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Article(id=1309226307783852051, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1747670400000, receivedDateStr=2025-05-20, revisedDate=1755446400000, revisedDateStr=2025-08-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1790073476024, onlineDateStr=2026-09-22, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790073476024, onlineIssueDateStr=2026-09-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790073476024, creator=13701087609, updateTime=1790073476024, updator=13701087609, issue=Issue{id=1309226306445865584, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='6', pageStart='1121', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790073475705, creator='13701087609', updateTime=1790073520980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309226496414286647, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309226496414286648, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1169, endPage=1177, ext={EN=ArticleExt(id=1309226307976790036, articleId=1309226307783852051, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=Development trends of AI-enabled digital transformation in manufacturing, columnId=1309226307205034609, journalTitle=Studies in Science of Science, columnName=Hot Issues, runingTitle=null, highlight=null, articleAbstract=

Under the backdrop of the profound restructuring of the global manufacturing competitive landscape and the emergence of a new wave of technological revolution, AI-enabled digital transformation in manufacturing has become a Strategic Initiatives for breaking through industrial upgrading bottlenecks and cultivating new quality productive forces. Through systematic literature review and analysis of global trends in AI-driven manufacturing transformation, this study reveals the evolutionary patterns, global competitive dynamics, and future challenges of AI-empowered digital transformation in manufacturing. Key findings include: (1) AI technology penetration exhibits phased leapfrogging characteristics, evolving from single-point applications and partial process optimization to systemic empowerment. This transformation is driven by a three-dimensional mechanism—technology infiltration, process reengineering, and ecosystem reconstruction—that comprehensively accelerates manufacturing digitalization. (2) The global landscape demonstrates five salient features: data elements becoming strategic assets, systemic enhancement of full-chain efficiency, structural transformation of corporate operational logic, accelerated strategic deployment by major economies, and the emergence of AI governance as a global common agenda. China has developed a distinctive pathway leveraging its "technology-market-industry" synergistic advantages. (3) Three-tier challenges persist: technical barriers including data silos and algorithmic black boxes, industrial constraints involving green transition pressures and standardization gaps, and geopolitical tensions over technological dominance and fragmented governance frameworks. The study proposes future research priorities: innovation in AI foundational logic and its impact on reshaping global manufacturing geography, pathways for AI-enabled green-digital synergy in sustainable manufacturing, and strategies to navigate international AI competition toward an open, win-win global community. This research provides a systematic framework for understanding the strategic value, implementation pathways, and global competition patterns of AI-driven manufacturing digitalization, offering policy implications for developing new quality productive forces and advancing China's manufacturing powerhouse construction.

, authors=Jian-xuan LI, Jia-hao JIANG, authorsList=Jian-xuan LI, Jia-hao JIANG, authorCompany=null, correspAuthors=Jia-hao JIANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309226310073942039, articleId=1309226307783852051, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=AI赋能制造业数字化转型发展态势, columnId=1309226307364418163, journalTitle=科学学研究, columnName=热点议题, runingTitle=null, highlight=null, articleAbstract=

