Enhancing Master Data Management Within Enterprise Resource Planning Systems Using Artificial Intelligence for Optimized Business Processes

Master Data Management has been at the heart of the streamlining of data governance and its integrity, and the penetrations even into the Enterprise Resource Planning systems. This paper proposes, from the perspective of ERPs, how the integration of Artificial intelligence into MDM procedures and processes can leverage Business workflows. Through deployment of AIenabled algorithms, the enterprises can execute an entire set of operations including data validation, cleansing as well as enrichment at the same moment. This improves real time decision-making and optimal resource management. In the paper, AI techniques such as machine learning, natural language processing, and predictive analytics are used to enrich data quality, eliminate redundancies, and remove inconsistencies. It portrays a new architecture that embodies AI capabilities within existing ERP frameworks, with specific emphasis on modular adaptability and scalability. Experimental results suggest that data processing speed and accuracy improvements, which are the potential of AI to revolutionize MDM in ERP systems, are substantial. This research concludes with a discussion of challenges and limitations, and further directions toward smooth AI adoption in enterprise environments.