Integrating Artificial Intelligence in Autonomous Cashier Systems: A Study on Functional Schema Design and Its Impact on Supermarket Operations

Abstract

This integrative literature review (ILR) explores the transformative potential of artificial intelligence (AI) in optimizing in-store logistics, a crucial aspect for retail environments striving to meet growing consumer demands for convenience and quick product availability. It aims to provide retail managers and logistics specialists with actionable insights on AI-driven tools that enhance competitiveness by improving inventory management and space utilization in retail settings. The conceptual framework is framed by functional schema design, user acceptance and experience, operational efficiency, data security and privacy, and equitable access, emphasizing the ethical and inclusive implementation of technology. Key findings suggest that AI improves demand accuracy, reduces inventory mismatches, and enables adaptable store layouts that respond dynamically to real-time consumer behavior, creating a more efficient, responsive, and customer-centered retail environment. Additionally, this study examines the adoption of AI in cashier systems as a response to evolving consumer expectations for efficient digital shopping experiences, revealing that AI-enabled cashier systems significantly reduce wait times and operational costs while providing personalized experiences through real-time analytics. However, challenges such as robust data security protocols, inclusive design for diverse user groups, and employee reskilling remain critical as automation progresses. Recommendations focus on privacy-enhancing data practices, user-friendly interfaces, and hybrid solutions combining human support with automation to foster inclusivity and consumer trust. Future research should address the long-term impacts of AI in retail, cultural adaptability, customer engagement, and secure data handling to support a balanced, customer-centered approach to automation in the retail sector.