Innovative AI - ERP Integration: Elevating Enterprise Project Management and Business Process Improvement

Dileep Hamsaneni, Venkateswarlu Yenuganti

Abstract: The integration of artificial intelligence (AI) with enterprise resource planning (ERP) systems is revolutionizing modern business operations, offering unprecedented opportunities for efficiency, innovation, and strategic growth. This article explores how AI - enhanced ERP systems are transforming enterprise project management and process improvement by leveraging predictive analytics, automation, and real - time data insights. Predictive analytics enables businesses to forecast trends, optimize inventory, and anticipate risks, while automation technologies like machine learning (ML) and robotic process automation (RPA) streamline routine tasks, reduce errors, and enhance operational efficiency. Real - world applications across industries such as manufacturing, retail, healthcare, and finance demonstrate the tangible benefits of AI - ERP integration, including improved decision - making, cost savings, and enhanced customer experiences. Additionally, the article addresses key challenges in implementation, ethical considerations, and future trends such as generative AI and IoT - enabled optimization. By combining empirical evidence, case studies, and practical insights, this article underscores the transformative potential of AI - ERP systems in driving business success and maintaining a competitive edge in the digital age.

Keywords: AI - ERP integration, enterprise automation, business process optimization, real - time data insights, AI in Enterprise Resource Planning (ERP), AI - Driven ERP Solutions, Artificial Intelligence for Business Optimization, ERP System Automation, Predictive Analytics in ERP

How to Cite?: Dileep Hamsaneni, Venkateswarlu Yenuganti, "Innovative AI - ERP Integration: Elevating Enterprise Project Management and Business Process Improvement", Volume 14 Issue 3, March 2025, International Journal of Science and Research (IJSR), Pages: 380-383, https://www.ijsr.net/getabstract.php?paperid=SR25309104219, DOI: https://dx.doi.org/10.21275/SR25309104219