Core Viewpoint - AI technology is reshaping the asset management industry by enhancing research capabilities and operational efficiency, transitioning from a human-driven model to a dual-driven model of human intelligence and machine intelligence [1][6]. Group 1: AI Integration in Asset Management - The asset management industry is data and knowledge-intensive, and AI's characteristics align well with its needs, enabling significant improvements in research efficiency and investment capabilities [2][4]. - Taikang Asset has developed its own deep research agent (TKDR) that integrates extensive internal and external research data, enhancing the investment research process [2][4]. Group 2: Advantages of TKDR - TKDR demonstrates advantages over traditional research methods by quickly identifying core demands, utilizing resources efficiently, and producing structured research outputs [3][5]. - The system is designed to support active investment research, aligning with the methodologies of active researchers and enhancing the quality and timeliness of outputs [5]. Group 3: Future Outlook and Strategic Development - The construction of an intelligent agent system is seen as a key driver for AI development, with plans to create a collaborative human-machine research paradigm [6][8]. - The transition to AI-driven asset management is expected to enhance decision-making across various functions, including investment strategy, risk management, and operational efficiency [6][7]. Group 4: Recommendations for AI Implementation - To effectively advance AI initiatives, asset management firms should establish supportive mechanisms, optimize governance structures, and develop talent teams that align with AI transformation [7][8]. - Emphasizing collaboration between business and technology teams is crucial for successful AI integration, ensuring that AI applications are closely aligned with business objectives [8].