IDEAS home Printed from https://ideas.repec.org/a/bhx/ojijce/v7y2025i8p21-50id2939.html
   My bibliography  Save this article

Intelligent Revenue Operations Platform Using AI, NLP, and Machine Learning

Author

Listed:
  • Laxmi Narayana Chejarla

Abstract

The intelligent revenue operations platform integrates artificial intelligence, natural language processing, and machine learning to transform fragmented business processes into cohesive, automated workflows. This platform addresses critical challenges in lead management, quote-to-cash processes, and compliance requirements by implementing autonomous company research, intelligent classification algorithms, and adaptive workflow automation. Upon identifying a new lead, the system performs comprehensive research across multiple sources, conducts sophisticated profile analysis, determines appropriate industry classifications, and evaluates transaction behaviors—all without manual intervention. The architecture extends through the entire revenue lifecycle, automating quoting, contract generation, order processing, and revenue recognition while maintaining regulatory compliance. Implementation experiences across SaaS and manufacturing industries demonstrate significant efficiency improvements, enhanced decision-making capabilities, and measurable financial benefits. The system's modular design and integration methodology enable adaptation to diverse organizational contexts while addressing data quality, system integration, and change management challenges

Suggested Citation

  • Laxmi Narayana Chejarla, 2025. "Intelligent Revenue Operations Platform Using AI, NLP, and Machine Learning," International Journal of Computing and Engineering, CARI Journals Limited, vol. 7(8), pages 21-50.
    • Handle: RePEc:bhx:ojijce:v:7:y:2025:i:8:p:21-50:id:2939
    as

    Download full text from publisher

    File URL: https://carijournals.org/journals/index.php/IJCE/article/view/2939
    Download Restriction: no
    ---><---

    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:bhx:ojijce:v:7:y:2025:i:8:p:21-50:id:2939. 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: Chief Editor (email available below). General contact details of provider: https://www.carijournals.org/journals/index.php/IJCE/ .

    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.