Purpose:  This study examines how Artificial Intelligence (AI) influences consumer trust (e-trust) in e-Commerce. It identifies key AI-driven factors, explores their interrelationships and develops a structural framework to explain how AI technologies enhance trust.

Methods: This study applies Interpretive Structural Modelling (ISM) to analyse nine AI-related variables: personalization, fraud detection, predictive analytics, transparency, recommendation accuracy, data privacy, customer support, reputation management and e-trust. Expert opinion was used to construct the VAXO matrix, followed by SSIM, reachability matrices, MICMAC analysis and a conceptual framework.

Results: The findings show that fraud detection, data privacy and predictive analytics are the strongest drivers of trust. Personalization, transparency and recommendation accuracy act as mediators, while E-trust emerges as the most dependent outcome variable. The structural model highlights how technical drivers combine with experiential factors to create consumer confidence on online shopping platforms.

Implications: This study makes both theoretical and practical contributions to the literature. For businesses, the framework offers actionable insights into building trust through transparent AI, robust fraud detection and ethical data practices. Policymakers emphasize the need for clear regulatory frameworks to protect consumer rights. It offers a foundation for future empirical studies across industries and geographies.

Originality: This study integrates multiple AI factors into a unified model of trust, providing a novel perspective on how AI strategically enhances consumer trust in e-Commerce.