This study empirically investigates the adoption of Artificial Intelligence-enabled Customer Relationship Management (AI-CRM) systems in Business-to-Business (B2B) marketing, focusing on key determinants, implementation outcomes, and performance effects. Based on theoretical integration of the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), structural relationships among technology readiness, perceived usefulness, behavioural intention, and B2B marketing performance are tested. Data were collected from B2B firms implementing AI-CRM solutions and analyzed using structural equation modeling (SEM).

This study contributes to the AI-CRM literature by: 1.Demonstrating how TAM and RBV jointly explain AI-CRM adoption. 2.Providing empirical evidence on adoption determinants and performance effects in B2B marketing. 3.Highlighting the moderating effect of leadership support on performance. Future research should explore longitudinal outcomes of AI-CRM adoption and integrate qualitative insights into user experience. Firms embracing AI-enabled CRM systems with adequate readiness and support frameworks can achieve superior customer relationship outcomes and sustained competitive advantage.

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