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The Role of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in Scaling Enterprise Resource Planning (ERP) Systems

Author(s) Vidushi Sharma
Country United States
Abstract Enterprise Resource Planning (ERP) systems have long been the backbone of organizational efficiency, integrating key business functions such as finance, HR, and supply chain management. However, the complexity and scale of modern enterprises demand greater agility, automation, and intelligent decision-making capabilities. This research explores the role of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in scaling ERP systems. The study investigates how RPA streamlines repetitive, rule-based processes, while AI enhances ERP systems with predictive analytics and decision support. A mixed-methods approach, combining qualitative case studies with quantitative analysis of ERP performance metrics, was used to assess the impact of RPA and AI on ERP scalability. The findings indicate that the integration of RPA and AI significantly improves operational efficiency, reduces human error, and enables better decision-making in large-scale ERP environments. Moreover, organizations that adopted these technologies experienced enhanced scalability, making it easier to adapt to business growth. The study concludes that RPA and AI are key enablers of ERP systems, providing the necessary capabilities for enterprises to scale effectively in the digital era. This research contributes to the growing body of knowledge by highlighting practical applications and providing a roadmap for future ERP enhancements.
Keywords Robotic Process Automation (RPA), Artificial Intelligence (AI), Enterprise Resource Planning (ERP), Automation, Scalability, Digital Transformation.
Field Engineering
Published In Volume 4, Issue 6, June 2023
Published On 2023-06-07
Cite This The Role of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in Scaling Enterprise Resource Planning (ERP) Systems - Vidushi Sharma - IJLRP Volume 4, Issue 6, June 2023. DOI 10.5281/zenodo.14769567
DOI https://doi.org/10.5281/zenodo.14769567
Short DOI https://doi.org/g83kt9
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