᪈ 㾱 ൘ӪᐕᲪ㜭 AI ᢰᵟ傡ࣘлˈᡁഭ୶、Ӫษޫփ㌫↓䶒Ѥ␡Ⲵᡈоࡽ ᡰᵚᴹⲴᵪ䙷DŽᵜ⹄ウᰘ൘ሩ䘉аփ㌫Ⲵ⧠⣦ǃ䰞仈оਁኅ䐟ᖴ䘋㹼㌫㔏ᙗ䇺ᯝDŽ Ѫᇎ⧠↔ⴞḷˈᵜᣕ䟷⭘Ҷᇊ䟿䰞ধ䈳ḕ⌅ˈሩ 28 ᇦ䴦䘎䬱Աъǃ283 ൘ṑᆖ⭏৺ 58 ԭ儈ㅹ䲒ṑ䰞ধ䘋㹼Ҷᇎ䇱䈳⹄ˈᰘ൘Ӿӗъ䴰≲ǃ䲒ṑ㔉о ॿ਼ᵪࡦйњ㔤ᓖˈ⽪ᖃࡽӪษޫⲴ⧠⣦ǃṨᗳ⸋оᵚᶕਁኅ䐟ᖴDŽ ⹄ウਁ⧠ˈᖃࡽ 、୶AI ᮠᆇॆӪษޫᆈ൘ᱮ㪇Ⲵ㔃ᶴᙗཡ㺑о䴰呯⋏DŽ ൘ӗъ䴰≲ㄟˈԱъሩ㜭ሶъ࣑䰞仈䖜ॆѪ AI 䀓ߣᯩṸⲴ༽ਸරǃᓄ⭘රӪ 䴰≲ᶱѪ䘛࠷ˈަṨᗳ㜭࣋ᵏᵋ⧠ࠪъ࣑⨶䀓>ᮠᦞ࠶᷀<ᐕާᇎⲴᲠቲ ⅑DŽ❦㘼ˈԱъԕ䘁Ѿа㠤Ⲵޡ䇶˄96.4%˅ᤷࠪˈᖃࡽ䲒ṑ∅ъ⭏ᴰ㠤ભⲴ⸝ ᶯ൘Ҿ㕪ѿⵏᇎ୶ъ൪ᲟⲴশ㓳DŽ൘䲒ṑ㔉ㄟˈ䈮〻᭩䶙㲭ᐢᲞ䙽ࣘˈնཊ ڌ൘⮉ቁ䟿䈅⛩ቲ䶒ˈᒦ䶒Ѥᐸ䍴࣋䟿н䏣˄86.2%˅ǃᇎ䇝䇮༷㕪ѿ˄75.9%˅ оᮉᆖᇩᴤᯠ┎ਾ˄58.6%˅йབྷṨᗳࡦ㓖DŽ൘ᆖ⭏ᝏ⸕ㄟˈᆖ⭏ሩᆖҐ AI ᣡ ᴹ⎃ޤ䏓˄72.4%˅ˈնަᢰ㜭ਁኅ⧠ࠪн൷㺑⣦ᘱˈণ䙊⭘ AIGC ᐕާᓄ⭘ 㜭࣋ᕪˈ㘼уъ BIǃRPA ᐕާᓄ⭘㜭࣋ᕡˈфሶ㕪ቁᇎ ...

最近和一位做五金工贸的张总聊天，他的困惑很有代表性："去年花30万上了ERP，销售还在用Excel记客户，生产和仓库对不上库存；今年又加了CRM，结 果订单改了之后，生产部三天才收到消息，做出来的货全错了。数字化我知道要做，但第一步到底该踩哪块砖？" 张总的问题不是个例。据《2025工贸行业数字化报告》显示，62%的工贸企业尝试过数字化，但仅28%认为效果显著——很多老板要么"撒网式"投钱买系 统，要么"单点式"补短板，最终陷入"钱花了、效率没提"的怪圈。工贸企业数字化的第一步，真的不是买工具那么简单。 宝林云智企CEO 一、先搞懂：你到底在痛什么？ 要找第一步，得先拆透工贸企业的核心痛点。这些痛点往往藏在三个层面： 很多工贸企业的流程是"人治"而非"法治"： 二、别踩坑：这些错误做法正在浪费你的钱 很多老板想数字化，却第一步就走错了： 三、找对路：数字化第一步，先打通"核心业务链路" 1. 管理上的"部门墙"：信息传递靠喊 工贸企业的业务链路是"客户→订单→生产→库存→发货→收款"，但每个环节几乎都在"孤岛"里： 2. 数据上的"分散症"：报表等半天还不准 销售接了订单，用微信把需求发给生产，生产部要看库存 ...

