AI-Enabled Knowledge Management Systems for Organizational Intelligence
TL;DR
The architecture of AI-KMS is examined, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation, which shows improved accuracy in knowledge retrieval and decision-making efficiency.
Abstract
The rapid development of Artificial Intelligence (AI) has significantly transformed organizational operations, particularly in Knowledge Management Systems (KMS). In today’s dynamic and data-driven environment, competitive advantage depends on effectively capturing, storing, retrieving, and utilizing knowledge. AI-based KMS (AI-KMS) represent an evolution from traditional systems, shifting from rule-based repositories to intelligent, adaptive, and context-aware platforms that enhance organizational intelligence. Traditional KMS faced limitations in scalability and efficiency due to reliance on structured data and manual input. In contrast, AI-enabled systems leverage machine learning, natural language processing, and data analytics to process unstructured data such as documents, emails, and multimedia, enabling semantic search, personalized recommendations, and predictive insights. These systems also support continuous learning by automatically updating knowledge bases through user interactions. This paper examines the architecture of AI-KMS, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation. It also addresses key challenges including data quality, privacy, scalability, and ethical concerns. A detailed literature review traces the evolution of KMS and AI integration prior to 2018. The proposed methodology uses a hybrid model combining supervised and unsupervised learning for knowledge extraction and classification. Experimental results show improved accuracy in knowledge retrieval and decision-making efficiency compared to traditional systems, supported by quantitative analysis. Overall, AI-KMS enhance organizational intelligence by accelerating decisions, fostering collaboration, and driving innovation. The paper concludes by recommending future research in explainable AI, governance frameworks, and integration with emerging technologies like IoT and blockchain.