ICAIMT-Machine Learning-Driven Innovations in Knowledge Management: Integrating Business Intelligence, e-Learning and Organizational Culture for AI Management Trends

This study investigates the intersection of business intelligence, e-learning and organizational culture with machine learning (ML) for improving knowledge management (KM) practices in contemporary organizations. The primary objective is to examine how the application of advanced analytics can transform conventional knowledge creation, sharing and retention processes. A comprehensive questionnaire-based dataset was used, enabling a mixed-methods approach those benefits from quantitative analysis using ML algorithms and qualitative insights from open-ended survey questions. This hybrid methodological framework enables an in-depth examination of the key factors influencing KM effectiveness, including demographic profiles, BI tool adoption level, e-learning intensity and prevailing organizational culture. The results indicate that young, IT-savvy professionals and private sector organizations are likely to have a higher propensity to embrace and benefit from these converged approaches. Additionally, the findings indicate that greater ML integration is positively associated with better knowledge discovery and innovation. For example, organizations with high ML integration (top 25% of respondents) reported a 34% higher knowledge discovery effectiveness score (measured via Likert scale analysis) and were more likely to cluster in innovation-strong segments identified via K-means. Regression models showed a significant positive correlation (Formula: see text 2 Formula: see text0.62) between BI adoption, ML integration and KM effectiveness. However, the effectiveness of such integration is moderated by elements such as communication practices and leadership support. Overall, this study adds to the current discourse on digital transformation in KM by offering empirical evidence of the transformational potential of AI-driven initiatives. The study offers practitioner-friendly insights and paves the way for future research on optimizing KM systems in an increasingly data-driven business environment.