生成式人工智能在电商客服中的应用困境与解决路径

The Challenges and Solutions of Applying Generative Artificial Intelligence in E-Commerce Customer Service

摘要: 生成式人工智能在近年迅速突破原有技术边界，以语言模型、知识增强系统和多模态生成架构为核心的技术体系使电商客服的自动化与智能化达到前所未有的高度。在问答生成、情绪识别、意图理解、场景推理与知识提取等方面生成式人工智能均展现出显著优势，能够在高并发场景下提升客服效率、降低企业成本，并在一定程度上改善用户体验。然而，随着模型在电商业务环境中深入应用，其幻觉频发、伦理风险难以控制，成本持续居高不下等问题逐渐显现，且呈现高度场景化特征。需要从技术与管理并重的视角出发，分析生成式人工智能在电商客服中的应用逻辑与现实阻滞，构建一套系统化的解决路径框架，以支持电商企业实现智能客服的可持续部署。研究发现，生成式人工智能应用的核心不在于模型本身的智能程度，而在于企业如何将其纳入组织制度与服务流程之中，使其成为与人协同的结构性力量而非单纯技术替代。最终构建起可信赖的智能客服体系，实现发展与安全并重的人工智能发展。

Abstract: Generative artificial intelligence has rapidly surpassed the original technological boundaries in recent years. Technology systems centred on language models, knowledge-enhanced systems, and multimodal generation architectures have enabled unprecedented levels of automation and intelligence in e-commerce customer service. Generative AI demonstrates significant advantages in areas such as question-answer generation, emotion recognition, intent understanding, scenario reasoning, and knowledge extraction, improving customer service efficiency under high concurrency scenarios, reducing corporate costs, and partially enhancing user experience. However, as models are increasingly applied in the e-commerce business environment, issues such as frequent hallucinations, difficult-to-control ethical risks, and persistently high costs are gradually emerging, often exhibiting highly scenario-specific characteristics. It is necessary to analyse the logic and practical obstacles of generative AI in e-commerce customer service from a perspective that values both technology and management, and to establish a systematic framework of solutions to support e-commerce enterprises in achieving sustainable deployment of intelligent customer service. Research indicates that the core of generative AI applications lies not in the intelligence of the model itself, but in how enterprises integrate it into organisational systems and service processes, transforming it into a structural force that collaborates with humans rather than a mere technical substitute. Ultimately, a reliable intelligent customer service system will be established, achieving balanced development of artificial intelligence with both growth and security.