Modernizing Enterprise Platforms for Generative AI and Intelligent Automation
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Abstract
Modernizing enterprise platforms for Generative Artificial Intelligence (GenAI) and intelligent automation has become a strategic priority for organizations seeking to improve operational efficiency, scalability, agility, and innovation. Traditional enterprise systems often rely on rigid architectures, fragmented data sources, manual workflows, and rule-based automation, which limit their ability to respond effectively to rapidly changing business requirements. Integrating GenAI, large language models (LLMs), intelligent agents, and automation technologies into existing enterprise platforms enables organizations to transform these systems into adaptive, data-driven, and intelligent operational environments. This paper explores the key architectural principles, enabling technologies, and implementation strategies for modernizing enterprise platforms to support GenAI and intelligent automation. It examines cloud-native architectures, microservices, application programming interfaces (APIs), retrieval-augmented generation (RAG), enterprise knowledge management, and intelligent workflow orchestration. Particular emphasis is placed on automating complex business processes, enabling natural-language interactions, improving decision support, and establishing intelligent agents capable of coordinating multi-step tasks under appropriate governance controls. The paper also addresses challenges related to data quality, security, privacy, interoperability, model reliability, regulatory compliance, and operational costs.
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