AI-Driven Innovation for Operational Transformation: A Case Study of Organizational, Technological, and Human Factors in WH Company
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Abstract
The research objectives were 1) to examine how artificial intelligence technologies are applied to support operational innovation in a traditional manufacturing enterprise, 2) to identify the key organizational, technological, and human factors influencing the effectiveness of AI-driven operational transformation, and 3) to propose practical guidelines for enhancing the effectiveness of AI-driven operational transformation in traditional manufacturing enterprises. A qualitative case study approach was employed. The data were collected through semi-structured in-depth interviews with 20 purposively selected participants, including senior managers, middle managers, frontline employees, and IT and engineering staff. The interview data were triangulated with internal documents and on-site observations. Thematic analysis was conducted using open coding, axial coding, and selective coding to identify major patterns and core themes. The findings indicated that AI has significantly contributed to operational innovation, particularly in production automation, quality control, warehousing, and logistics management. AI-enabled systems improved operational efficiency, reduced labor intensity, lowered defect rates, and enhanced decision-making accuracy. However, AI adoption remains uneven across departments, with limited integration in customer service, sales support, and administrative functions. The effectiveness of AI-driven transformation is strongly influenced by organizational factors (leadership support, strategic alignment, and change management), technological factors (data quality, system integration, and IT infrastructure readiness), and human factors (employee digital literacy, training, and acceptance of AI). Practical guidelines for enhancing the effectiveness of AI-driven transformation include positioning AI as a long-term organizational strategy, improving system integration and data quality, designing role-based skill development, and institutionalizing systematic change management.
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