Digitalisation has multiplied the media, channels, and touchpoints through which consumers and firms interact, reshaping the customer journey around digital experiences. In parallel, the rapid maturation of Machine Learning, Deep Learning, and especially Artificial Intelligence (AI), is pushing both scholars and managers to rethink how human actors engage with, rely on, and derive value from technological systems, and to revisit the design of these interactions as well as their governance.
A growing body of research shows that AI is increasingly becoming a central shopping companion across the entire customer journey, supporting consumers from product discovery and evaluation in the pre-purchase stage, to basket building and checkout at purchase, and into post-purchase activities such as customer service and returns.
Compared with traditional recommender systems, AI companions are powered by language models and conversational interfaces that do more than filter information. They can operate as interactive decision partners, engaging consumers in dialogue, adaptively tailoring support, and providing cognitive scaffolding. Through these mechanisms, AI companions can directly shape how preferences are constructed, how uncertainty is interpreted, and how choices are ultimately made.
The conversational nature of AI-mediated assistance introduces dynamics that are not yet fully understood. These include real-time persuasion and framing, the delegation of decisions to 'the system', the emergence and calibration of trust, perceived agency and controllability, the role of explanation and disclosure, and the effects of anthropomorphism. Taken together, these elements can make the interaction feel closer to an ongoing relationship than to a single, isolated touchpoint.
The literature has devoted little attention to how AI companions affect downstream outcomes that matter for both retail performance and consumer welfare. In particular, we still know too little about whether, and under what conditions, an AI companion can increase or decrease basket conversion among consumers who are already expected to purchase, or how conversational assistance may contribute to monitoring and shaping assortment status. Outcomes such as basket conversion, assortment shaping, and returns are strategically consequential for firms, platforms, and shoppers, yet they remain less systematically examined than upstream constructs such as adoption, user experience, trust, and intention-based measures.
Our understanding remains incomplete regarding the conditions under which assistant guidance improves preference-product fit and long-term value, versus when it accelerates decisions that amplify mismatch, encourage opportunistic trial behaviours, or increase operational costs through returns. This Special Issue aims to address this gap by inviting new conceptual, methodological, qualitative, and quantitative contributions that advance insight into this domain.