AI-Capable Relationship Marketing: Shaping The Future Of Customer Relationships

AI is no longer just a tool, it's a transformational capability. This study reveals how firms can utilize AI to detect market shifts, personalize their outreach, and foster lasting customer relationships.

Research: AI-capable relationship marketing: Shaping the future of customer relationships. Image Credit: FON's Fasai / Shutterstock

A new study in the Journal of Business Research explores the interlinkages between artificial intelligence (AI), dynamic capabilities, and relationship marketing (RM) outcomes. Drawing on insights from dynamic capabilities and resource-based view (RBV) theory, this study outlines the strategies and initiatives that organizations can adopt using machine learning (ML) and artificial intelligence (AI) to enhance their adaptability to changing market dynamics and customer preferences, thereby developing and maintaining stronger customer relationships. Based on qualitative data from 67 interviews with managers in different organizations in India, an emerging economy context chosen to highlight the unique challenges, resource constraints, and rapid digital transformation opportunities faced by developing markets, this study contributes to existing theoretical knowledge and managerial practices, as it proposes a comprehensive research framework that demonstrates how AI technologies can enhance customer relationships throughout the entire customer journey.

More specifically, it adopts a dynamic capabilities lens to extend our understanding of the marketing applications of AI by conceptualizing the dual role of AI as (a) a distinct organizational capability and (b) an enabler of dynamic capabilities, improving firms' position to sense, seize, and transform organizational resources and fortify customer relationships. The study identifies three core components of AI-enabled dynamic capabilities (AI-DC): market sensing through analytical expertise and data management; opportunity seizing via decision agility and resource optimization; and organizational transformation through digital initiatives, talent development, and collaborative ecosystems.

Our findings also highlight several facilitators and barriers to the adoption of AI, both as a dynamic capability and as an enabler for RM. Key facilitators include human oversight and AI knowledge, which support the ethical and practical implementation. Major barriers, on the other hand, include ethical concerns, data bias, integration difficulties, and trust issues among employees and customers.

The study also introduces a conceptual model of AI-enabled relationship marketing (AI-RM), which is structured into three sequential yet interconnected phases: establishing, developing, and maintaining customer relationships. AI technologies support customer outreach, segmentation, personalized experiences, loyalty initiatives, and real-time support. The study further outlines firm- and customer-level outcomes, including enhanced loyalty, cost efficiencies, and improved customer experience. At the firm level, benefits include an improved brand image, increased conversion rates, and reduced operational costs. At the customer level, outcomes include higher satisfaction, better engagement, and a stronger emotional connection to the brand.