AI-Enhanced Multimedia Business Intelligence for Digital Organizational Transformation: Case Studies and Management Process Optimization

Abstract

Digital organizational transformation (DOT) has become an imperative for enterprises to maintain competitiveness in the data-driven era, yet traditional management processes suffer from inefficiencies due to fragmented multimedia data and limited intelligent analysis capabilities. This study explores how artificial intelligence (AI) integrated with multimedia business intelligence (MBI) can reconstruct professional management processes. A mixed-method research design is adopted, including literature review, multi-case study (covering manufacturing and financial sectors), process modeling, and empirical evaluation. Two real-world cases demonstrate that the proposed AI+MBI framework achieves significant improvements: process cycle time reduced by 42.3% on average, error rate decreased by 37.1%, and decision-making efficiency improved by 58.6%. The study also identifies key success factors including data integration standardization, cross-departmental collaboration, and AI model adaptability. Optimization strategies targeting technical, process, and organizational dimensions are proposed to enhance implementation effectiveness. This research enriches the theoretical system of DOT and provides actionable guidelines for enterprises leveraging AI and MBI in management innovation.