AI-Driven Customer Experience: Program Management Challenges in Retail

Abstract

Artificial intelligence is fundamentally reshaping customer experience in the retail sector, enabling unprecedented personalization, predictive capabilities, and intelligent automation that increasingly define competitive differentiation. However, translating AI's theoretical potential into tangible business value presents formidable program management challenges that extend well beyond technical implementation. This article explores the multifaceted obstacles retailers face when deploying AI-driven customer experience initiatives, drawing on case insights from large-scale transformations and established program management theory to develop a comprehensive framework for practitioners. The article reveals that successful AI-CX programs demand careful orchestration across five critical domains: establishing robust data governance and quality standards, achieving seamless technology integration with legacy infrastructure, managing organizational change and stakeholder alignment, ensuring ethical compliance and responsible AI practices, and demonstrating measurable value through customer-centric metrics and continuous improvement. Through examination of real-world implementations, including omnichannel personalization platforms and conversational AI deployments, the article identifies common failure patterns stemming from underestimated data preparation efforts, insufficient change management, unrealistic stakeholder expectations, and inadequate governance structures. The proposed AI-CX Program Management Framework addresses these challenges by providing program managers with actionable guidance that balances technical execution with human factors, integrates ethical considerations throughout program lifecycles, and maintains unwavering focus on customer outcomes rather than merely technical deliverables. Findings emphasize that program managers must develop distinctive capabilities, including AI literacy, cross-domain leadership, strategic translation between business and technical stakeholders, and ethical advocacy, to successfully navigate AI's socio-technical complexity. This article contributes to both program management theory and digital transformation scholarship by demonstrating how traditional frameworks require extension and adaptation for AI contexts while advancing understanding of how organizations can operationalize responsible AI principles within practical program constraints facing real business pressures and resource limitations.