Core Viewpoint - The AI industry is on the brink of significant transformation, with the emergence of Agentic AI and the need for AI applications to penetrate professional scenarios, particularly in the legal sector, where error tolerance is extremely low [1][8][30] Group 1: AI Productization and Application - The challenge lies not in making AI products but in ensuring that AI applications can effectively assist legal professionals in their tasks [2] - Many companies have digitized contract management, but most remain in the early stages of digital collaboration, relying heavily on manual review for non-standard contracts [6][7] - iTerms Pro, developed by 法大大, is designed to perform tasks like intelligent contract review and compliance monitoring, showcasing a collaborative approach between AI and legal professionals [8][17] Group 2: Legal Digitalization and Compliance - Legal digitalization has been established among medium to large enterprises, but the vision of AI-human collaboration to drive business processes still has a long way to go [6][21] - The introduction of new regulations, such as the Data Security Law and Personal Information Protection Law, has prompted legal departments to shift from passive responses to proactive risk management [7][19] Group 3: AI's Role in Enhancing Legal Value - The goal of AI in the legal field is to release productivity by automating time-consuming tasks, such as contract review, which can reduce the average review time by 50% [25][27] - The strategic value of legal departments is becoming more apparent, especially in the context of globalization and compliance with complex international regulations [28][29] Group 4: Future Directions and Challenges - The future of legal AI applications will depend on the accumulation of high-quality proprietary data and the ability to adapt to dynamic compliance requirements across different jurisdictions [18][19] - The focus should be on human-centered approaches that enhance collaboration between AI and legal professionals, ensuring that technology serves to maximize human value [30]

Core Viewpoint - The article emphasizes the importance of integrating business logic with technology in the retail industry, particularly through the example of Belle Fashion Group's approach to AI implementation, which focuses on making business the "navigator" of technology rather than the other way around [5][8][20]. Group 1: Digital Transformation Challenges - The retail industry faces a paradox in intelligent transformation, where advanced models and algorithms often fail to align with business needs, leading to dissatisfaction among business departments [2][3]. - Since 2023, the digitalization of enterprises has been chasing the trend of large models, but there is a disconnect between business and cutting-edge technology, resulting in increased data governance costs and limitations of SaaS systems [4][9]. Group 2: Belle Fashion's AI Implementation - Belle Fashion has developed a methodology for AI implementation in collaboration with its long-term partner, Deepu Technology, focusing on transforming business rules into the "mother tongue" of AI [7][12]. - The company recognizes the illusion of large models in industrial scenarios and emphasizes the need for AI capabilities to be anchored in business rules and data quality [9][11]. Group 3: Data Governance and Management - Data governance is seen as the foundational issue for AI industrialization, requiring a shift from isolated technical perspectives to a strategic framework [13]. - Belle Fashion has moved from a label-based data processing approach to a dynamic context that allows AI to understand and reason with data, thus enhancing data governance [13][14]. Group 4: Intelligent Data Warehouse - The traditional data warehouse model is static, while Belle Fashion's intelligent data warehouse aims to create dynamic rules and insights from real-time business analysis [16][17]. - The shift from pre-defined static rules to a model that generates rules dynamically is crucial for enhancing business insights and decision-making [17][18]. Group 5: Agentic AI and Operational Efficiency - Agentic AI is highlighted as a key component in the final mile of AI industrialization, enabling real-time efficiency and seamless integration of business processes [21][22]. - By utilizing Agentic AI, Belle Fashion has transformed its management processes into traceable digital tracks, providing valuable data for model training [23]. Group 6: Future of AI in Retail - The article concludes that the best approach to intelligent transformation is to first reconstruct the understanding of business and human interactions before moving on to creation [26][27]. - The role of technology suppliers is evolving from traditional SaaS sales to a more collaborative approach that listens to business needs [25].