreover, the study identified a moderating role of perceived product risk. Consumers who believed that a product carried higher safety risks responded even more positively to the AI assistant. In high-risk contexts, the chatbot's ability to deliver concise, relevant, and reliable information had a stronger influence on perceived system usability and consumer behavior.

These findings align with the Technology Acceptance Model (TAM), which links user experience and system design to behavioral intentions. By applying TAM to food traceability, the authors established that reducing cognitive barriers is central to encouraging engagement. The study also validates earlier theories suggesting that AI interfaces enhance user trust and satisfaction in digital commerce by creating more human-like and interactive experiences.

Practical implications for industry and policy

The study provides a practical roadmap for food producers, regulators, and technology developers seeking to improve consumer confidence in processed food products.

For manufacturers, adopting AI-powered traceability systems could represent a competitive advantage in a saturated market. Traditional traceability tools require consumers to navigate through dense menus to locate specific information, which can discourage them from engaging. In contrast, an AI assistant can handle queries automatically, summarize complex data, and provide targeted answers, thereby improving transparency and the overall user experience.

The study recommends that food companies prioritize chatbot integration in product categories with higher perceived risks, such as processed or multi-stage foods. These are precisely the segments where information asymmetry between producers and consumers is greatest. In such cases, an AI assistant can serve as a digital mediator, clarifying safety data, explaining quality assurance processes, and offering reassurance in moments of doubt.

Beyond private industry, the research suggests broader policy implications. Governments and food safety authorities could encourage the adoption of AI-based traceability solutions to support digital governance and consumer protection. The model also provides a foundation for combining AI with blockchain technology, enabling both reliable data management and user-friendly access to verified information. Such hybrid systems could simultaneously address the twin challenges of data authenticity and information usability that currently hinder the effectiveness of traceability frameworks.

However, the authors caution that implementing AI-driven systems comes with challenges. Developing and maintaining these technologies requires substantial investment in software, hardware, and personnel training. Additionally, ensuring the integrity of supply chain data, often contributed by multiple independent actors, remains a significant concern. Maintaining data accuracy and preventing manipulation are essential for the credibility of AI-driven transparency tools.