Artificial intelligence (AI) technology is powerfully promoting the intelligence of public management services, standardization of processes, and digitalization of systems. In the field of centralized drug procurement, some regions have conducted preliminary explorations in areas such as qualification review, drug listing, price regulation, intelligent Q&A, price monitoring, and even full-process supervision. AI technology has demonstrated significant value in improving service quality, enhancing process efficiency, increasing transparency, optimizing costs, and assisting in monitoring. However, it also faces challenges in four areas: model computing power requirements, industry data aggregation, data security and ownership, and regional applicability of models. AI applications in centralized drug procurement can be promoted from perspectives such as prospective study, national guidance, innovation-driven development, continuous iteration, and shared collaboration, helping to build a fairer, more efficient, and more sustainable drug procurement system.