在全球制造业竞争格局深度重构与新一轮科技革命交汇的背景下,AI赋能制造业数字化转型已成为突破产业升级瓶颈、培育新质生产力的战略举措。本文通过系统性梳理国内外文献,结合AI赋能制造业转型的全球动态进行解析,揭示AI赋能制造业数字化转型的演进规律、全球竞争态势与未来挑战。研究发现:(1)AI技术渗透呈现阶段性跃迁特征,已从单点技术应用、局部流程优化演进至系统性赋能阶段,通过技术渗透-流程再造-生态重构的三维作用机制,全面驱动制造业数字化转型。(2)AI赋能制造业数字化的全球态势:数据要素上升为战略资产、全链效能系统性跃升、企业底层逻辑结构性变革、主要经济体加速战略布局、AI治理成为全球共性议题,中国依托"技术+市场+产业"组合优势形成独特发展路径。(3)AI赋能制造业数字化转型存在三重挑战:技术层面存在数据孤岛与算法黑箱,产业层面存在绿色约束与标准壁垒,国际层面存在技术主导权争夺与治理体系分化。研究提出应进一步关注的研究热点:AI底层逻辑创新及重组全球制造业版图的态势、AI赋能制造业数字化与绿色化耦合重塑全球青山绿水的路径、AI赋能制造业国际博弈焦点及走向开放共赢全球命运共同体的时代趋势。本文为理解AI驱动制造业数字化转型的战略价值、实践路径及全球竞争格局提供了系统性框架,对中国发展新质生产力和建设制造强国具有决策参考价值。

, authors=李健旋, 蒋佳浩, authorsList=李健旋, 蒋佳浩, authorCompany=null, correspAuthors=蒋佳浩, authorNote=

李健旋(1985-),女,副教授,博士。

, correspAuthorsNote=
蒋佳浩(1996-),男,硕士研究生,,E-mail:。
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李健旋(1985-),女,副教授,博士。

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李健旋(1985-),女,副教授,博士。

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AI赋能制造业数字化转型发展态势
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李健旋 , 蒋佳浩
科学学研究 | 热点议题 2026,44(6): 1169-1177
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AI赋能制造业数字化转型发展态势
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李健旋, 蒋佳浩
作者信息
  • 河海大学商学院, 江苏南京 211100
通讯作者:
蒋佳浩(1996-),男,硕士研究生,,E-mail:。
作者简介:

李健旋(1985-),女,副教授,博士。

Development trends of AI-enabled digital transformation in manufacturing
Jian-xuan LI, Jia-hao JIANG
Affiliations
  • Business School, Hohai University, Nanjing 211100, China
出版时间: 2026-06-15
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在全球制造业竞争格局深度重构与新一轮科技革命交汇的背景下,AI赋能制造业数字化转型已成为突破产业升级瓶颈、培育新质生产力的战略举措。本文通过系统性梳理国内外文献,结合AI赋能制造业转型的全球动态进行解析,揭示AI赋能制造业数字化转型的演进规律、全球竞争态势与未来挑战。研究发现:(1)AI技术渗透呈现阶段性跃迁特征,已从单点技术应用、局部流程优化演进至系统性赋能阶段,通过技术渗透-流程再造-生态重构的三维作用机制,全面驱动制造业数字化转型。(2)AI赋能制造业数字化的全球态势:数据要素上升为战略资产、全链效能系统性跃升、企业底层逻辑结构性变革、主要经济体加速战略布局、AI治理成为全球共性议题,中国依托"技术+市场+产业"组合优势形成独特发展路径。(3)AI赋能制造业数字化转型存在三重挑战:技术层面存在数据孤岛与算法黑箱,产业层面存在绿色约束与标准壁垒,国际层面存在技术主导权争夺与治理体系分化。研究提出应进一步关注的研究热点:AI底层逻辑创新及重组全球制造业版图的态势、AI赋能制造业数字化与绿色化耦合重塑全球青山绿水的路径、AI赋能制造业国际博弈焦点及走向开放共赢全球命运共同体的时代趋势。本文为理解AI驱动制造业数字化转型的战略价值、实践路径及全球竞争格局提供了系统性框架,对中国发展新质生产力和建设制造强国具有决策参考价值。

制造业  /  数字化转型  /  AI赋能  /  态势

Under the backdrop of the profound restructuring of the global manufacturing competitive landscape and the emergence of a new wave of technological revolution, AI-enabled digital transformation in manufacturing has become a Strategic Initiatives for breaking through industrial upgrading bottlenecks and cultivating new quality productive forces. Through systematic literature review and analysis of global trends in AI-driven manufacturing transformation, this study reveals the evolutionary patterns, global competitive dynamics, and future challenges of AI-empowered digital transformation in manufacturing. Key findings include: (1) AI technology penetration exhibits phased leapfrogging characteristics, evolving from single-point applications and partial process optimization to systemic empowerment. This transformation is driven by a three-dimensional mechanism—technology infiltration, process reengineering, and ecosystem reconstruction—that comprehensively accelerates manufacturing digitalization. (2) The global landscape demonstrates five salient features: data elements becoming strategic assets, systemic enhancement of full-chain efficiency, structural transformation of corporate operational logic, accelerated strategic deployment by major economies, and the emergence of AI governance as a global common agenda. China has developed a distinctive pathway leveraging its "technology-market-industry" synergistic advantages. (3) Three-tier challenges persist: technical barriers including data silos and algorithmic black boxes, industrial constraints involving green transition pressures and standardization gaps, and geopolitical tensions over technological dominance and fragmented governance frameworks. The study proposes future research priorities: innovation in AI foundational logic and its impact on reshaping global manufacturing geography, pathways for AI-enabled green-digital synergy in sustainable manufacturing, and strategies to navigate international AI competition toward an open, win-win global community. This research provides a systematic framework for understanding the strategic value, implementation pathways, and global competition patterns of AI-driven manufacturing digitalization, offering policy implications for developing new quality productive forces and advancing China's manufacturing powerhouse construction.

