Artificial Intelligence Driven Consumer Experience Transformation: Conversational Systems, Hyper-Personalization, and Ethical Governance in Digital Commerce

Abstract

The rapid diffusion of artificial intelligence across digital commerce ecosystems has fundamentally reconfigured how organizations conceptualize, design, and govern consumer experience. No longer limited to automation or operational efficiency, contemporary AI systems increasingly function as experiential infrastructures that mediate perception, decision-making, trust, and value co-creation between firms and consumers. This article develops a comprehensive, theory-driven examination of AI-enabled consumer experience transformation, with particular emphasis on conversational artificial intelligence, personalization engines, and voice-based interaction systems. Drawing strictly on the provided body of literature, the study situates recent developments within broader historical trajectories of relationship marketing, database-driven personalization, and machine learning–based decision systems. Special analytical attention is devoted to the evolving role of generative and transformation-based AI architectures in shaping adaptive, context-aware, and emotionally resonant consumer journeys.

The article advances three core arguments. First, AI-driven consumer experience must be understood as a socio-technical phenomenon rather than a purely technological one, requiring integration of cognitive psychology, service-dominant logic, and human–computer interaction theory. Second, hyper-personalization enabled by generative AI represents a qualitative shift from rule-based customization toward probabilistic, continuously learning experience orchestration, as extensively documented in recent empirical and conceptual research (Upadhyay, 2025). Third, the expansion of AI-mediated experience intensifies ethical, privacy, and bias-related risks, necessitating governance frameworks that reconcile personalization benefits with normative principles of fairness, transparency, and consumer autonomy.

Methodologically, the study adopts an integrative qualitative research design combining structured literature synthesis, comparative theoretical analysis, and interpretive abstraction. Rather than producing empirical metrics, the article generates descriptive and analytical insights grounded in peer-reviewed studies, industry research, and scholarly surveys. The findings articulate key patterns in AI-enabled consumer experience outcomes, including perceived relevance, trust calibration, emotional engagement, and long-term loyalty formation. The discussion critically evaluates competing scholarly perspectives, identifies unresolved tensions in current research, and outlines future directions for theory development and applied investigation. By offering a maximally elaborated, publication-ready contribution, this article aims to serve as a foundational reference for researchers, practitioners, and policymakers engaged in the design and governance of AI-driven consumer experience systems.