This paper introduces artificial intelligence(AI) optimization algorithms to improve the scientificity and rationality of staffing and solve the inefficiency and inaccuracy of traditional staffing. By building a multidimensional evaluation model, a data-driven decision-making system, and a dynamic planning mechanism, the system can respond to changes inside and outside the organization in real time to ensure optimal human resource(HR) allocation and utilization. First, this paper establishes a data collection platform to collect multidimensional data, such as employee personal data, job requirements, and historical performance, and preprocesses them. Subsequently, this paper uses machine learning algorithms to model employees’ skills and adaptability, build an intelligent matching system, and optimize resource allocation. Finally, by incorporating deep learning techniques, a dynamic programming model is developed to adapt to worker allocation based on real-time changes and ensure efficient use of resources. Based on the proposed approach, the system allows for real-time analysis and adjustment of the workforce, improves the accuracy of worker matching, and optimizes productivity. Experimental results show that the system reduces resource usage time by 20% in practice compared with traditional configuration methods. The AI optimization configuration system introduced in this paper effectively solves the loopholes in traditional labor configuration, has high adaptability and real-time response capabilities, and can provide a more accurate basis for enterprise labor planning decisions.