Role of AI in Enterprise Content Management: Foundations and Current State

Enterprise Content Management (ECM) systems are undergoing a paradigm shift through the integration of Artificial Intelligence (AI), transforming how enterprises manage, classify, and retrieve unstructured information. This paper presents a comprehensive analysis of AI adoption in ECM, outlining foundational principles, architectural advancements, and sector specific use cases. We synthesize current methodologies, market dynamics, and implementation challenges, highlighting measurable gains in efficiency, accuracy, and compliance. Empirical findings demonstrate that AI enhanced ECM systems achieve up to 47% faster document retrieval, 92–95% classification accuracy, and a 68% reduction in manual processing. We propose a modular AI ECM framework incorporating machine learning, NLP, and privacy preserving techniques, supported by real world deployments in finance and healthcare. This work lays a foundation for scalable, secure, and intelligent ECM, and defines a roadmap for future innovation.

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Publication Details

Published
2025-05-09
DOI
https://doi.org/10.1109/ginotech63460.2025.11076659
Citations
2
Primary Topic
Information Systems and Technology Applications
Type
article
Field-Weighted Citation Impact
4.57
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article

Role of AI in Enterprise Content Management: Foundations and Current State

Chiranjeevi Bura, Praveen Kumar Myakala, Anil Kumar Jonnalagadda, Srikanth Kamatala et al.
2 citations
Information Systems and Technology Applications
4.57
article

Role of AI in Enterprise Content Management: Foundations and Current State

Chiranjeevi Bura, Praveen Kumar Myakala, Anil Kumar Jonnalagadda, Srikanth Kamatala, Prudhvi Naayini
article en
2 citations

Abstract

Enterprise Content Management (ECM) systems are undergoing a paradigm shift through the integration of Artificial Intelligence (AI), transforming how enterprises manage, classify, and retrieve unstructured information. This paper presents a comprehensive analysis of AI adoption in ECM, outlining foundational principles, architectural advancements, and sector specific use cases. We synthesize current methodologies, market dynamics, and implementation challenges, highlighting measurable gains in efficiency, accuracy, and compliance. Empirical findings demonstrate that AI enhanced ECM systems achieve up to 47% faster document retrieval, 92–95% classification accuracy, and a 68% reduction in manual processing. We propose a modular AI ECM framework incorporating machine learning, NLP, and privacy preserving techniques, supported by real world deployments in finance and healthcare. This work lays a foundation for scalable, secure, and intelligent ECM, and defines a roadmap for future innovation.

Openalex Percentile: Top 5%
Information Systems and Technology Applications
4.57
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Role of AI in Enterprise Content Management: Foundations and Current State — Chiranjeevi Bura, Praveen Kumar Myakala, et al. · (2025) | TGRS Research Map | TGRS