<p>This study addresses two critical questions within the framework of the “digital transformation paradox”: First, can digital transformation effectively enhance Total Factor Productivity (TFP)? Second, what heterogeneity constraints shape this effect? Utilizing firm-level data from Chinese listed manufacturing companies from 2010 to 2022, we construct a digital transformation indicator through a machine learning-based text analysis approach, specifically Term Frequency-Inverse Document Frequency (TF-IDF). Structural estimation techniques are applied to model TFP, explicitly incorporating digital transformation as an endogenous factor within the productivity process. Our findings reveal that digital transformation has a significant positive effect on TFP, driven by three key mechanisms: improved supply chain management, enhanced digital innovation, and optimized human capital allocation. Both foundational technologies—excluding blockchain—and the practical applications of digital transformation contribute positively to TFP. Heterogeneity analysis reveals that the impact of digital transformation is particularly pronounced in exporting, state-owned, non-high-tech manufacturing, and capital-intensive enterprises. Moreover, strong intellectual property protection amplifies the productivity-enhancing effects of digital transformation. Extended analysis underscores the importance of threshold conditions for the effectiveness of digital transformation. Firms must exceed a minimum level of internal digital investment and access robust external digital infrastructure to fully realize productivity gains. This study provides novel insights into the role that digital transformation plays in fostering high-quality economic and organizational development, providing valuable implications for policymakers and business managers.</p>