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The Future of ERP Cloud Functional Processes: AI-Driven Automation and OIC Integration

Author(s) Sreenivasa Rao Sola
Country India
Abstract The emerging role of Artificial Intelligence (AI) in Enterprise Resource Planning (ERP) Cloud and Oracle Integration Cloud (OIC) is revolutionizing functional procedures across various industries. Automation by AI enhances operational effectiveness with speeded-up workflows, business pattern forecasts, and enabling real-time decision-making. The current article explores how AI is incorporated into ERP Cloud platforms to maximize data management, predictive analytics, and business intelligence optimization. It also explores how companies are confronted with the implementation of AI-powered cloud solutions, such as data security issues, system compatibility and adjustment of employees. AI-powered improvements in OIC facilitate seamless integration of enterprise applications, automation of day-to-day tasks, and innovation. Through machine learning algorithms, businesses can be more agile, scalable, and cost-efficient. But to adopt AI-enabled ERP Cloud, there is a need for change through strategic planning, high-performance infrastructure, and trained human capital to unlock its maximum potential. This article investigates the future of AI within ERP Cloud focusing on its contribution towards enterprise digital transformation, automation, and strategic decision-making
Keywords AI for ERP Cloud, Oracle Integration Cloud (OIC), AI-based automation, enterprise digital transformation, predictive analytics, machine learning-based ERP, AI-based business intelligence, cloud-based integration of AI, operational efficiency, real-time decision-making
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 7, July 2024
Published On 2024-07-11
Cite This The Future of ERP Cloud Functional Processes: AI-Driven Automation and OIC Integration - Sreenivasa Rao Sola - IJLRP Volume 5, Issue 7, July 2024. DOI 10.5281/zenodo.15259105
DOI https://doi.org/10.5281/zenodo.15259105
Short DOI https://doi.org/g9f8w2
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