manufacturing  /  digital transformation  /  AI-enabled  /  trends
李健旋, 蒋佳浩. AI赋能制造业数字化转型发展态势. 科学学研究, 2026 , 44 (6) : 1169 -1177 .
Jian-xuan LI, Jia-hao JIANG. Development trends of AI-enabled digital transformation in manufacturing[J]. Studies in Science of Science, 2026 , 44 (6) : 1169 -1177 .
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DeepSeek以开源方式横空出世,引发全球范围爆炸式反响,更迅速促进了中国生成式AI的普遍而深入的应用。AI赋能制造业数字化转型,已成为全球主要国家的战略重点和制造业企业的发展方向,成为中国制造业由大到强高质量发展的关键和契机[1]。发达国家依靠科技创新先发优势,率先推进制造业数字化转型升级,科技含量持续提升、生产成本不断降低,给我国制造业带来了中低端锁定风险[2]。当前全球主要国家正加速推进AI与制造业深度融合,抢占制造业创新发展的优势地位。美国通过《国家先进制造战略计划》布局工业互联网、数字孪生等技术,2024年制造业AI渗透率已达23%,工业机器人密度达390台/万人。德国以工业4.0为核心,依托西门子、博世等头部企业构建智能工厂生态,AI驱动的预测性维护系统使设备停机时间减少40%。日本聚焦机器人产业链,2024年工业机器人全球市场份额超50%,丰田通过AI视觉检测技术将缺陷率降至0.01%。中国加快人工智能、大数据、云计算、5G、物联网等信息技术与制造全要素深度融合,推广应用新型传感、先进控制等智能部件,加快推动智能装备和软件更新迭代,推动制造业数字化转型[3]。AI赋能制造业数字化转型问题,已经是多年来尤其是近五年来学术界研究的热点议题。许多学者研究了AI赋能制造业数字化转型的背景、技术、组织、生态、政策、绩效及作用机制等。但对于AI赋能制造业数字化转型的战略价值认识还不够到位,对于AI赋能制造业数字化的核心要素和驱动机制的理解还不够深刻,对于AI赋能制造业企业创新迭代的前瞻还不够透彻,尤其是对于AI赋能制造业发展的态势及挑战分析还比较缺乏。本论文的贡献在于结合AI赋能制造业转型的全球动态,通过系统性文献梳理,解析了AI赋能制造业数字化阶段特征、全球态势、未来方向,提出了三个重要结论。
1 AI赋能制造业数字化转型从单点赋能进化到系统性赋能
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关于AI赋能制造业转型的研究文献,大致可以归纳为以下三个方面。
1.1 制造业数字化转型迈入AI赋能的3.0阶段
制造业数字化包括数字产业化和产业数字化。数字产业化实际上就是通过技术研发培育、壮大和发展新兴制造业的创新过程,产业数字化是指应用数字技术和数据资源赋能制造流程优化,通过全链条(研发、生产、供应链)渗透为传统制造业带来产出增加和效率提升的融合过程[4]。
制造业数字化转型是指通过数字技术对制造系统进行全要素、全流程的重构,实现技术架构革新与产业价值提升的过程。从技术维度看,其本质是通过物联网、边缘计算等新型基础设施,构建智能联接、存储和算力协同的制造业数字化体系,打破传统生产系统的信息孤岛,通过算法优化赋能设备自主决策,推动制造流程从“经验驱动”向“数据驱动”跃迁。从产业维度看,制造业数字化以数据要素为核心驱动价值链重塑,突破传统机械自动化对人工编程的依赖,通过AI技术实现工业机器人场景切换时间大大缩短,显著提升生产柔性,催动了制造业向价值链中高端攀升[5]。
