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

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.