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Enabling customer service training with an AI-technology chatbot
Author(s)
Mc Niel, Raeesah
Date Issued
2025
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
The popularisation of Artificial Intelligence (AI) has accelerated its integration and use across
various sectors for multiple purposes, particularly within the education sector. AI technologies,
such as AI-enabled chatbots, have also been applied to corporate operations to assist with
handling customer inquiries. However, the application of AI technologies to facilitate cross pollination between the educational and corporate sectors for staff training remains
underexplored. There is limited empirical evidence indicating the effects of AI in addressing
training needs to improve customer service standards in small to medium-sized enterprises
(SMEs). Despite the potential AI offers for information transfer and automation, its use in
learning and development within SMEs presents opportunities for emerging research. Guided
by the Implementation Science framework as a theoretical lens, this study investigates how AI
support tools influence customer service capability, the factors affecting the diffusion of such
innovations, and establishes a framework to support the effective implementation and
integration of AI tools to enhance customer service training. This study aims to bridge the gap
by examining the effects of an AI-enabled chatbot prototype in customer service training within
an SME in the South African context. The study employed a qualitative approach, utilising a
case study design. The data were primarily collected using semi-structured interviews with
participants from a purposive sample.
The study revealed that AI-enabled chatbots improved information accessibility, offered
personalised learning opportunities, facilitated self-paced and adaptive learning, provided
consistency in responses, enhanced operational efficiency among customer service agents,
and contributed to teamwork and engagement. Findings suggest that the AI tool is most
effective when used in conjunction with human facilitation. However, highlighted challenges
included the limited depth of content in the responses and technical infrastructure constraints
that questioned organisational readiness and strategic direction. Therefore, this study presents
the ABIRM Framework, which was developed to guide SMEs in implementing and adopting AI
tools for training purposes.
various sectors for multiple purposes, particularly within the education sector. AI technologies,
such as AI-enabled chatbots, have also been applied to corporate operations to assist with
handling customer inquiries. However, the application of AI technologies to facilitate cross pollination between the educational and corporate sectors for staff training remains
underexplored. There is limited empirical evidence indicating the effects of AI in addressing
training needs to improve customer service standards in small to medium-sized enterprises
(SMEs). Despite the potential AI offers for information transfer and automation, its use in
learning and development within SMEs presents opportunities for emerging research. Guided
by the Implementation Science framework as a theoretical lens, this study investigates how AI
support tools influence customer service capability, the factors affecting the diffusion of such
innovations, and establishes a framework to support the effective implementation and
integration of AI tools to enhance customer service training. This study aims to bridge the gap
by examining the effects of an AI-enabled chatbot prototype in customer service training within
an SME in the South African context. The study employed a qualitative approach, utilising a
case study design. The data were primarily collected using semi-structured interviews with
participants from a purposive sample.
The study revealed that AI-enabled chatbots improved information accessibility, offered
personalised learning opportunities, facilitated self-paced and adaptive learning, provided
consistency in responses, enhanced operational efficiency among customer service agents,
and contributed to teamwork and engagement. Findings suggest that the AI tool is most
effective when used in conjunction with human facilitation. However, highlighted challenges
included the limited depth of content in the responses and technical infrastructure constraints
that questioned organisational readiness and strategic direction. Therefore, this study presents
the ABIRM Framework, which was developed to guide SMEs in implementing and adopting AI
tools for training purposes.
Additional information
Thesis (Master of Information and Communication Technology)--Cape Peninsula University of Technology, 202
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