总体上看,全球制造业已进入制造业数字化转型的3.0阶段,即AI全面赋能发展阶段。在数字化1.0阶段,主要以数控机床应用为标志,推动机械制造向精密制造转型;在数字化2.0阶段,主要依托互联网实现协同制造,形成敏捷制造新体系;在数字化3.0阶段:基于AI算法、算力集群和大模型技术,实现制造业全产业链智能化,其典型特征表现为生产全流程自动化和决策自主化[6]。制造业数字化转型3.0阶段,AI系统性赋能体现在三个方面:一是流程再造。通过增加机器人和自动化解决方案,如运用AI视觉检测而极大降低产品缺陷率,并提升生产效率、改善产品质量、降低运营成本等。二是生态重构。通过开源开放的技术路径降低企业创新门槛,吸引激励更多研发者加入创新队伍,加速科技创新进程、提升科技创新成果转化效率,持续优化制造业创新发展生态。三是价值延伸。制造业数字化转型推动制造业从“产品交付”向“定制生产、及时交付、服务保障”的数字化服务型制造转变,大幅提高全员生产率和综合经济效益[7]。
1.2 AI系统性赋能制造业企业数字化转型的路径与价值
制造业企业数字化转型,通常与企业整体战略相结合,以战略定位为起点,通过识别优势、劣势、机会与威胁,运用网络平台、AI算法以及数字孪生系统等赋能实施。制造业企业数字化转型,通常以组织模式变革为基础,借助云端系统与视觉质检模块,实现生产流程关键工序数字化覆盖,形成能够持续优化的数字化转型新组织体系,从而保证转型的技术和经济效益[8]。
制造业企业数字化转型路径选择,具有显著的行业异质性特征。离散制造业多采用“渐进式”路径,通过设备智能化改造逐步向系统集成升级;而流程工业多选择“跃迁式”转型,采取全链条赋能方式实施。这种差异化路径表明,制造业企业数字化转型,既要充分考虑技术创新应用前景,更需要契合产业特性与企业自身技术能力,需要系统性思维和前瞻性布局[9]。
AI赋能制造业数字化转型,正通过数字技术、智能算法与绿色理念的深度融合,重构现代工业的价值创造范式。以宁德时代为代表的头部企业,创造性构建“人工智能+数字孵生+零碳工厂”的集成创新体系,通过虚拟仿真优化生产参数,AI算法实时调控能效,实现单位产能能耗降低28%、生产成本缩减19%的突破性进展。美的集团打造的工业互联网平台,整合了覆盖全生命周期的能耗监测体系与动态优化算法,在空调制造领域实现碳排放强度下降33%的同时,同步提升设备综合效率(OEE)12.6%。华为云与山东重工共建的智慧重卡生态系统,依托车联网实时采集的运营大数据,通过深度学习算法持续优化运输路径规划,成功将车辆空驶率从38%压缩至12%,创造了年节约燃油成本超2.3亿元的巨大价值。实践表明,制造业数字化转型正从单体企业创新向产业生态重构跃迁,不仅推动生产要素的重新配置效率,更在产业层面形成可持续发展的新型能力体系,为发展新质生产力增添了重要动能[10]。
1.3 AI系统性赋能制造业数字化转型的三维作用机制
AI系统性赋能制造业数字化转型,主要是通过技术渗透、流程再造与生态重构的三维度复合而发挥作用的演化过程。
从技术渗透维度看,AI赋能制造业系统实现从“数据感知”向“认知决策”跃迁。以生成式AI为例,美国欧特克公司开发的衍生式设计工具可自动生成数万种零部件方案,使产品迭代周期缩短50%以上。中国商飞公司利用AI驱动的复合材料缺陷检测系统,将检测精度提升至99.7%[11]。当前AI技术正加速渗透至生成式设计、机器视觉、智能控制等领域,发挥着优化设计流程、缩短开发周期、提升设计效率等显著功效,广泛深入地促进了制造业数字化转型进程。
从流程再造维度看,AI赋能改变了传统线性生产流程,实现实时数据流与多目标优化,改写了制造业增值的运行逻辑。如德国西门子依托AI生产调度系统,实现订单排产效率提升40%、库存周转率提高35%。日本发那科通过AI视觉引导的柔性装配线,使多品种混线生产切换时间从2小时压缩至15分钟。中国三一重工基于设备运行数据反哺研发设计,使新产品故障率降低60%,形成“制造即服务”的新型价值创造模式,优化了生产流程[12]。
从生态重塑维度看,AI赋能制造业数字化转型,推动传统制造业向制造网络转型,创造了全新的制造业发展生态,改变了全球制造业产业竞争格局。美国英伟达的Omniverse平台连接全球4700家制造企业,构建了跨组织协同的虚拟制造生态,使供应链协同效率提升3倍。中国“东数西算”工程结合AI算力调度,使西部制造基地的能效利用率提升至65%,展示了区域协同的巨大潜能[13]。
