AI-Driven Digital Assistants for Enterprise Knowledge Management

Abstract

Enterprise Knowledge Management (EKM) plays a vital role in leveraging organizational knowledge, improving decision-making, and maintaining competitive advantage. Traditional knowledge management systems struggle with scalability, real-time adaptability, and contextual understanding. AI-driven digital assistants address these limitations by using technologies like Natural Language Processing (NLP), deep learning, and knowledge graphs to enable intelligent automation and semantic understanding. These assistants support conversational interactions, making knowledge access and contribution more intuitive. The study proposes a modular architecture with components such as data ingestion, semantic processing, knowledge repositories, and user interfaces, enhanced by reinforcement learning for continuous improvement. Hybrid models combining rule-based and learning-based approaches improve reliability and interpretability. Findings show that AI-based assistants enhance knowledge retrieval accuracy, reduce response time, and increase user satisfaction. They are widely applicable in areas like customer support, internal knowledge discovery, and decision support. Overall, AI-powered digital assistants improve efficiency and promote knowledge democratization, with future research focusing on explainability, ethical AI use, and integration of multimodal data.