Core Insights - The value of enterprise-level AI agents depends on having the right model, good data, and appropriate scenarios [2][3] - The complexity of real business processes can significantly reduce the success rate of AI agents, highlighting the gap between demo performance and actual operational effectiveness [2][3] Data and Model Integration - A significant portion of enterprise data, approximately 10%, is structured and contains core business logic, which is crucial for AI understanding [5][4] - The focus should not be on the largest models but rather on the most suitable ones for specific scenarios, emphasizing the importance of good data [3][5] Unique Advantages of the Alibaba Ecosystem - The background of Lingyang as part of Alibaba's data middle platform provides a unique perspective on the value of data in enterprise AI [6] - The integration of Alibaba's ecosystem allows Lingyang to leverage scenario-based data capabilities for its AI solutions [6][8] Product Design and Functionality - Lingyang's AgentOne allows enterprises to manage AI agents similarly to managing employees, offering options to recruit existing agents or train custom ones [9][10] - The product architecture consists of model services, training capabilities, and rapid agent deployment, tailored to specific business needs [10] Targeted Scenarios for AI Implementation - Lingyang prioritizes addressing the most pressing pain points for enterprises, focusing on marketing, analysis, and operational scenarios [12][14][16] - The marketing agent encompasses various functions, including creative inspiration and media placement, to optimize advertising budgets [14] Conclusion - Lingyang aims to create a comprehensive closed-loop system for enterprise data, transitioning from serving human needs to serving AI requirements, thereby enhancing the understanding and application of AI in business contexts [5][18]