2 AI赋能制造业数字化转型的全球态势
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AI赋能制造业数字化转型,带来了崭新的发展模式、发展路径和发展绩效。根据AI赋能制造业数字化转型的要素、动力、路径,制造业数字化与制造强国建设的关联,以及人工智能广泛应用带来的伦理与法律问题等,大致可以归纳出AI赋能制造业数字化转型的5个相互关联的全球态势,参见图1。
2.1 数据从辅助要素升级为核心战略资产
数字资源作为数字经济时代的基础性生产要素,包含数据、信息和知识等数字化形态,具有可共享、可传输和可增值的核心特性。在制造业领域,数据要素已深度融入研发设计、生产制造、运营管理和市场服务等全价值链环节。AI技术通过数据采集、传输、处理和分析的全流程赋能,可以使制造企业在数字化转型中能精准把握市场趋势、优化决策流程、提升生产效率并降低运营成本,从而显著提高产品质量和企业竞争力,为培育和发展新质生产力提供强劲支撑[14]。
全球主要国家均高度重视数据要素应用,形成了各具特色的实践方案。美国依托工业互联网联盟(IIC)建立了数据互操作标准,通用电气Predix平台日均处理150TB工业数据,显著提升供应链敏捷性。欧盟通过《数据治理法案》构建了工业数据空间(IDS),实现跨境数据流通,德国弗劳恩霍夫研究所的工业数据平台已连接1.2万家企业,数据调用效率显著提升。中国实施“东数西算”国家工程,贵阳大数据交易所2024年交易规模突破100亿元,有力支撑了跨区域制造业协同[15]。
随着AI技术与工业互联网的深度融合,制造业数字化转型发生了质变。从产品维度看,制造业正由“制造产品”向着“制造产品+数据服务”转型;从价值维度看,数据资源已从辅助要素升级为核心战略资产,其深度应用正持续释放制造业数字化转型的指数增值效应[16]。
2.2 制造业在转型中进入效能跃升阶段
随着物联网、大数据、人工智能等技术的迅猛发展,制造业正快速从机械化向自动化、智能化转型,AI技术的突破与广泛应用起到了基础和关键作用。AI赋能制造业企业,不仅再造生产流程,提升生产效率和产品质量,还推动了企业管理模式的创新,提高了管理水平和运营效率。AI赋能制造业企业,不但能够大幅缩短新产品的设计周期,而且能够激发出更具市场竞争力的创新产品和服务模式,从而实现更高的商业价值[17]。
AI赋能制造业数字化转型,已在全球范围内取得显著成效。日本发那科将AI嵌入工业机器人控制系统,实现焊接路径自优化,使汽车产线切换时间从4小时缩短至20分钟。德国博世开发应用AI驱动的能源管理平台,显著提升了整合可再生能源与制造数据的速率,帮助宝马工厂能耗降低18%。中国东莞大模型中心依托华为昇腾算力,优化板材切割AI算法,材料利用率从82%提升至97%[18]。这些案例表明,AI赋能制造业数字化转型,不仅是优化单点环节,而且是推动了制造业整体效能跃升。
AI赋能制造业企业数字化转型,增添了制造业各环节的巨大能量。在生产端,AI赋能可精准识别生产瓶颈,优化工艺参数,结合机器视觉与实时监测,提升质量与效益。在管理端,AI赋能可助力实现数据驱动的精准决策,提升整体业务水平。在服务端,AI赋能可强化个性化定制生产,根据客户偏好动态调整制作流程,增强用户体验与品牌忠诚度[19]。
2.3 制造业企业发展的底层逻辑发生深刻变化
AI赋能制造业数字化转型进入3.0阶段,驱动制造业系统从“工具智能化”走向“系统智能化”,带来三大变化。一是制造业数字化从单点技术应用向全链条自主决策跃升,通过工业物联网、数字孪生与AI算法深度融合,实现生产全流程的实时感知、自主分析与动态优化。二是通过开放共享的制造网络,突破企业边界实现产业链级协同,实现从孤立工厂向网络化、分布式智能制造业系统转型。三是通过AI赋能实现用户需求、服务响应与价值增值有机集成,提升综合经济效益[20]。
