Exploring the Impact of Artificial Intelligence on Predictive Analytics and Operational Efficiency in E-Commerce

This study explores the transformative impact of Artificial Intelligence (AI) on predictive analytics and operational efficiency in the E-commerce sector. The primary objectives were to assess AI's role in enhancing demand forecasting, decision-making, and process optimization, identify the benefits and challenges of AI adoption, and provide actionable recommendations for businesses, policymakers, and technology developers. Utilizing a mixed-methods approach, the research gathered quantitative and qualitative data from 462 participants across various organizational sizes and regions, including AI practitioners, operational managers, and data analysts. The findings reveal that AI significantly enhances predictive analytics by improving demand forecasting accuracy, enabling data-driven decisions, and enhancing customer personalization. Regression analysis demonstrated that AI adoption levels explained 46% of the variance in forecasting accuracy, emphasizing its critical role in operational strategies. AI also drives operational efficiency by automating processes, reducing errors, and achieving cost savings, with advanced adopters reporting substantial gains in process speed and inventory management.