Intelligent Process Automation in Enterprise Systems: Advancements, Challenges, and Future Directions
Keywords:
Artificial Intelligence, Robotic Process Automation, Intelligent Process Automation, Enterprise SystemsAbstract
The advent of artificial intelligence (AI) and robotic process automation (RPA) has transformed the landscape of enterprise systems, enabling organizations to optimize operational efficiency, reduce human error, and enhance decision-making capabilities. This research provides an extensive investigation into the theoretical, methodological, and practical dimensions of AI-driven process automation in modern enterprises. By synthesizing insights from contemporary studies, including knowledge-intensive processes, robotic process automation, intelligent process automation, and AI-augmented business process management systems, this study identifies key technological innovations, methodological frameworks, and organizational challenges that shape enterprise automation. Through a critical review of empirical findings and applied case studies, the study elaborates on strategies for enhancing automation performance, workflow reliability, and adaptive decision-making. The research highlights the progression from basic RPA systems to intelligent process automation (IPA) frameworks that leverage AI for predictive analytics, self-learning capabilities, and adaptive process optimization. Furthermore, this study examines the limitations associated with automation deployment, including scalability, process complexity, and integration challenges, and proposes directions for future research and practical adoption. By offering a comprehensive theoretical and practical discourse, the research aims to contribute to the strategic understanding and implementation of AI-driven automation within enterprise ecosystems.
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