Katta, Tejaswi Bharadwaj (2025) Comparative analysis of AI-powered iPaaS Solutions for Enterprise Integration. World Journal of Advanced Research and Reviews, 26 (1). pp. 2524-2533. ISSN 2581-9615
![WJARR-2025-1326.pdf [thumbnail of WJARR-2025-1326.pdf]](https://eprint.scholarsrepository.com/style/images/fileicons/text.png)
WJARR-2025-1326.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial Share Alike.
Abstract
This article presents a comprehensive comparative analysis of AI-powered Integration Platform as a Service (iPaaS) solutions transforming enterprise integration landscapes. As organizations navigate increasingly complex digital ecosystems, these platforms leverage artificial intelligence to address traditional integration challenges through automated data mapping, intelligent error handling, predictive analytics, and natural language processing capabilities. The article examines leading market solutions, evaluating their distinctive strengths and limitations. Through a detailed financial services case study, the article demonstrates how organizations balance technical capabilities with business priorities when selecting integration platforms. Implementation considerations spanning organizational readiness, technical infrastructure, and strategic alignment are explored, highlighting critical success factors beyond technical functionality. It concludes by examining emerging trends that will shape future integration platforms, including autonomous integration capabilities, edge intelligence, and embedded business analytics, providing valuable insights for organizations seeking to leverage AI-enhanced integration to drive digital transformation.
Item Type: | Article |
---|---|
Official URL: | https://doi.org/10.30574/wjarr.2025.26.1.1326 |
Uncontrolled Keywords: | AI-Powered Integration Platforms; Enterprise Application Connectivity; Machine Learning For Data Mapping; Autonomous Integration Workflows; Edge Computing Integration |
Depositing User: | Editor WJARR |
Date Deposited: | 25 Jul 2025 17:01 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/2032 |