Thompson, Daniel and Zhang, Olivia and Patel, Ethan and Singh, Maya and Chen, Liam (2025) DEVELOPMENT OF MACHINE LEARNING SOLUTIONS THAT OPTIMIZE BUSINESS OPERATIONS AND INCREASE EFFICIENCY THROUGH INTELLIGENT PROCESS AUTOMATION. International Journal of Business, Law and Political Science, 2 (12). pp. 674-681. ISSN 3032-1298

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Abstract

Objective: This research develops machine learning solutions that optimize business operations through intelligent process automation, combining robotic process automation (RPA) with cognitive capabilities. Method: Our framework integrates natural language processing, computer vision, and predictive analytics to automate complex decision-making processes traditionally requiring human intervention. Results: Implementation across five industry sectors demonstrates average cost reductions of 42%, processing time improvements of 65%, and error rate reductions of 89%. The study provides practical guidelines for organizations seeking to implement intelligent automation strategies and quantifies the potential returns on investment. Novelty: Business process automation has emerged as a critical driver of operational efficiency and competitive advantage in modern enterprises.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 May 2026 09:41
Last Modified:	07 May 2026 09:41
URI:	http://eprints.umsida.ac.id/id/eprint/16463

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