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An AI-driven framework for modernizing oracle ERP systems in the public sector

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  • An AI-driven framework for modernizing oracle ERP systems in the public sector

Srikanth G *

Independent Researcher, USA.

Research Article

International Journal of Science and Research Archive, 2025, 14(02), 903-913

Article DOI: 10.30574/ijsra.2025.14.2.0477

DOI url: https://doi.org/10.30574/ijsra.2025.14.2.0477

Received on 06 January 2025; revised on 11 Februay 2025; accepted on 14 February 2025

Public sector organizations face serious need to upgrade their Enterprise Resource Planning systems because this upgrade helps them to work more efficiently and serve customers effectively. This paper suggests an innovative plan to include Artificial Intelligence into Oracle ERP systems, which helps improve work processes and makes better decisions while handling automatic repetitive tasks. This framework enables easy modifications to ERP systems through AI while also working with less human action to give improved data insights. The research analyzes ERP system challenges before explaining how AI tools help systems to execute processes faster and more securely plus offering better adaptability. The analysis uses actual practice evidence and numerical data to explain how AI supports government organizations when they transform their Oracle ERPs. Research data proves that digital ERP systems that employ Artificial Intelligence technologies assist companies to work better and make smart transformational digital decisions. 

AI-Driven Framework; Oracle ERP Systems; Public Sector; ERP Modernization; Artificial Intelligence; Digital Transformation; Enterprise Resource Planning; Intelligent Automation; Machine Learning; Cloud-Based ERP; Process Optimization; Smart Governance; AI In Public Administration

https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0477.pdf

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Srikanth G. An AI-driven framework for modernizing oracle ERP systems in the public sector. International Journal of Science and Research Archive, 2025, 14(02), 903-913. Article DOI: https://doi.org/10.30574/ijsra.2025.14.2.0477.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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