Integrating Generative AI into Knowledge Management Systems for Context-Aware Decision Support

In complex organizations, traditional Knowledge Management Systems (KMS) and decision support systems (DSS) often struggle with three persistent challenges: capturing and converting tacit knowledge, like employee expertise and contextual insights, into explicit shareable formats; dynamically adapting recommendations and knowledge delivery to rapidly changing organizational contexts, user roles, and project needs; and enabling fast, relevant knowledge retrieval for timely decision-making. These challenges result in knowledge silos, loss of critical expertise, and decision delays. Integrating Generative AI (GenAI) with KMS, and context-aware DSS (CADSS) directly addresses these challenges by automating the collection, extraction, and structuring of tacit knowledge; personalizing insights based on real-time context; and streamlining access to actionable information. This approach enhances organizational agility, supports more informed decisions, and ensures that valuable knowledge is retained from retiring staff and leveraged across distributed teams. GenAI presents transformative opportunities to overcome the challenges of traditional KMS and enhance CADSS. GenAI encompasses a broad class of AI models capable of generating novel content, including text, images, audio, and code, with Large Language Models (LLMs) specializing in language understanding and generation from vast textual datasets. This research leverages LLMs for language-based tasks and incorporates other GenAI models, such as Variational Autoencoders (VAEs) for anomaly detection and Generative Adversarial Networks (GANs) for scenario simulation. Central to the proposed framework is GenAI's ability to process unstructured data, generate summaries, answer complex queries, and synthesize insights, thereby bridging the gap between individual tacit knowledge and organizational explicit knowledge. Key innovations include NLP-driven semantic search, dynamic content tagging, and predictive analytics to proactively identify knowledge gaps. The proposed framework architecture integrates cloud-based and on-premise components emphasizing scalability, API-based interoperability, and ethical AI governance to ensure transparency and mitigate bias. Central to the design is CADSS, which tailors responses using user profiles, historical data, and project-specific contexts. The development of this framework is explicitly grounded in Alavi’s Knowledge Management process model (2001), supporting knowledge creation (externalizing tacit knowledge), storage and retrieval (context-aware indexing), transfer (role-specific, adaptive content delivery), and application (embedding predictive analytics and real-time recommendations). The proposed mixed-methods evaluation strategy employs quantitative metrics (decision quality, time savings) and qualitative assessments (user interviews) to measure efficacy. Future work will focus on prototyping and addressing scalability in diverse organizational contexts.