AI赋能链主型科技制造企业ESG的实践表现与成效研究——以联想集团为例

Research on the Practical Performance and Effectiveness of AI-Enabled ESG in Chain-Leading Technology Manufacturing Enterprises—A Case Study of Lenovo Group

摘要: 数字化转型与可持续发展的背景下，AI成为驱动企业ESG转型的关键力量，但现有研究对AI赋能ESG的表现和成效研究仍存在探索缺口。研究以联想集团为对象，采用单案例研究法系统探究AI对链主型科技制造企业ESG的影响。研究发现，AI赋能ESG转型非全域同步，其依托关键技术节点实现分维度突破，通过数据互通、技术复用、价值锚定的多元协同，激活各维度联动效应；其核心赋能逻辑为技术落地后对资源配置的精准优化，进而推动企业从被动履责转向主动增值，形成兼具适配性与可复制性的转型模式。研究的理论创新体现在三个方面：一是突破现有宏观论述的局限，提炼AI赋能企业ESG的微观传导机制，填补AI与ESG内在关联的理论空白；二是构建多维度转型理论框架，克服单一维度碎片化缺陷，丰富ESG转型的理论体系；三是立足科技企业实践，拓展AI与可持续发展融合的理论应用场景，为数字化背景下企业ESG转型的理论研究提供新视角。

Abstract: Under the background of digital transformation and sustainable development, artificial intelligence has become a critical driving force for corporate ESG transformation. However, existing studies lack an in-depth exploration of the performance and effectiveness of AI-enabled ESG practices. Taking Lenovo Group as the research object, this study adopts a single case study method to systematically investigate the impact of artificial intelligence on ESG practices in chain-leading technology manufacturing enterprises. The findings indicate that AI-enabled ESG transformation does not occur simultaneously across all dimensions. Instead, it achieves dimensional breakthroughs relying on key technological nodes, and activates interactive effects among different dimensions through multiple synergies including data interoperability, technology reuse, and value anchoring. The core enabling logic lies in the precise optimization of resource allocation following technology implementation, which further drives enterprises to shift from passive responsibility fulfillment to proactive value creation, forming a transformation model with both adaptability and replicability. This study makes three theoretical contributions. First, it breaks through the limitations of macro-level discussions, extracts the micro transmission mechanism of AI-enabled corporate ESG practices, and fills the theoretical gap regarding the internal connection between artificial intelligence and ESG. Second, it constructs a multi-dimensional theoretical framework for ESG transformation, addresses the fragmentation defect of single-dimensional research, and enriches the theoretical system of ESG transformation. Third, grounded in the practices of technology enterprises, it expands the theoretical application scenarios of the integration between artificial intelligence and sustainable development, providing a new perspective for theoretical research on corporate ESG transformation in the digital era.