ISSN2456-3315
Impact Factor10.57
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ESTD2016
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Paper Title
AI-Enhanced Business Intelligence for Digital Enterprises
Authors
SWAPNIL SAURAV , Shruti Tyagi , Dheeraj Akula
Keywords
Artificial Intelligence, Business Intelligence, Data-Driven Decision-Making, Predictive Analytics
Abstract
- Artificial Intelligence (AI) is redefining the landscape of Business Intelligence (BI), enabling organizations to transform raw data into actionable insights that drive strategic decision-making. This paper explores the transformative role of AI-enhanced BI, detailing how advanced technologies such as machine learning, natural language processing (NLP), and automated analytics are revolutionizing data processing and utilization in enterprises. By integrating AI capabilities, BI systems extend beyond traditional descriptive and diagnostic analytics to include predictive and prescriptive functionalities, empowering organizations to anticipate trends, optimize resource allocation, and enhance operational efficiency.
The paper presents a comprehensive framework for implementing AI-driven BI, encompassing data integration, model development, and insights delivery. It also addresses critical challenges, including data quality, model interpretability, and scalability, offering practical solutions to overcome these obstacles. Real-world case studies from e-commerce, manufacturing, banking, and IT operations illustrate the tangible benefits of AI-enhanced BI, such as reduced churn rates, optimized supply chains, improved risk management, and enhanced system reliability.
Key recommendations highlight the importance of robust data infrastructure, scalable AI platforms, and explainable AI frameworks to ensure successful adoption and user trust. By embracing AI-driven BI, organizations can unlock the full potential of their data assets, fostering innovation, competitiveness, and sustainable growth in a rapidly evolving digital economy. This paper aims to provide both academic and practical contributions to the field, serving as a roadmap for researchers and practitioners seeking to leverage AI for transformative business intelligence.
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How To Cite
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IJRTI — journal style
"AI-Enhanced Business Intelligence for Digital Enterprises", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 1, page no.a126-a132, January-2025, Available :https://ijrti.org/papers/IJRTI2501020.pdf
APA — 7th edition
SAURAV, S., Tyagi, S., & Akula, D. (2025). AI-Enhanced Business Intelligence for Digital Enterprises. International Journal for Research Trends and Innovation, 10(1), a126-a132. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020
MLA — 9th edition
SAURAV, SWAPNIL, et al. "AI-Enhanced Business Intelligence for Digital Enterprises." International Journal for Research Trends and Innovation, vol. 10, no. 1, 2025, pp. a126-a132, https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020.
Chicago — 17th, bibliography
SAURAV, SWAPNIL, Shruti Tyagi, and Dheeraj Akula. "AI-Enhanced Business Intelligence for Digital Enterprises." International Journal for Research Trends and Innovation 10, no. 1 (2025): a126-a132. https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020.
Harvard — author–date
SAURAV, S., Tyagi, S. and Akula, D. (2025) 'AI-Enhanced Business Intelligence for Digital Enterprises', International Journal for Research Trends and Innovation, 10(1), pp. a126-a132. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020
IEEE — numbered reference
S. SAURAV, S. Tyagi and D. Akula, "AI-Enhanced Business Intelligence for Digital Enterprises," IJRTI, vol. 10, no. 1, pp. a126-a132, Jan. 2025.
Vancouver — biomedical
SAURAV S, Tyagi S, Akula D. AI-Enhanced Business Intelligence for Digital Enterprises. IJRTI. 2025 Jan;10(1):a126-a132.
AMA — 11th edition
SAURAV S, Tyagi S, Akula D. AI-Enhanced Business Intelligence for Digital Enterprises. IJRTI. 2025;10(1):a126-a132. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2501020,
author = {SWAPNIL SAURAV and Shruti Tyagi and Dheeraj Akula},
title = {AI-Enhanced Business Intelligence for Digital Enterprises},
journal = {International Journal for Research Trends and Innovation},
volume = {10},
number = {1},
pages = {a126-a132},
year = {2025},
month = {January},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - SAURAV, SWAPNIL
AU - Tyagi, Shruti
AU - Akula, Dheeraj
TI - AI-Enhanced Business Intelligence for Digital Enterprises
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 10
IS - 1
PY - 2025
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020
SP - a126-a132
ER -
Issue
Volume 10 Issue 1,
January-2025
Pages : a126-a132
Other Publication Details
Paper Reg. ID
IJRTI_200247
Published Paper ID
IJRTI2501020
Downloads
205,630
Research Area
Computer Science & TechnologyÂ
Country
Hyderabad, Telangana, India
Published
January 2025
Published Paper PDF
https://ijrti.org/papers/IJRTI2501020.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2501020
About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
Impact Factor
2016
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Open
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Impact Factor 10.57 calculated by Google Scholar and Semantic Scholar.
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
