【摘要】 随着人工智能（AI）驱动的技术与管理深度融合，以需求为导向的场景化数智化转型已成为工业升级的核心路径。然而，当前工业实践中普遍存在“技术盲从”倾向，导致资源投入与业务回报失衡、技术架构与业务逻辑失配，严重阻碍了转型的可持续性。明确转型目标及其评价测度是破局关键。本文针对工业场景的复杂特性，通过深度辨析自动化（Automation, A）、自主化（Autonomy,U）与智能化（Intelligence, I）的内涵差异与内在联系，结合所提出的场景模型的解构方法 ,构建了基于A/U/I框架的能力评价模型。相比传统评价方式和维度，该模型旨在揭示技术能力供给与场景绩效表现之间的因果映射关系，引导企业从“盲目技术替代”转向“精准能力配置”。研究表明，因地制宜地选择评价准则，能够有效提升转型收益并降低系统摩擦成本，为工业数智化转型的良性循环提供理论支撑与决策工具。更多还原

【Abstract】 As AI integrates deeply with management, demand-driven, scenario-based transformation has become the core path for industrial upgrading. However, prevalent "tech-blindness" in practice causes imbalances between investment and returns, and misalignments between architecture and logic, hindering sustainability. Defining clear goals and metrics is the key to breaking this deadlock. Addressing industrial complexity, this paper analyzes the distinctions and links between Automation(A), Autonomy(U), and Intelligence(I). Combined with a scenario-deconstruction method, we construct an A/U/I capability evaluation model. Unlike traditional methods, this model reveals the causal mapping between technical supply and scenario performance, guiding enterprises from "blind substitution" to "precision allocation". Research shows that tailored evaluation criteria effectively increase returns and reduce friction costs, providing theoretical support and decision tools for a virtuous cycle of industrial transformation.更多还原