Research on the Transformation Mechanism, Challenges, and Development Path of AI Empowering the Logistics Industry

Abstract

Against the background of the integration of the digital economy and industrial intelligence, AI technology has become the core support for the logistics industry to reduce costs, improve efficiency, and break through development bottlenecks. This paper constructs a research framework of “Application Scenarios–Transformation Mechanism–Challenges–Development Path,” systematically analyzing the application value and practical issues of AI in the logistics industry. The core applications of AI are concentrated in three scenarios: intelligent customer service, logistics data analysis and decision optimization, and intelligent inventory management. Through process automation replacement, service model upgrading, and data-driven decision-making, it achieves a systematic transformation of industry operational efficiency improvement, customer experience optimization, and decision-making model transformation. At the same time, AI applications still face practical challenges such as insufficient technical integration compatibility, data security and privacy protection risks, and talent structure adaptation gaps. In the future, the logistics industry needs to fully release the value of AI technology by deepening the integration of AI with cross-technologies, promoting green and flexible development.