Designing a Customer Experience Framework to Optimise the Impact of AI Adoption in the Retail Sector

Abstract

The use of artificial intelligence (AI) in retailing is widely recognised as a significant catalyst for operational, supply chain and consumer behavioural change. However, despite substantial investment in AI, several retail organisations are struggling to achieve the desired customer experience and firm performance. While extant literature explores AI adoption or customer experience, very few studies discuss the relationship between backend operational efficiency enabled by AI and frontend customer experience. The aim of this study is to address this research gap by proposing an overarching framework of customer experience that maximises the effect of AI adoption in the retail sector. This study adopts the socio-technical perspective and an extended Leavitt Diamond Model to conceptualise AI adoption as a change agent that affects four interconnected organisational elements, namely, people, task, technology, and structure. A mixed methods approach is employed for data collection and analysis. The quantitative survey data from 390 retail employees and consumers were analysed using SPSS and Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the inter-relationships between AI adoption and some organisational and customer-related constructs. In addition, semi-structured interviews with retail managers and employees were conducted to gather insights into AI adoption implementation, operational challenges and customer experience. The reflexive thematic analysis is employed to analyse the qualitative data and triangulate with the quantitative data. The results suggest that AI adoption has a significant impact on customer purchasing behaviour, inventory management efficiency, employee productivity, shopping and task efficiency and customer experience. However, implementation, data integration and security are major constraints to successful AI adoption. Moreover, this study confirms that backend operational efficiency has a positive relationship with customer experience, which suggests that organisational efficiency plays a crucial role in enhancing customer experience. This study contributes to the extant literature primarily by extending the Leavitt Diamond Model within the context of AI adoption in the retail sector. The findings provide empirical evidence linking v

AI-enabled operational efficiency to customer experience and demonstrate how the interdependent dimensions of structure, people, technology, and tasks shape AI-enabled organisational transformation. In addition, the study develops an evidence-informed practical AI adoption framework to support retail organisations in implementing AI initiatives that enhance both operational performance and customer experience. The study suggests that customer experience should be configured as an integrated framework of interdependent constructs. The framework can be used by practitioners as a tool to evaluate their readiness for AI adoption and manage the challenges associated with AI adoption. Moreover, the framework will help retail decision makers to ensure that AI adoption contributes to sustainable value for both organisations and their customers. This study argues that AI adoption should be considered as an organisation-wide initiative rather than a technology only solution. Future studies may investigate the longitudinal effect of AI adoption, governance and ethical issues in AI-enabled retailing and apply the proposed framework in other service industries.