Group 1 - The 2025 ITValue Summit focused on the theme "The Truth of AI Scene Implementation," addressing ten core issues in AI application for enterprises, including strategy, reliability, data challenges, scenario selection, model selection, industry implementation, knowledge base construction, security compliance, human-machine collaboration, and talent bottlenecks [1] - During the summit, five closed-door meetings were held covering various topics and industries, allowing participants to discuss specific industry challenges in depth [1] Group 2 - Many small and medium-sized manufacturing enterprises face challenges in digital transformation, with 90% of their data remaining "asleep" due to a lack of unified data and business process standards [2][3] - The digitalization of supply chains is evolving from merely moving procurement online to achieving end-to-end collaboration and optimization through data integration [2] Group 3 - Companies like Shenzhen Genesis Machinery are integrating AI large model technology to break down data silos and enhance data sharing and value release [3] - The lack of standardization in business and data processes is a fundamental issue, particularly in non-standard manufacturing, where unique project characteristics complicate data integration [3] Group 4 - AI and data technologies are increasingly being applied to enhance supply chain transparency, responsiveness, and risk management [5] - Companies are utilizing AI to analyze historical sales and inventory data to predict risks, such as chip price increases, allowing proactive inventory management [6] Group 5 - The manufacturing sector's AI application differs significantly from the internet industry, focusing on "small data" and "scenario closure" rather than large models [6][7] - The core of successful digital transformation in manufacturing lies in standardization, followed by system implementation, data collection, and AI modeling [4] Group 6 - The financial sector is exploring AI infrastructure to address industry pain points, with companies like JD Cloud leveraging their diverse data advantages to enhance AI model training and application [10] - The successful application of AI in enterprises hinges on data quality, identifying suitable business scenarios, and establishing a supportive organizational structure [11][12] Group 7 - The retail industry is undergoing significant changes, with CIOs emphasizing the need to adapt to evolving consumer behaviors and market trends [19][20] - Successful retail operations require a focus on creating value for consumers and leveraging technology to enhance customer engagement [21] Group 8 - The hospitality and airline industries are integrating AI into their operations, with companies like East China Airlines deploying AI applications to improve efficiency and customer service [22][24] - The transition to AI-driven solutions in these sectors involves overcoming initial high costs and ensuring leadership commitment to AI initiatives [23][24] Group 9 - The CIOxCFO closed-door meetings highlighted the importance of collaboration between IT and finance leaders in driving AI implementation [25][26] - Key factors for successful AI application in enterprises include high-quality data accumulation, focusing on high-value business scenarios, and continuous operational improvement [27][30]

ITValue txy景体集团 MTPOST GROUP WPost asso | ITValue | > 2025 x 11: 深圳工业展 | 工/DJ AI+8动下的 寸链变革 产业升级 I 供应链数字化并非新话题，但在非标制造、项目式生产的行业背景下，其内涵正在发生深刻变化。它不再仅仅是把采购流程从线下搬到线上，而是要通过数 据打通实现端到端的协同与优化。 深圳市创世纪机械有限公司信息管理中心总监王恒信分享了企业的真实挑战："我们虽然各类系统齐全，例如：SAP、PLM、MES、WMS、SRM、CRM， 但系统之间数据缺乏有效整合，形成了数据孤岛。" 这导致业务数据无法贯通，企业因而缺失全局视角以支撑高效决策。为破此局，创世纪已引入AI大模型 技术，通过自然语言交互，智能关联与整合各系统数据。通过直接输出全局化的分析洞察，彻底打破数据壁垒，实现数据的高度共享与价值释放。 标准缺失的问题则更为根本。令狐荣茂深刻指出："很多企业其实并没有去做业务标准和数据标准的设计，直接跳入上系统、干自动化。"这种本末倒置的做 法导致即使上了最先进的系统，也因为缺乏统一标准而无法发挥效用。特别是在非标制造领域，边设计、边生产 ...