RockFlow 投研团队第一时间对这个数万字的系列报告进行了深度编译与逻辑重塑。我们剥离了繁杂的术语，为你精炼出决定未来十年投资格局的五大核心 叙事。Enjoy AI 基础设施与 Agent：从"交互工具"进化为"自主生命体" 划重点： 1）AI 正在从"数字助理"进化为"自主执行集群"。2026 年将见证 AI 从"对话工具"向"多智能体系统（Multi-Agent）"的跨越。a16z 预言屏幕时代 即将终结，Agent 原生基建将重定义云端速度，开启企业运营杠杆的历史性飞跃。 2）科技正在溢出屏幕，"比特"开始全面接管"原子"。电气化、材料科学与 AI 融合而成的"电子工业堆栈"将成为物理世界运行的底层逻辑。通过 软件定义制造与 AI 自动化，美国有望迎来工厂复兴的黄金时代。 3）SaaS 正经历从"被动记录"到"主动推理"的范式转移，个性化服务将实现从"为所有人优化"到"为每个人定制"的飞跃。加密货币将化身为互联 网的基础结算层，稳定币与 RWA 将重构金融底层；而预防性医疗将开启长效变现的新蓝海。 在美股市场，预见趋势的能力往往决定了 Alpha 的成色。作为硅谷风投界的"定海神针"，a16z（An ...

Core Viewpoint - Traditional SaaS applications like CRM and ERP systems will not be replaced but will evolve to serve as the infrastructure for AI Agents, which will enhance the importance of data definition and interpretation within enterprises [2][10][15] Group 1: The Role of AI Agents - AI Agents will not eliminate traditional software systems; instead, they will necessitate a clearer separation between how tasks are performed and the sources of facts [2][10] - The effectiveness of AI Agents is contingent upon their ability to access and understand the correct data from various systems, highlighting the need for accurate and structured input data [2][9] - The emergence of AI Agents creates significant entrepreneurial opportunities for companies that can help businesses manage and structure their unstructured data [3][10] Group 2: Data Management Challenges - A significant portion of enterprise knowledge (80%) exists in unstructured data, which is becoming increasingly difficult to manage [2] - The complexity of data definitions within organizations leads to discrepancies in key metrics like Annual Recurring Revenue (ARR), complicating the role of AI Agents in providing accurate information [7][11] - The traditional approach of consolidating data into warehouses has only partially succeeded, as operational teams still rely on individual systems for real-time transactions [8][10] Group 3: Evolution of Systems - CRM and ERP systems will transition from user-centric interfaces to machine-oriented APIs, allowing AI Agents to interact with these systems programmatically [12][15] - The core value of enterprise systems lies in their ability to encapsulate chaotic data, which will remain essential despite changes in interface and interaction methods [13][15] - The demand for a clear, authoritative source of truth will only increase as AI Agents become more prevalent in business processes [14][15] Group 4: Future of Data Infrastructure - The combination of data warehouses, semantic layers, and governance tools will form the foundation for AI Agent workflows, evolving beyond traditional reporting systems [10][12] - The valuation of AI platforms will increasingly depend on their ability to define and manage facts, rather than just their user interfaces [14][15] - Companies that can create exceptional AI Agent experiences based on reliable data sources will have a competitive advantage in the evolving landscape [15]

