Exploring Enterprise Digital Transformation: The Perspective of Explainable Machine Learning

Digital transformation (DT) significantly enhances enterprise efficiency, innovation, and competitiveness through cutting-edge technologies such as artificial intelligence (AI), cloud computing, and big data. This study employs interpretable machine learning (ML) models to investigate the determinants of DT outcomes. The primary aim is to assess the impact of these technologies on operational efficiency, innovation, and market competitiveness. Using the 2011–2022 CSMAR Enterprise Digital Transformation Database, we performed regression analyses and SHAP value assessments to elucidate the contributions of various factors. Our findings highlight the criticality of early adoption and ongoing advancement of digital processes. AI and cloud computing substantially improve digital outcomes, whereas broader innovation initiatives, though crucial, exhibit more variable effects. Theoretically, this study enriches DT frameworks by integrating interpretable ML insights. Practically, it provides actionable strategies for enterprises to achieve superior digital outcomes and maintain a competitive edge.