  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.
