Core Insights - The article emphasizes the transformation of research processes in finance through AI, specifically highlighting the capabilities of Alice 27 in automating deep research tasks, which traditionally required significant human effort [5][20]. Group 1: Research Process Transformation - Traditional research consumes over 60% of time on data collection, validation, and logical structuring, which is labor-intensive and time-consuming [4][11]. - Alice 27 shifts the paradigm from a question-answer model to a project execution model, allowing AI to autonomously manage the research process [6][8]. - The AI system operates like a research team, with multiple agents working in parallel on different aspects of the research, such as macro analysis, industry competition, and financial modeling [10][12]. Group 2: Efficiency and Output - The new agentic model enables the completion of complex research tasks in approximately 15-20 minutes, significantly reducing the time from days or weeks to a short coffee break [11][12]. - The output is a structured, comprehensive report of 10-50 pages that is ready for meetings and discussions, enhancing productivity and focus on core business judgments [11][20]. - The system's ability to dynamically collaborate and fill information gaps leads to a more robust and validated research output [10][20]. Group 3: Practical Applications - The AI can assist in generating detailed macroeconomic outlooks and asset allocation strategies, which typically require extensive team efforts over weeks [13][14]. - It can also analyze specific sectors, such as the cost breakdown of core components in the robotics industry, streamlining the research process for analysts and investors [16][17]. - The technology allows for a comprehensive view of complex topics, enabling professionals to focus on strategic decision-making rather than data gathering [20].