当前全球不少优秀制造业企业已进入数字化转型的3.0阶段。美国洛克希德·马丁公司采用数字主线技术贯通F-35战机全生命周期数据,大幅提升研发效率。德国西门子公司的工业元宇宙平台通过高保真数字孪生技术,同步模拟生产参数,使得故障预测准确率升到92%;AI赋能订单排产使得效率提升40%。中国赛力斯智慧工厂部署AI视觉引导系统,机械臂定位精度达±5μm,使得产品不良率下降76%;中国商飞AI缺陷检测系统通过深度学习实现复合材料质量自主判定,检测精度达99.7%[21]。
AI赋能制造业数字化转型,正深度改写制造业企业发展的底层逻辑。从技术维度看,通过工业物联网、数字孪生和智能决策系统,制造业企业数字化转型可以实现精准的环境分析和科学决策。从组织维度看,制造业企业数字化转型可以借力“人类专家+AI系统”的组织模式进行创新。从经济维度看,AI赋能创造了按需定制、协同制造、共享制造等新制造业态,不仅提升了生产效率,更显著提升了附加价值[22]。
2.4 AI赋能制造业数字化转型上升为国家战略性部署
AI驱动的制造业数字化转型,已成为建设制造强国的战略支点。AI赋能制造业数字化转型的核心价值,大致可归结为三个方面:一是在系统整合方面,通过融合数字技术、智能平台和工业网络,制造业企业可以实现研发-生产-服务全链条贯通;二是在协同创新方面,AI赋能制造业数字化转型,可以促进产业链纵向协同和区域间横向联动,提升整体创新效率;三是在产业变革方面,AI赋能制造业数字化转型,既改造传统制造模式,又培育新兴制造产业,可以为发展新质生产力提供强大动能[23]。
全球主要国家均高度重视AI赋能制造业数字化转型,积极抢占主动权和制高点。美国积极布局云计算、大数据、先进制造、5G、量子通信等前沿领域,其工业互联网渗透率已达38%。德国工业4.0标准已覆盖85%的制造企业,日本垄断工业机器人三大核心部件(伺服系统/控制器/减速器),其市场份额均占到全球70%左右。韩国实施《数字新政2.0》,智慧工厂普及率三年提升了27%[24]。
中国运用AI赋能制造业数字化转型,已形成独特的发展模式和优势。在技术基础设施方面,工业互联网已覆盖全部工业大类,5G基站占全球60%以上。在市场生态方面,云计算产业年均增速超过30%,催生个性化定制的迅猛增长。在产业效能方面,完整的产业链使得AI赋能成本大幅降低,数字化转型周期缩短许多,效率和效益显著提升。这种“技术+市场+产业”的组合赋能,正推动中国由制造大国向制造业强国快速稳健迈进[25]。
2.5 AI治理成为全球共同面对的前沿问题
AI赋能制造业数字化转型的同时,催生出复杂的伦理与法律挑战。当数据要素通过AI赋能广泛融入制造业生产流程之时,数据主权争议已突破传统隐私保护范畴,比如制造企业核心数据资产、用户生物特征信息、个人行为数据等多维度敏感信息的交汇,不仅威胁商业机密与个人隐私,更触及人类主体性认知、技术异化风险等深层次伦理命题。据研究显示,全球67%的制造业AI应用存在数据权属模糊问题,构建科学合理的适配AI时代的数据治理框架已成为广泛关注的热点议题[26]。
面对技术伦理的严峻挑战,全球主要国家正加速构建AI治理体系。欧盟《人工智能法案》首创工业机器人“高风险应用”分类监管,通过算法透明性审查强化技术问责。美国《可信AI制造框架》强制要求人机协作系统必须集成动态伦理评估模块,实现道德约束的工程化落地。中国实施《生成式人工智能服务管理暂行办法》,构建起包含安全评估、算法备案、分级监管的三维治理体系,通过监管机制平衡创新发展与风险防控。截至2024年,中国制造业已有302款AI工具通过合规审查,形成全球最大的智能制造伦理实践样本[27]。
破解AI赋能的伦理困境,需要“技术-产业-法规”的协同治理。在技术层面,应建立基于人类尊严与技术向善的伦理评估框架,将道德考量嵌入AI研发全生命周期。在产业层面,应引导企业将隐私保护设计融入数字化转型的各环节。在法规实施层面,需完善数据确权、算法问责、损害救济等法律体系,筑牢法规支撑发展的安全屏障[28]。
3 未来亟待关注的研究热点
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根据研究文献分析和重要态势解析,进一步展望AI赋能制造业数字化转型的研究方向,我们认为以下三个方面应是持续研究的热点领域。
其一,人工智能赋能的底层逻辑及创新前景是什么?AI赋能将如何重组全球制造业版图?
