AI-Powered CRM Systems and Customer Experience Transformation: Balancing Hyper-Personalization, Data Privacy, and Digital Trust
TL;DR
A novel conceptual framework is developed that integrates AI-driven personalization with responsible data governance and is proposed as a foundation for future empirical validation, offering organizations practical guidance.
Abstract
Walk into any business meeting today and the same buzzwords often dominate the conversation: data, personalization, and customer experience. Yet behind these terms lies a persistent challenge. Organizations are collecting more customer data than ever before, but many continue to struggle with distinguishing between personalization that enhances customer value and practices that feel intrusive. This paper investigates how organizations implement Artificial Intelligence (AI)-powered Customer Relationship Management (CRM) systems, why many initiatives fail to deliver the expected outcomes despite substantial investments, and what differentiates successful implementations from unsuccessful ones. This study uses a qualitative secondary research design structured as a scoping review, guided by the PRISMA framework, to synthesize evidence from nine peer-reviewed empirical studies published between 2019 and 2024 and retrieved from the Dimensions AI database. The findings identify 21 Critical Success Factors (CSFs), grouped into four interconnected domains: organizational culture and capabilities, data and technological infrastructure, strategic alignment and business processes, and customer and ethical governance. The review also highlights the significant performance benefits of AI-enabled CRM, with effective real-time personalization capable of increasing customer lifetime value by more than 20%. However, the findings reveal that the primary limitation is not technological but psychological. Customers possess a threshold beyond which personalization is perceived as intrusive rather than valuable, resulting in declining trust and reduced business performance. To address this challenge, the paper develops a novel conceptual framework that integrates AI-driven personalization with responsible data governance. This framework is proposed as a foundation for future empirical validation, offering organizations practical guidance.