INTEGRATING ARTIFICIAL INTELLIGENCE DRIVEN DECISION SUPPORT SYSTEMS FOR OPTIMIZING RETAIL OPERATIONS AND ENHANCING CUSTOMER EXPERIENCE MANAGEMENT

Purpose – The purpose of this paper is to examine how artificial intelligence-driven decision support systems contribute to optimizing retail operations and improving customer experience management in modern retail environments. The study focuses on understanding how intelligent systems enable data-driven decision-making across supply chains, customer engagement, and strategic planning.

The paper also aims to highlight how retailers can leverage these systems to achieve operational efficiency while simultaneously enhancing personalization and customer satisfaction. The growing role of AI in transforming retail processes is explored through both conceptual and analytical perspectives.

Design/Methodology/Approach – This study adopts a conceptual research design supported by secondary data analysis and synthesis of existing literature. Various academic sources, industry reports, and case-based insights are examined to understand the integration of AI in retail decision-making systems.

Additionally, analytical illustrations such as graphs and tables are used to demonstrate the impact of AI adoption on performance metrics. The approach combines qualitative insights with quantitative representation to ensure clarity and relevance.

Findings – The findings suggest that AI-driven decision support systems significantly improve demand forecasting accuracy, inventory management, and customer personalization. Retailers adopting AI technologies demonstrate higher revenue growth and improved customer retention.

Moreover, AI systems enhance real-time decision-making capabilities, allowing businesses to respond dynamically to market changes. The integration of predictive analytics and machine learning models leads to more efficient operations and improved customer engagement.

Practical Implications – The study provides actionable insights for retail managers and practitioners aiming to implement AI-driven decision support systems. It highlights the importance of investing in data infrastructure, training, and scalable AI solutions.

Retailers can use these insights to streamline operations, reduce costs, and improve customer satisfaction. The paper also emphasizes the need for ethical considerations and data privacy in AI implementation.

Originality/Value – This paper contributes by integrating operational optimization and customer experience management within a unified AI-driven decision support framework. It offers a comprehensive perspective that bridges both operational and customer-centric domains.

The inclusion of analytical representations and structured insights enhances its practical relevance for both academic researchers and industry professionals.