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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67550
Volume:
10 |
9 |
Sep 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Artificial Intelligence and Data Science
Priyadarshini Collage of Engineering , Nagpur
Most CRM platforms today are still fundamentally passive. They store customer records well enough, but someone still has to dig through them manually to find anything useful. That is starting to change now that Large Language Models, Retrieval-Augmented Generation (RAG), and conversational AI have matured enough to sit on top of enterprise data and answer questions in plain language. This paper reviews 14 peer-reviewed IEEE publications from 2024, 2025 and 2026, covering four overlapping areas: AI-driven CRM analytics, enterprise knowledge management, RAG/LLM-based assistants, and conversational AI in customer service. For each paper, we looked at the problem the authors were trying to solve and what they actually claimed to achieve. One gap kept turning up throughout: nobody has really combined structured CRM data with a conversational assistant that can answer company-specific questions grounded in real internal knowledge. Addressing that gap is exactly what the proposed AI-Powered Enterprise CRM with Intelligent Company Knowledge Assistant sets out to do, and the paper closes by laying out what such a system stands to gain in productivity, decision speed, and customer engagement.
KEYWORDS
Across the 14 IEEE papers reviewed here, spanning AI-driven CRM, knowledge management, RAG/LLM assistants, and conversational AI, one thing kept coming up: each of these areas has matured on its own, but nobody has unified them yet. There is no existing system that pairs structured CRM data with a conversational, company-wide knowledge assistant. That is the gap the proposed AI-Powered Enterprise CRM with Intelligent Company Knowledge Assistant is built to close, by combining predictive CRM analytics with a RAG-based conversational layer grounded in the organization's own data. If it works as intended, it should translate into faster operations, quicker decisions, and better customer engagement overall. What remains is the implementation itself: building the system, testing how accurate the retrieval actually is, and running real user-experience testing in a live enterprise setting.
1. I. A. K. Shaikh et al., “Transforming Customer Relationship Management (CRM) with AI in E-Commerce,” 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), 2024, doi: 10.1109/ICRTCST61793.2024.10578449. 2. “Strategic Integration of Artificial Intelligence in Customer Relationship Management: A Path to Personalization,” 2024 IEEE International Conference, Greater Noida, India, Mar. 2024. 3. A. Gupta et al., “Is Artificial Intelligence Transforming the CRM (Customer Relationship) Experience? An Empirical Study in Indian Retail Industry,” IEEE Conference Publication, 2025. 4. “Customer Relationship Management (CRM) Automation Using Machine Learning Algorithm,” IEEE Conference Publication, 2024. 5. “Artificial Intelligence for Customer Relationship Management: Personalization and Automation,” IEEE Conference Publication, 2024. 6. “Leveraging Artificial Intelligence and Machine Learning for Advanced Customer Relationship Management in the Retail Industry,” IEEE Conference Publication, 2024. 7. “Customer Churn Prediction Using Machine Learning and Explainable AI,” IEEE Conference Publication, 2026. 8. D. E. O'Leary, “Large Language Models and Applications: The Rebirth of Enterprise Knowledge Management and the Rise of Prompt Libraries,” IEEE Intelligent Systems, vol. 39, no. 2, pp. 72–75, 2024. 9. “LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics,” IEEE Conference Publication, 2025. 10. “Chatbots in Academia: A Retrieval-Augmented Generation Approach for Improved Efficient Information Access,” IEEE Conference Publication, 2024.
Publication Date
9/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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