The aim of this study is to develop a framework for implementing demand-driven, AI-enabled business intelligence in the apparel industry. The study is applied in purpose and adopts a mixed-methods design (qualitative–quantitative). In the qualitative phase, 17 university faculty members, domain experts, sales specialists, apparel industry managers, and AI specialists were selected through purposive sampling. In the quantitative phase, 384 managers, senior experts, data analysts, supply chain managers, and information technology specialists in the apparel industry were recruited using cluster random sampling. Data were collected via semi-structured interviews and a questionnaire. Qualitative data were analyzed using MaxQDA 2020, while quantitative analyses were conducted with SPSS and PLS-SEM.The coding process generated 675 initial codes, which were refined to 461 codes after cleaning and subsequently organized into 214 conceptual codes, 45 subcategories, 19 main categories, and 6 core categories. Based on the paradigmatic model, key causal conditions included intensified competition, shifts in customer roles and market behavior, rising internal costs, and resource constraints. Continuous learning and monitoring, along with privacy protection, were identified as facilitating interventions; conversely, limited AI competencies, resistance to change, and legal constraints may function as inhibiting interventions. These conditions shape strategies such as transformational leadership, organizational memory management, and technology investment. Overall, the proposed model is aligned with firms’ internal and structural challenges.