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All Journal International Journal of Business, Law and Political Science
Thompson, Daniel
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Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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DEVELOPMENT OF MACHINE LEARNING SOLUTIONS THAT OPTIMIZE BUSINESS OPERATIONS AND INCREASE EFFICIENCY THROUGH INTELLIGENT PROCESS AUTOMATION Thompson, Daniel; Zhang, Olivia; Patel, Ethan; Singh, Maya; Chen, Liam
International Journal of Business, Law and Political Science Vol. 2 No. 12 (2025): International Journal of Business, Law and Political Science
Publisher : PT. Antis International Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijblps.v2i12.466

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.
Co-Authors Chen, Liam Patel, Ethan Singh, Maya Zhang, Olivia