Leveraging Artificial Intelligence Agents to Drive Customer Experience in the Realm of AI Agents in the Retail Industry in India

Abstract

India's retail industry faces a paradox: digital marketing tools are booming, yet the customer experience at the point of purchase in-store remains largely disconnected from them. Despite their wide distribution, digital coupons suffer from persistently low
redemption rates: shoppers encounter them as buried emails or forgotten notifications rather than as the useful, valuable tools they are meant to be. The use of Artificial Intelligence beyond store-based information-dispensing tools or enhancements to the instore shopping experience remains unexplored, especially in emerging markets. This research examined the factors driving the adoption of an AI agent that helps retrieve relevant coupons from their emails on demand at in-store checkout, thereby enhancing the in-store shopping experience for shoppers in India. It also studied whether such an agent could reshape shoppers’ attitudes toward digital coupons as a category. An extended Stimulus-Organism-Response framework served as the theoretical
foundation, integrating constructs from the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Value-Based Adoption Model (VAM). A mixed-methods exploratory sequential design was used.
Qualitative interviews with shoppers were conducted first to extract contextual and attitudinal themes, followed by a structured online quantitative study of urban Indian shoppers.
Findings indicated that ‘Attitude toward AI’ and ‘Social Influence’ were the strongest predictors of adoption intention, followed by perceived ease of use, perceived usefulness, and risk perception, suggesting that AI adoption in the Indian retail context is shaped less by functional utility and more by value-based and relational evaluations. Unlike established studies, the demographic analysis revealed that AI exposure variables, such as occupation, have a greater impact on behavioral intention towards artificial intelligence agents than socioeconomic variables. Additionally, the findings indicate that AI agent adoption is a
value-exchange negotiation in which shoppers weigh perceived benefit against perceived risk. More importantly, a significant spillover effect established that AI agents serve as ‘perception accelerators’ and ‘category rehabilitators’ for the underlying tasks or tools they assist with.
For retailers, marketers, and AI product designers operating in emerging markets like India, these results recommend a design centered on the trustworthiness of the agent and security of handling their data, socially anchored marketing strategies, and the use of AI agents as instruments of perceptual change towards the underlying tools and tasks they support.