IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1221.html

The Transformative Impact of AI on Modern Product Management

Author

Listed:
  • Srinivas Sriram Mantrala

Abstract

The integration of artificial intelligence into product management represents a fundamental shift in how organizations develop, market, and maintain digital products. This comprehensive article explores how AI technologies are transforming product management across multiple dimensions. It examines AI's capacity to revolutionize decision-making through enhanced data intelligence, enabling product teams to uncover insights from vast datasets that would remain hidden to human analysis alone. The article investigates how AI facilitates unprecedented personalization at scale, allowing for tailored user experiences that significantly improve engagement and retention. It further explores predictive analytics capabilities that shift product development from reactive to proactive approaches, forecasting user needs before they are explicitly articulated. Through examination of specific applications like A/B testing enhancement, feature prioritization, churn prediction, and market intelligence, the article demonstrates AI's practical impact on product management workflows. Finally, the article addresses the emerging revolution of generative AI technologies that are creating new possibilities for rapid prototyping, content creation, stakeholder communication, and innovative problem-solving in product development.

Suggested Citation

  • Srinivas Sriram Mantrala, 2025. "The Transformative Impact of AI on Modern Product Management," 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 1555-1562, March.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1221
    DOI: 10.32628/CSEIT25112507
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112507
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112507
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112507/CSEIT25112507
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT25112507?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:1221. 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.