从核心技术来看,制造业数字化有三个阶段。一是20世纪40-60年代,以计算机和数控机床为代表的单点自动化阶段;二是70-90年代随着CAD/CAM和CIMS系统普及形成的流程数字化阶段;三是21世纪以来依托互联网技术实现的网络化协同阶段。从数字化转型特征来看,制造业迈过了“分布式CAD系统-自适应的柔性制造产线-制造业全链条数字化网络”的历程,制造业的数字化渗透率逐步提升,显著优化了资源配置效率。
当前,AI技术正引发制造业发生新变革,核心是从“工具智能化”向“系统自主化”升迁。生成式AI通过构建制造知识图谱,形成了具有自组织、自优化特性的新型生产系统:美国借助Diffusion模型将芯片设计周期压缩98%,德国工业GPT实现工艺参数实时动态优化,中国DeepSeek-R1开源大模型突破了“多模态制造知识融合、小样本快速迁移、分布式计算优化”三项关键技术瓶颈,开创了中国AI赋能发展的新局面。据麦肯锡研究,AI驱动的新型制造模式使产品迭代速度提升4-8倍,研发成本降低30%-50%,正在重构着全球制造业竞争力格局[29]。
进一步展望:当AI开始具备制造知识的创造能力时,会发生底层逻辑的根本性变革吗?在技术维度,构建具有因果推理能力的工业认知智能体,会不会突破数据驱动的工具局限性?在产业维度,无人工厂的终极形态是替代人类还是创造“超能工人”的新工种?在文明维度,伦理防火墙可以防止制造系统异化为“人造巨兽”吗?这些问题的探讨将直接影响AI赋能制造业数字化的演进方向,应会持续成为相关研究的热点话题。
其二,AI赋能制造业数字化转型如何与绿色约束深度耦合?AI全面深度赋能制造业同时能否带来全球化青山绿水?
全球气候治理加速重构制造业竞争规则,绿色化已成为制造业数字化转型的核心约束要素。发达国家凭借碳核算体系与绿色技术先发优势,已建立起覆盖全球85%贸易量的碳边境调节机制,形成“绿色壁垒”。中国作为全球唯一拥有全部工业门类的国家,工业碳排放占全国总量40%(钢铁、水泥等高耗能行业排放占比超80%),单位GDP能耗仍是世界平均水平的1.5倍,需持续努力从根本上破解“资源-环境-增长”三角困境[30],AI赋能中国制造业数字化转型,能否实现智能化和绿色化的深度耦合及协同共进?
AI技术重塑全球绿色制造体系,推动减排模式从末端治理转向全过程优化。在技术层面,德国弗劳恩霍夫协会开发的制造GPT实现工艺参数自优化,使钢铁生产能耗降低12%;日本通过AI驱动的电池材料基因组计划,将新能源研发周期缩短60%。在系统层面,沙特通过AI赋能零碳工业体系建设智慧城市,使得可再生能源利用率达97%[31]。AI赋能制造业数字化转型,构建数字化与绿色化深度耦合的协同体系,显然是中国制造业创新发展和实现双碳目标协同的关键路径。中国开源大模型突破多模态知识融合瓶颈,使光伏电站运维效率提升40%而间接减少了巨额碳排放,宁德时代通过AI优化电解液配比提升电池能量密度15%的同时降低生产碳排放21%,隆基绿能利用AI光伏阵列设计使单晶硅碳足迹下降33%[32]。
进一步展望:AI赋能如何驱动制造业数字化与绿色化深度耦合?基于工业互联网的碳排放动态监测体系、嵌入AI算法的绿色工艺创新、面向碳中和的智能供应链重构等相关技术突破与应用速度,能否实现AI赋能制造业数字化转型同时达到控制碳达峰实现碳中和的预期目标?类似问题可能是未来相当长时期的相关研究的热点话题。
其三,AI赋能制造业数字化转型的国际博弈焦点是什么?全球AI赋能制造业的态势是分道扬镳,还是走向全球命运共同体而实现合作共赢?
