Original Research

Enabling customer service training with an artificial intelligence-technology chatbot

Raeesah Mc Niel, Errol R. Francke
South African Journal of Information Management | Vol 28, No 1 | a2108 | DOI: https://doi.org/10.4102/sajim.v28i1.2108 | © 2026 Raeesah Mc Niel, Errol R. Francke | This work is licensed under CC Attribution 4.0
Submitted: 05 October 2025 | Published: 10 July 2026

About the author(s)

Raeesah Mc Niel, Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa
Errol R. Francke, Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa

Abstract

Background: The popularisation of artificial intelligence (AI) has accelerated its integration and use across various sectors for multiple purposes. Artificial intelligence technologies have also been applied to corporate operations to assist with handling customer inquiries. However, there remains a limited understanding of how these tools influence customer service capability and training, particularly within small- and medium-sized enterprises (SMEs) in developing contexts.
Objectives: 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, and establishes a framework to support the effective implementation and integration of AI tools to enhance customer service training.
Method: The study adopted a qualitative approach, employing the case study design. The data were collected using semi-structured interviews with participants from a purposive sample.
Results: 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. Challenges included the limited content depth of the responses and technical infrastructure constraints that questioned organisational readiness and strategic direction.
Conclusion: The findings highlight both the benefits and limitations of using AI-enabled tools in training environments. Despite this, human oversight and strategic infrastructure planning remain critical for successful adoption.
Contribution: This study presents the ABIRM framework developed to guide SMEs in the implementation and adoption of AI tools for training purposes.


Keywords

artificial intelligence; chatbots for training; customer service; workplace learning; implementation science framework

JEL Codes

O31: Innovation and Invention: Processes and Incentives; O32: Management of Technological Innovation and R&D; O33: Technological Change: Choices and Consequences • Diffusion Processes

Sustainable Development Goal

Goal 9: Industry, innovation and infrastructure

Metrics

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