Transforming Enterprise Technology: The Synergy of SAP, Cloud Computing, Machine Learning, and AI
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Computer Science and Information Technology | India | Volume 13 Issue 2, February 2024 | Popularity: 6.4 / 10


     

Transforming Enterprise Technology: The Synergy of SAP, Cloud Computing, Machine Learning, and AI

Chetan Sharma, Mukesh Kumar Saini, Adarsh Vaid


Abstract: In the contemporary digital landscape, the integration of SAP, Cloud Computing, Machine Learning (ML), and Artificial Intelligence (AI) is revolutionizing business operations and resource management. This paper explores the synergistic effects of these technologies, examining their benefits and the challenges they pose. Through a comprehensive review of existing literature, we outline the methodologies employed in this research and present a detailed comparative analysis. The unique characteristics of this integrated approach are highlighted, alongside practical recommendations for effective implementation. Findings indicate that leveraging these technologies collectively enhances operational efficiency, scalability, and decision-making capabilities within organizations, paving the way for innovative business strategies in a rapidly evolving market.


Keywords: SAP, Cloud Computing, Machine Learning, Artificial Intelligence, ERP, Predictive Analytics, Automation, Enterprise Transformation, Data Security, Integration Complexity, Skillset Development


Edition: Volume 13 Issue 2, February 2024


Pages: 1892 - 1896


DOI: https://www.doi.org/10.21275/SR240216111503


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Chetan Sharma, Mukesh Kumar Saini, Adarsh Vaid, "Transforming Enterprise Technology: The Synergy of SAP, Cloud Computing, Machine Learning, and AI", International Journal of Science and Research (IJSR), Volume 13 Issue 2, February 2024, pp. 1892-1896, https://www.ijsr.net/getabstract.php?paperid=SR240216111503, DOI: https://www.doi.org/10.21275/SR240216111503

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