Transforming Enterprise Content Management through Machine Learning Driven Automation Generative AI and Scalable Cloud Native Architectures
Abstract
Enterprise Content Management (ECM) is being transformed through machine learning-driven automation, generative AI, and scalable cloud-native architectures. By integrating AI-powered content classification, automated metadata tagging, and generative content synthesis, organizations can streamline document workflows, enhance knowledge discovery, and reduce manual operational overhead. Cloud-native principles, including microservices, serverless computing, and container orchestration, enable scalable, resilient, and low-latency content delivery across distributed enterprise environments.
Generative AI further enhances ECM by producing context-aware summaries, recommendations, and content variations that improve collaboration and decision-making. Combined with intelligent automation and real-time analytics, these platforms support adaptive, efficient, and compliant management of enterprise knowledge assets. This approach empowers organizations to achieve operational excellence, accelerate digital transformation, and drive innovation in content-centric business processes
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
21-30 |
Published |
February 13, 2026 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Michael Wooldridge (2026). Transforming Enterprise Content Management through Machine Learning Driven Automation Generative AI and Scalable Cloud Native Architectures. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 9 No. 1 (2026): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 21-30. https://doi.org/10.15662/IJAESIT.2026.0901004 |
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