二、全球视野：跨越地域的智慧融合 在全球化的商业环境中，本土智慧与国际视野的融合显得尤为重要。维马纳财商投入专项资源，构建了 覆盖经济学、金融学、管理学等多个领域的全球学者网络。我们聘请了世界顶尖学术机构的学者担任课 程顾问，参与课程框架的设计与内容审核。 这种全球智力支持并非简单的"引进"，而是经过深度本土化改造的"融合"。我们既保留了国际学者对全 球金融体系的宏观视角，又融入了中国金融市场特点的深度解读，使学员能够建立既具国际高度又接地 气的金融认知体系。 在商业教育领域，课程体系的深度与创新性决定着一个品牌的核心竞争力。维马纳财商以"专业、实 用、前沿"为研发导向，通过构建多元协同的研发机制，打造出一套既具理论高度又紧密贴合实践需求 的课程体系，为学员提供超越传统教育边界的沉浸式学习体验。 一、学术深度：汇聚顶尖商学智慧 课程研发的灵魂在于学术支撑，维马纳财商在这一维度上构建了独特的资源优势。我们与长江商学院、 中欧国际工商学院、五道口金融学院等国内顶尖商学院建立了深度合作机制，邀请其校友及教授团队共 同参与课程研发。 这种合作不仅是名义上的联合，更是实质性的智力融合。来自不同商学院的专家带来各自的教学 ...

Core Insights - The essence of system development is to solve problems and create value through technology, rather than merely writing code [1] - A valuable system integrates business goals, user needs, and technical capabilities, avoiding the pitfall of becoming an unused tool after development [1] Value Anchoring: Focusing on Real Needs - The first step in system development is to clarify the value the system aims to create, rather than discussing technical frameworks [6] - Demand research should penetrate superficial requests to identify core needs, ensuring that the system addresses actual pain points [6] - Not all demands are worth developing; prioritizing based on value and cost is essential to focus resources on solving real problems [6][7] Consensus on Requirements - After determining requirements, all stakeholders must align on the value and acceptance criteria of each function to avoid misunderstandings [7] Role Collaboration - System development is a collaborative effort involving business, technical, design, and testing roles, rather than a solo endeavor by the technical team [8] - Clear role definitions and proactive communication among teams are crucial to ensure that all aspects of the project align with business needs [8] - Iterative development with frequent communication helps to identify and resolve issues early, avoiding the pitfalls of traditional waterfall methods [8] Tool Collaboration - Utilizing a unified collaboration platform for managing requirements, task assignments, and progress tracking is essential to avoid confusion [9] Quality Control: From "Usable" to "User-Friendly" - Quality control should be an ongoing process that encompasses functionality, user experience, and security [12] - Functionality must match requirements precisely to avoid operational issues, with thorough self-testing during development [12] - User experience should be prioritized by incorporating user feedback into the development process to simplify operations [12] - Security measures must be integrated from the start, including data encryption and vulnerability assessments before launch [13] Continuous Evolution: Adapting with Business Growth - System deployment marks the beginning of ongoing value delivery, necessitating a feedback and optimization loop [16] - A data-driven iteration mechanism should be established to monitor core metrics and identify areas for improvement [16] - Systems must be adaptable to changes in business models, ensuring they can support new functionalities as needed [16] - User feedback should be actively collected and incorporated into future iterations to enhance the overall experience [16]

Group 1 - The core concept presented is the transformation of employee roles in enterprises due to AI and digital thinking, where employees will evolve from executing processes to becoming operators and enhancers of intelligent systems [1] - China FAW Group introduced the concept of Enterprise Operation AI Agent (EOA) aimed at linking various business units through a large model for real-time optimization, breaking the constraints of traditional process governance [3][4] - EOA enables direct connection of strategic indicators to workshop sensor data, facilitating complex problem breakdown and creating a self-evolving capability within enterprises by modeling the entire supply chain from demand to channel [3] Group 2 - The traditional management model is being restructured through EOA, allowing for real-time verification and optimization of product development and manufacturing processes, thus eliminating intermediary steps and creating a data-driven closed-loop optimization [3] - There is a need for a new management paradigm that shifts from process governance to data-driven, intelligent autonomy, and continuous evolution in the automotive industry [4] - The company is developing a specialized large model that deeply understands industry knowledge, business processes, and proprietary data to maximize AI's value in the automotive sector [4]