DEVELOPMENT OF MACHINE LEARNING SOLUTIONS THAT OPTIMIZE BUSINESS OPERATIONS AND INCREASE EFFICIENCY THROUGH INTELLIGENT PROCESS AUTOMATION - Daniel Thompson; Olivia Zhang; Ethan Patel; Maya Singh; Liam Chen (PT. Antis International Publisher, 2025)
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.