Core Viewpoint - Digital transformation is often misunderstood by company leaders, leading to superficial or incomplete efforts. A deeper understanding of digitalization is essential for effective transformation [1][3]. Group 1: Misunderstandings and Issues - Many traditional company leaders equate digital transformation with merely purchasing advanced software systems, neglecting the unique aspects of their own organizations [2]. - There is a tendency to expect immediate financial returns from digital initiatives, which can lead to budget cuts or project cancellations when short-term results are not visible [4]. - Leaders often either delegate digital transformation entirely or interfere excessively in technical decisions, resulting in confusion and misalignment [4][5]. Group 2: Methodological and Cultural Challenges - Companies frequently lack a clear digital strategy and implementation roadmap, leading to ad-hoc approaches that fail to address real business needs [5]. - There is a common oversight of the human and cultural aspects of transformation, with many leaders failing to establish cross-departmental teams or address the resulting shifts in power and interests [6]. - A lack of a data-driven culture and reliance on traditional management practices hinder effective digital transformation [6]. Group 3: Recommendations for Leaders - Leaders should personally engage in learning about digitalization, lead transformation efforts, and actively participate in decision-making [7]. - It is crucial for leaders to maintain a long-term perspective, avoid rushing for quick wins, and embrace the complexities of digital transformation [7][8]. - Successful transformation requires a fundamental shift in leadership mindset, focusing on collaboration and data-driven decision-making [8].

Investment Rating - The report maintains an "Outperform" rating for the computer sector [4] Core Insights - The industrial software sector is experiencing robust growth driven by policy support and technological advancements, with a projected market size of CNY 354.14 billion in 2024, reflecting an 11.2% year-on-year increase [2][21] - The integration of AI with industrial software is accelerating, enhancing capabilities in design, production control, and operational management, which is crucial for achieving the "Made in China 2035" goals [3][49] - The market structure shows a significant presence of foreign companies in high-end segments, while domestic firms are gaining ground in production control and management software due to localized services and cost advantages [25][38] Summary by Sections Policy Support - The "14th Five-Year Plan" emphasizes the importance of intelligent manufacturing, aiming for over 70% digital penetration in large manufacturing enterprises by 2025 [2][18] - Recent policies indicate a sustained commitment to upgrading industrial software and operating systems as core technologies for high-quality development [19][20] Industrial Software Market Growth - The industrial software market is projected to reach CNY 765 billion by 2029, with a compound annual growth rate (CAGR) of 19.1% [2][21] - The market is characterized by a high share of foreign companies in high-end sectors, while domestic firms are making strides in embedded software and management solutions [25][26] AI Integration - AI is transforming industrial software, enabling generative design in CAD and enhancing simulation capabilities in CAE, which improves efficiency and reduces costs [41][42] - The combination of AI with production control systems like DCS and PLC is creating closed-loop intelligent systems that optimize decision-making and enhance production efficiency [44][47] Future Outlook - The report anticipates that industrial software will be a key focus area in the upcoming "15th Five-Year Plan," with policies likely to support breakthroughs in critical areas of design and production control [20][21] - The integration of physical AI is expected to drive advancements in various industries, enhancing simulation and predictive capabilities [66][69]

Core Insights - The article discusses the shift in China's role from being the "world's largest customer" to a "super developer," indicating a significant change in global trade dynamics [1][3][20] - European manufacturers are facing challenges as China increasingly focuses on self-sufficiency and domestic production, leading to a decline in imports from Europe [5][11][39] Group 1: Changes in Trade Dynamics - China is no longer a major importer of European high-end machinery, automobiles, and luxury goods, which has left European manufacturers searching for new opportunities [3][5] - The demand for traditional imports like soybeans and iron ore remains, but these do not significantly benefit European manufacturing [7][20] - The rise of local high-end brands in China poses a threat to European luxury brands, as Chinese consumers are increasingly favoring domestic options [9][39] Group 2: China's Manufacturing Strategy - China is investing heavily in high-end manufacturing sectors such as semiconductors, industrial software, and commercial aircraft, aiming for self-sufficiency [16][18][20] - The Chinese government views imports as temporary learning opportunities, with a focus on developing domestic capabilities to produce high-quality goods [18][20] - The "14th Five-Year Plan" prioritizes manufacturing, indicating a strategic shift towards enhancing domestic production capabilities [13][20] Group 3: Impact on Europe - European economies, particularly Germany, are projected to face economic growth declines due to China's strong export capabilities, with estimates suggesting a 0.3 percentage point reduction in growth annually [24][28] - The article highlights the existential crisis faced by European manufacturers, who must either reform to enhance competitiveness or resort to protectionist measures [28][32] - The contradiction in European expectations for China to stimulate global demand while also limiting its exports creates a complex challenge for the region [35][39]

