Exploring Knowledge Management Drivers in AIPowered CRM: A Conceptual Framework for Marketing Practitioners

Purpose: This study investigates the knowledge management (KM) drivers of Artificial ‎Intelligence-powered Customer Relationship Management (AI-CRM) systems. It explores ‎how organisational culture, learning processes, and individual characteristics affect ‎knowledge seekers and marketer’s conduct in acquiring or using knowledge and marketing ‎practices.‎ Design/methodology/approach: The study used KM, Social Cognitive Theory, and the ‎Technology Acceptance Model. This study provides a conceptual framework for KM and AI-‎CRM. Data were collected from a sample of 302 marketing professionals (94% response rate) ‎across the IT, retail, healthcare, education, and e-commerce sectors in India using a structured ‎questionnaire. A purposive sampling approach was used, and Partial Least Squares Structural ‎Equation Modelling (PLS-SEM) was used to examine the direct and moderating effects.‎ Findings: The results indicate that learning mechanisms and individual characteristics have ‎significant impacts on marketing KM practices in AI-CRM environments. In contrast, ‎organisational culture alone cannot directly influence KM, but it can when moderated by ‎individual characteristics. These results indicate that factors related to structured training and ‎individual adaptability, rather than cultural factors, play a more decisive role in promoting AI-‎CRM adoption.‎ Research limitations/implications: This study has several limitations, including its cross-‎sectional design, geographic setting (North India), and reliance on self-reports. Future ‎research should consider longitudinal designs and include more industries and regions to ‎improve generalizability.‎ Practical implications: Managers and policymakers should focus on targeted training, ‎experiential learning, mentorship schemes, and the creation of psychologically safe spaces for ‎knowledge sharing. Investments in technological and human capacity are key to fully ‎exploiting AI-CRM results.‎ Originality/value: This study contributes to the existing literature on AI-CRM by generalizing ‎the (Jamil et al., 2025; Nonaka et al., 2000) SECI model to AI-enabled customer relationship ‎management environments, which in turn integrates the knowledge creation process with ‎generative automation. The study empirically validated the PLS SEM framework, providing ‎substantive evidence to support marketers’ decision-making in emerging markets. The study ‎also emphasizes the role of individual readiness as a moderating construct in increasing the ‎effectiveness of AI adoption‎.