全球主要国家正以人工智能为核心展开制造业数字化转型竞赛,技术主导权和规则制定权成为国际博弈焦点。美国通过《全球AI伙伴关系》联合37国构建工业AI伦理框架,依托数十个国家级制造创新中心(涵盖芯片、生物制造、航空航天等领域)强化技术壁垒。日本以“超智能社会”战略推动机器人等优势产业数字化升级,德国通过工业4.0战略实现智能工厂规模化落地,中国依托“工业互联网+智能制造”双轮驱动战略建成全球最大规模5G+AI工业质检网络,推动电子、汽车等支柱产业良品率迅猛提升。这种全球性的技术-产业-标准的三维角逐,折射出AI赋能制造业的主导权争夺已上升至国家战略层面。
当前国际博弈呈现“民粹主义”与“开放合作”并行的悖论。美国通过“实体清单”与技术联盟构筑封锁体系,欧盟以“技术援助提供者”条款扩大管制范围,日本则参与构建针对中国的技术锁定链。与之相对,中国通过自主创新突破芯片等卡脖子技术、共建东盟智能制造实验室网络等举措,构建了“技术突围-生态共建”的共进机制。这种对抗性博弈导致全球多中心化走势,北美、东亚、欧洲三大技术生态圈并立态势雏形已现。深层次看,AI赋能制造业不仅是技术革命,更是文明形态的演进。中国应在自主创新基础上积极拓展国际合作,通过开源生态与数据要素重构全球价值链分工,为人类工业文明和人类命运共同体建设作出主导性贡献。
进一步展望:中美领先的全球AI竞赛的前景是什么?AI赋能制造业数字化转型的国际博弈焦点如何演化?美国的打压和中国的突破如何改变全球AI赋能发展格局?如何通过AI赋能使发展中国家获得制造业跃迁机会?“数字丝绸之路”会成为人类命运共同体理念在AI时代的关键路径吗?类似问题应当是未来AI赋能制造业数字化等相关研究的热点议题。
4 主要结论
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第一,AI赋能制造业数字化转型正重构产业发展的底层逻辑与技术范式。通过技术渗透、流程再造与生态重构的三维作用机制,AI不仅推动制造业从“经验驱动”向“数据驱动”跃迁,更深度改写了企业发展的技术、组织与经济逻辑。这种底层逻辑的变革标志着制造业正在从单点工具智能化迈向系统性自主化的新阶段,标志着全球产业格局正处于重塑变革之浪潮中。
第二,数据要素驱动制造业全链效能跃升成为数字化转型的核心特征。数据要素已从辅助资源升级为战略资产,AI赋能通过数据要素全面渗透到生产端、管理端和服务端,集成推动着制造业效能进入“整体跃升”阶段。这种全链条效能提升使制造业全方位突破传统线性增长模式,加速向价值链高端攀升。
第三,国家战略博弈与AI治理体系建设决定转型未来格局。全球主要国家围绕技术主导权展开激烈竞争,美国积极推进《国家先进制造战略计划》布局工业互联网,德国加速工业4.0覆盖所有企业,中国形成“技术+市场+产业”组合优势。同时,AI伦理与法律挑战催生制造业数字化转型的治理体系创新,“技术-产业-法规”协同治理模式是平衡创新发展与风险防控的关键。这种战略竞合态势,在未来相当长时期内将深刻影响全球制造业数字化转型的速度与方向。
基金
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  • 国家自然科学基金面上项目(72472045)
  • 国家自然科学基金青年项目(72102061)
文献
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2026年第44卷第6期
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  • 接收时间：2025-05-20
  • 首发时间：2026-09-22
  • 出版时间：2026-06-15
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  • 收稿日期：2025-05-20
  • 修回日期：2025-08-18
基金
国家自然科学基金面上项目(72472045)
国家自然科学基金青年项目(72102061)
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    河海大学商学院, 江苏南京 211100

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蒋佳浩(1996-),男,硕士研究生,,E-mail:。
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