Introduction: Modern challenges in food safety and the growing need for advanced solutions have driven the adoption of artificial intelligence in food safety management systems. Implementing new systems to ensure safe, high-quality food and standards compliance remains crucial for public health. Various artificial intelligence technologies have been used in the food industry, including expert systems, fuzzy logic, neural networks, machine learning, sensors, and language models. The benefits of artificial intelligence— such as rapid data analysis, automated decision-making, and improved risk prediction accuracy—are being thoroughly examined. Purpose: To evaluate the potential application of artificial intelligence language models in food safety management systems, focusing on risk factor analysis. Methods: This study is based on a theoretical literature review, analysis and comparison of language models, and a case study. The case study tested a selected artificial intelligence system on two theoretical and two real-world examples from the food industry following HACCP principles. Results: The results show that the artificial intelligence system is most comparable with benchmark cases for microbiological risk factors, both in detection (48 %) and in managing risks through preventive measures (42 %). The system is less comparable for chemical and physical risk factors and allergens. A high degree of compliance was achieved in determining critical control points, ranging from 79 % to 92 % in the selected examples. However, when establishing critical limit values, corrective actions, monitoring systems, documentation procedures, and verification protocols, the artificial intelligence system was considerably less precise; its outputs were more general and less tailored to the specific production process, often mentioning »automation« and »digitization«. Discussion and conclusion: A comparison with existing HACCP study records indicates that while artificial intelligence can enhance risk factor analysis, complete automation at this stage—without further tool adaptations—is not advisable. The consistency of the artificial intelligence system was based on content comparisons with examples from scientific literature and existing HACCP studies. Expert evaluation and regulatory oversight remain crucial for ensuring food safety. Future research should focus on targeted algorithm improvements and regular verification of compliance with legal requirements and food standards.
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