This paper proposes a human-machine interface framework called MI-CHMI, designed for enterprise customer service scenarios. By integrating card-based layouts with large-model AI-assisted capabilities, the framework aims to improve the efficiency of multi-threaded task processing and user experience of customer service representatives. The MI-CHMI framework consists of four configurable areas: navigation, conversation queue, chat area, and AI assistance. Each area can be flexibly configured according to users’ personalized needs, including card size and position adjustment, customization and sorting of functions to meet the diverse needs of customer service representatives in different industries. Researchers built a high-fidelity interface for simulated testing and compared with the traditional HMI of the Alibaba Cloud Tongyi Xiaomi customer service workspace. Preliminary results demonstrate that the framework significantly improves the efficiency of multi-threaded task processing, enhances customer satisfaction, and facilitates the integration of additional AI-assisted capabilities in the customer service domain. Overall, this framework provides an effective interface design, which helps customer service representatives access relevant information more efficiently when handling multi-threaded tasks, and helps them provide more efficient and accurate services, thereby improving the customer experience. Future work will focus on developing and implementing additional enterprise customer service functionalities within this framework to tackle a broader range of customer service challenges in the commercialization process of enterprises.