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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM67550

AI-Powered Enterprise CRM with Intelligent Company Knowledge Assistance

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67550

Volume:

10 |

9 |

Sep 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

SSB

Sagar Shridhar Bandawar

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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SB

Sagar Bandawar

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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MF

Mrugakshi Fulzele

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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AT

Apurva Tonge

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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YT

Yuvraj Thakre

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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PW

Pranali Warhade

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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Mentors

PW

Pranali Warhade

Artificial Intelligence and Data Science

Priyadarshini Collage of Engineering , Nagpur

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Abstract

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

Enterprise CRM Artificial Intelligence Knowledge Assistant Retrieval-Augmented Generation Large Language Models Conversational AI

Cite This Article

Sagar Shridhar Bandawar, Sagar Bandawar, Mrugakshi Fulzele, Apurva Tonge, Yuvraj Thakre, Pranali Warhade, Pranali Warhade, "AI-Powered Enterprise CRM with Intelligent Company Knowledge Assistance," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-4, Sep 2026.

Full Article/Conclusion

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.

References

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.

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Publication Date

9/30/2026

Peer Review Type

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Review Rounds: 2

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