Large model-driven agent assist systems are emerging as a critical tool for improving customer service efficiency, with the core philosophy of “empowering” rather than “replacing.” According to McKinsey’s 2025 report, agents using AI assistance see an average productivity increase of 30-40% and a 25% reduction in employee turnover.
Latest application scenarios include real-time knowledge retrieval, conversation suggestions, emotion detection, and automatic summarization. For example, when an agent is speaking with a customer, the AI analyzes the conversation in real time, pushes the most relevant solutions from the knowledge base, and even generates suggested scripts. In insurance claims scenarios, the AI can automatically extract key information provided by the customer (such as policy numbers and accident descriptions) and populate system forms, reducing repetitive data entry for the agent.
On the technology front, models are evolving from “passive assistance” to “proactive prediction.” By analyzing historical data and real-time conversations, the system can predict the customer’s next question and prepare answers in advance. For instance, if a customer asks about billing, the system can automatically push account balances, recent transaction records, and potential dispute resolution processes.
Industry insights show that the key to successful implementation lies in a “human-machine collaboration” design. GlobalConnect’s agent assist platform uses low-code configuration, allowing enterprises to customize assistance rules based on business needs, while also providing real-time feedback mechanisms to help the model continuously improve.