Implementation of Artificial Intelligence (AI) in The Food Industry: Basis for an AI Comprehensive Model

Artificial Intelligence (AI) has emerged as a major disruptive and transformative force within the food and retail industries, reshaping supply chains, quality metrics, and organizational operational paradigms. This study evaluated the extent of AI implementation within the food industry based on software quality dimensions—Functionality, Reliability, Usability, Efficiency, Maintainability, and Portability—and determined the core operational challenges encountered during deployment. Utilizing a quantitative research design, data were collected from food industry stakeholders and analyzed using frequency distribution, percentages, weighted means, and One-Way Analysis of Variance (ANOVA). The findings revealed that AI implementation is highly realized across key metrics, with Functionality scoring highest (Overall Weighted Mean = 3.29, Highly Implemented) and Portability presenting stable operational compliance (Overall Weighted Mean = 3.17, Implemented). Inferential testing verified that there are no statistically significant variances in the level of AI implementation across workforce demographic markers or structural configurations such as company size (p > 0.05). However, significant organizational, infrastructure, and technical expertise limitations persist. Based on these empirical results, a comprehensive structural AI Model is proposed to provide a practical foundation for sustainable, ethical, and strategic AI optimization within food industrial systems.

Artificial Intelligence, Food Industry, Industry Acceptance, Technological Capabilities, Software Quality Model, Structural AI Model.

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