IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i4id1341.html

AI-Driven Predictive Analytics for Workforce Planning and Optimization

Author

Listed:
  • Nishitha Reddy Nalla

Abstract

Predictive analytics powered by AI is revolutionizing the way organizations plan and optimize their workforce. Through the use of big data, machine learning and AI technologies, organizations can forecast workforce requirements, improve talent management techniques, and drive operational effectiveness. In recent years, the integration of AI-driven predictive analytics in workforce planning and optimization has transformed the way organizations manage talent, enabling them to adapt quickly to changing demands. It also explores the future of AI in human resource management, offering guidance on the 'Innovation Roadmap' to help organizations as they evolve.

Suggested Citation

  • Nishitha Reddy Nalla, 2024. "AI-Driven Predictive Analytics for Workforce Planning and Optimization," 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. 10(4), pages 349-353, August.
  • Handle: RePEc:jbh:ijsrcs:v10:y2024:i4:id:1341
    DOI: 10.32628/CSEIT2477112
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2477112
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT2477112
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT2477112/CSEIT2477112
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT2477112?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:v10:y2024:i4:id:1341. 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.