Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents.

翻译：随着产品与服务数字化互联程度的加深，企业不断寻求新方案以提供客户支持并提升满意度。为保持竞争力，企业需提供或外包快速、高效且专业的客户支持，这已成为内在需求。支持解决方案亦随技术同步演进，涵盖社交媒体应用、人工智能、机器学习及远程设备连接等技术，旨在更好地服务客户。客户支持人员经过培训，可运用这些技术拓展客户触达范围，并为偏远地区客户提供支持。产品与支持系统的互联互通，使企业得以拓展国际市场，扩大产品市场与业务规模。本文报告了人工智能在客户支持中的潜在应用，该研究依据伯明翰城市大学与一家跨行业客户服务系统外包企业间的知识转移伙伴计划（KTP）合作开展。本研究探索了多种基于标注与非标注文本数据准确预测客户意图的方法。尽管部分方法在特定数据集中展现出潜力，但寻找单一普适性方法的探索仍在继续。针对意图检测与发现分别构建独立流程，虽提升了已知意图的检测准确率，但未知意图的发现准确率仍需进一步优化。