The Influence of Artificial Intelligence on the Automation of Processes in Electronic Commerce

This study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. The research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. The application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. This research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.