Developing and Validating an AI-Augmented Strategic Account Management Framework for Customer Lifetime Value and Revenue Growth: A PLS-SEM Approach

AI-augmented account management; customer lifetime value; revenue growth; PLS-SEM; strategic account management; predictive analytics

This study develops and validates an AI-Augmented Strategic Account Management (AI-SAM) framework designed to enhance Customer Lifetime Value (CLV) and revenue growth in B2B contexts. Drawing on resource-based view and dynamic capabilities theory, the framework integrates AI capabilities (predictive analytics, personalization engines, and automated insights) with strategic account management processes (account prioritization, cross-selling orchestration, and relationship governance). Data from 412 mid-to-senior account managers and CRM specialists across technology, manufacturing, and professional services firms were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate that AI capabilities exert a strong positive effect on strategic account management effectiveness (β = 0.487, p < 0.001), which in turn significantly elevates CLV (β = 0.412, p < 0.001) and revenue growth (β = 0.368, p < 0.001). The model explains 54.3% of the variance in CLV and 47.8% in revenue growth. Mediation analysis confirms that strategic account management fully mediates the AI–CLV relationship (indirect effect = 0.201, 95% CI [0.147, 0.268]). Firms in the high-AI-adoption quartile reported 28.6% higher average CLV and 19.4% greater year-over-year revenue growth relative to low-adoption peers. The validated framework provides managers with an empirically grounded roadmap for embedding AI into account-level strategies to drive sustainable value creation.