Core Insights - Oracle Corporation is a global provider of enterprise software and cloud services, headquartered in Austin, Texas, and operates Oracle Cloud Infrastructure to deliver various computing capabilities to businesses worldwide [1] - The company has a market capitalization of $570.96 billion, classifying it as a "mega-cap" stock [2] Stock Performance - Oracle's stock reached a 52-week high of $345.72 on September 10 but has since declined by 43%, influenced by investor concerns regarding an inflated AI bubble and high sector valuations [3] - Over the past three months, Oracle's stock has decreased by 16.3%, while the Dow Jones Industrial Average has increased by 4% during the same period [3] - In the longer term, Oracle's stock has increased by 4.8% over the past 52 weeks, underperforming the Dow Jones' 5.3% gain, but has outperformed with a 26.3% increase over the past six months compared to the index's 13.2% gain [4] Financial Performance - For the first quarter of fiscal 2026, Oracle reported a 12% year-over-year revenue growth to $14.93 billion, driven by a 28% increase in cloud revenues, although it missed Wall Street's estimate of $15.01 billion [5] - The company's non-GAAP EPS increased by 6% annually to $1.47, matching analyst forecasts [5] - Following the strong cloud performance in Q1, Oracle's stock gained 36% intraday on September 10 [5] AI Initiatives - Oracle is aggressively expanding its AI capabilities, having recently enhanced its partnership with Advanced Micro Devices, Inc. (AMD) [6] - The partnership includes an initial deployment of 50,000 GPUs in Q3 CY2026, with further expansion anticipated in 2027 and beyond [6]

智通财经APP获悉，广发证券发布研报称，无论算力还是应用，未来一段时间计算机行业仍然将是以内 需TOB方向为主。国产算力包括芯片、服务器和EDA等替代加速，ERP与工业软件包括智能制造等企业 应用，智能驾驶与机器人方向，鸿蒙等国产基础软件生态推进等。社融和制造业等下游新增贷款的好转 有利于保障后续相关开支。考虑到前期行业表现较为平淡、三季报中规中矩以及前述下游开支的前瞻信 号，短期市场环境和风险偏好的影响或许有限。 广发证券主要观点如下： 下游开支的前瞻变化 ①上半年ERP和智能制造等企业应用在收入增长的同时面临下游因为关税等影响订单释放节奏的挑战。 而三季度以来PMI和社融变化等前瞻指标有积极变化。②观察社融数据的变化，该行可以预期下游扩张 在波动中回暖，相对于去年同期而言在未来1~2个季度或有改善。③PMI环比有所回落，但是ERP和智 能制造软件应用的主要下游高技术制造业、装备制造业、消费品行业的PMI指数均处于扩张区间。④因 此可以预期相关软件技术和应用公司在四季度至一季度会有更好的收入表现。 EDA领域 ①根据新思科技财报电话会议，其业绩主要受到IP业务表现不佳的影响，原本预计会达成的一些交易并 未实 ...

Group 1: Industry Insights - The Ministry of Industry and Information Technology (MIIT) released the "Guidelines for High-Standard Digital Park Construction," aiming to establish benchmarks for enterprise digital transformation [1] - The guidelines emphasize the importance of digital transformation in manufacturing, utilizing a comprehensive evaluation index system to guide enterprises in their digital upgrades [1] - AI is expected to enhance operational efficiency across all manufacturing processes, leading to a reduction in R&D cycles by approximately 20.7%, an increase in production efficiency by about 34.8%, a decrease in defect rates by around 27.4%, and a reduction in carbon emissions by approximately 21.2% [1] - The manufacturing sector is shifting from a product-centric to a user-centric model, focusing on flexible production to meet personalized consumer demands [1] - The market size for AI applications in China's manufacturing industry has maintained a growth rate of over 40% since 2019, projected to reach 14.1 billion yuan by 2025 [1] Group 2: Company Developments - Dingjie Zhizhi has integrated AI applications into its ERP, PLM, and BI systems, enhancing the fusion of AI with its product matrix [2] - Saiyi Information has partnered with Huawei to launch the iMOM product series, focusing on key manufacturing sectors such as electronics, automotive, and equipment manufacturing to facilitate industrial collaboration [2]