Author(s)

Minnan Zhang, Shuang Han

Corresponding Author

Minnan Zhang

Abstract

Amid accelerating industry-wide digital-intelligent transformation and rising macro uncertainty, evaluating enterprise digital transformation capabilities and optimizing management models are pivotal to competitiveness and resilience. Leveraging annual reports, ESG disclosures, patents and hiring texts, and IT/OT operations logs (2019–2024), this study builds a Capability–Elements–Performance framework and applies large language models (LLMs) with machine learning to extract themes, quantify indicators, and classify sentiment across heterogeneous sources. We construct a comparable Digital Transformation Capability Index (DTCI) and derive actionable optimization roadmaps. Results show that data governance, process intelligence, and organizational coordination are the strongest drivers of performance; AI application depth complements change management quality; and regulatory intensity and supply-chain complexity condition capability formation. We propose an AI-enabled closed-loop management model (diagnose–design–deploy–measure–iterate) and provide sector- and size-specific implementation guidance.

Keywords

Digital Transformation; Artificial Intelligence; Capability Evaluation; Management Optimization; Large Language Models; Data Governance; Process Intelligence