Factors influencing the adoption of Generative AI for customer service in the South African banking sector

Abstract

AI technology has been identified as a technology that can transform how companies engage with their customers, offering novel opportunities for customization, streamlining operations, and enhancing results. Generative AI technology has taken this notion a nudge up through its transformative ability to create new and meaningful content such as text, images, and audio, leveraging advanced algorithms and neural networks trained on extensive datasets to generate synthetic data closely resembling real-world data. This study explores the effective integration of Generative AI (GenAI) technologies in South African banks to enhance the functions of Relationship Managers (RMs) to help them deliver superior customer service. With the banking industry facing increasing customer expectations, digital transformation pressures, and stringent regulatory demands, the research investigates GenAI's potential to transform relationship management by automating routine tasks, providing real-time insights, and enabling personalised, data-driven customer engagement. A qualitative research approach through the case study method and an interpretative research paradigm was followed to examine participants' perspectives across various roles including relationship managers, architects, technical leads, product and solution owners and senior executives in the banking sector. The data collection method followed was semi-structured interviews conducted using online capabilities. The data was analysed using both deductive and inductive methods which resulted in additional findings. Key findings highlight GenAI's capacity to enhance customer service through hyper-personalisation, operational efficiency, and proactive decision-making. Practical applications, including task automation, compliance facilitation, and advanced financial advisory, were identified, highlighting the broad applicability of GenAI in banking. However, challenges such as data quality, system integration, regulatory compliance, and organisational readiness emerged as critical barriers to the adoption of this technology. iii The research contributes to the field by expanding the existing literature, particularly by offering practical strategies to address legacy system constraints, align technological advancements with human expertise, and foster organisational readiness. Recommendations for banks include strengthening data governance, investing in employee training, addressing regulatory gaps by engaging and partnering with policymakers to shape relevant AI regulations, and adopting phased implementation strategies to balance automation with human oversight. This study also outlines directions for future research, such as exploring GenAI's ethical dimensions, its long-term impact on workforce dynamics, and the development of regulatory frameworks. The findings underscore GenAI's transformative potential in banking, offering actionable insights for leveraging this technology to enhance relationship management and drive innovation in a rapidly evolving industry.