未来产业代表未来科技和产业发展的新方向，是培育新质生产力的重要载体；人工智能（AI）驱动科学研究的范式变革，引领生产方式转型和效率提升，对未来产业发展具有赋能作用。本文基于新质生产力理论和“技术 ‒ 经济范式”框架，构建了AI赋能未来产业的“要素 ‒ 场景 ‒ 规则”三位一体整体框架，厘清了AI赋能科技创新（AI for science）、AI赋能新质生产力（AI for productivity）、AI赋能未来产业发展（AI for industries）的三重逻辑；阐述了AI发展面临的产业秩序和就业结构问题、数据隐私和伦理安全问题、能源消耗与算力成本问题、技术与应用鸿沟问题，提出了AI赋能未来产业发展的重点方向和实现路径。研究建议，推动AI for science，抢占科技创新制高点；推动AI for productivity，培育新质生产力；推动 AI for industries，构筑未来产业新生态；采用健全技术创新机制、健全要素支撑机制、健全场景驱动机制、健全产业规则体系的发展路径，确保AI和未来产业协同、有序、高质量发展。

Future industries represent the directions of future technology and industrial development, and serve as a fundamental carrier for cultivating new-form productive forces. Artificial intelligence (AI) drives paradigm shifts in scientific research, facilitates efficiency improvement and transformations in production methods, and reshapes industrial development patterns. Based on the theory of new-form productive forces and the tech-economic paradigm framework, this study constructs an element-scenario-rule triadic framework to clarify AI's role in empowering future industries. Specifically, it clarifies the threefold logic of AI-driven scientific and technological innovation (AI for science), AI-enabled productivity transformation (AI for productivity), and AI-facilitated industrial upgrading (AI for industries). Furthermore, this study examines the key challenges in AI development, including industrial order and employment structure shifts, data privacy and ethical security concerns, energy consumption and computing cost issues, as well as the gap between technology and application. In response, the strategic priorities and implementation pathways for empowering future industrial development through AI are defined. The research proposes recommendations to comprehensively advance the "AI+" initiative, aiming to seize the strategic high ground in sci-tech innovation, cultivate new-form productive forces, and build a future-oriented industrial ecosystem. This entails strengthening four core mechanisms: AI technology innovation, scenario-driven application, factor support infrastructure, and industrial governance framework, thereby advancing the synergistic, orderly, and high-quality integration of AI with future industries.