IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1405.html

From Reactive to Proactive: AI-Driven CCaaS Solutions in Predictive Customer Service

Author

Listed:
  • Vipin Kalra
  • Shaveta Arora

Abstract

Customer service is no longer the answer to a complaint but a personal, accurate, timely interaction that is expected to be highly influenced by AI. AI is being integrated into CCaaS solutions as more and more of these platforms aim to provide better ways to engage with customers while anticipating their needs and even providing personalized updates. The role of advanced intelligent CCaaS solutions in the context of predictive customer experience transformation is the focus of this paper. These areas are: shift from traditional reactive systems to proactive AI, use of Predictive Analytics, Machine Learning Models and Conversational AI for customer satisfaction. In so doing, this article outlines a comprehensive approach to a literature review and case study analyses to stress the significance of embedding AI into CCaaS for long-term competitive advantage. It calls for the adoption of predictive customer service as critical to organizations that are determined to strive in the current volatile business environment.

Suggested Citation

  • Vipin Kalra & Shaveta Arora, 2025. "From Reactive to Proactive: AI-Driven CCaaS Solutions in Predictive Customer Service," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 3653-3666, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1405
    DOI: 10.32628/CSEIT25112843
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112843
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112843
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112843/CSEIT25112843
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT25112843?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1405. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.