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

Laxmi Narayana Chejarla ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 8, 21 - 50

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

Keywords: Revenue Operations Automation; Artificial Intelligence; Natural Language Processing; Lead Intelligence; Compliance Automation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://carijournals.org/journals/index.php/IJCE/article/view/2939 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bhx:ojijce:v:7:y:2025:i:8:p:21-50:id:2939

Access Statistics for this article

More articles in International Journal of Computing and Engineering from CARI Journals Limited
Bibliographic data for series maintained by Chief Editor ().