https://ijmras.com/index.php/ijmras <p><strong>ISSNe 2640 -7272</strong><br /><strong>Impact Factor:-6.0</strong><br /><strong>Cross-ref / DOI:- 10.33826/ijmras</strong><br /><strong>Elsvior/ Mendeley / DOI :- 10.17632</strong><br /><strong>Call For Paper Volume 07 Issue 05 May 2024</strong></p> <p><strong><img src="https://ijmras.com/public/site/images/ijmras/open-access-logo-png-transparent-d26c9b4ffbfff319bc5c9d0c74a1a3d7.png" alt="" width="250" height="100" /><br /></strong></p> Steven Sayasy en-US International Journal of Multidisciplinary Research and Studies 2640-7272 <p><span style="color: rgba(0, 0, 0, 0.87); font-family: 'Noto Serif', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 13.02px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #faebd7; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">Individual articles are published Open Access under the Creative Commons Licence: </span><a style="box-sizing: border-box; background-color: #faebd7; color: #4b7d92; font-family: 'Noto Serif', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 13.02px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px;" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a><span style="color: rgba(0, 0, 0, 0.87); font-family: 'Noto Serif', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 13.02px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: #faebd7; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">.</span></p> https://ijmras.com/index.php/ijmras/article/view/851 <p>The rapid development of artificial intelligence (AI) has significantly changed the way organizations manage customer relationships, communicate with consumers, and deliver personalized services. Traditional customer relationship management (CRM) systems primarily focused on storing customer information, tracking interactions, and supporting sales and service activities. The integration of AI has expanded these capabilities by enabling organizations to analyze large volumes of customer data, predict customer needs, automate interactions, personalize recommendations, and provide real-time support. As a result, AI-driven CRM has become an important strategic capability for organizations seeking to improve customer experience and strengthen customer loyalty. This study examines the influence of AI-driven customer relationship management on customer experience and customer loyalty. The study develops a conceptual model in which AI-driven CRM is considered an antecedent of customer experience, while customer experience is expected to contribute to customer loyalty. The study draws upon previous research on AI in marketing, CRM, customer experience, technology adoption, service quality, and relationship marketing. A quantitative research design is proposed, using a structured questionnaire to collect data from customers who have interacted with AI-enabled CRM technologies such as chatbots, virtual assistants, recommendation systems, personalized marketing platforms, and automated customer service systems. The proposed analysis uses reliability and validity assessment followed by Partial Least Squares Structural Equation Modeling (PLS-SEM). The conceptual framework proposes that AI-driven CRM improves customer experience through personalization, responsiveness, convenience, predictive service, and interaction quality. Improved customer experience is expected to strengthen customer loyalty by increasing customers' willingness to repurchase, remain with an organization, and recommend its products or services. The study also discusses the importance of balancing technological automation with human interaction, transparency, privacy, and customer trust. The study contributes to the growing literature on AI-enabled CRM by highlighting customer experience as an important mechanism connecting AI capabilities with long-term customer relationship outcomes.</p> Dr. Danil M. Carter M. Carter Copyright (c) 2026 Dr. Danil M. Carter M. Carter https://creativecommons.org/licenses/by/4.0 2026-10-02 2026-10-02 9 10 01 12