Integration of Emerging Technologies for Business Workflow Optimization: A Systematic Analysis of IoT, AI, and Blockchain Solutions

Ranadheer Suram

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 1995-2003

Abstract: The rapid evolution of emerging technologies presents unprecedented opportunities for optimizing business workflows through integrated automation solutions. This article examines the convergence of the Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technologies in transforming traditional business processes. Through systematic analysis, the article investigates how IoT enables system automation, while machine learning algorithms facilitate workflow prediction and AI-driven process mining enhances operational efficiency. Special attention is given to the role of AI orchestration tools in bottleneck reduction and workflow optimization. The article further explores blockchain implementation for secure workflow tracking and hyper-automation support, complemented by cloud-native architectural innovations. The findings demonstrate that integrating these emerging technologies significantly enhances workflow optimization, improves process transparency, and strengthens operational security. The article contributes to the growing body of knowledge on business process automation by providing a comprehensive framework for technology integration while highlighting current trends and future directions in workflow optimization. These insights offer valuable implications for businesses seeking to modernize their operational processes through emerging technologies.

Keywords: Workflow Optimization; Business Process Automation; Emerging Technologies Integration; AI-driven Process Mining; Blockchain Implementation (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410612390
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:596

DOI: 10.32628/CSEIT2410612390

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