Core Viewpoint - The article emphasizes the need to promote the construction of industry-level intelligent infrastructure and accelerate the deep integration of large models with core business systems in enterprises [1] Group 1: Infrastructure and Integration - It is suggested that AI should be embedded into enterprise processes, with companies like Ronglian Cloud already achieving large-scale applications in customer service quality inspection, marketing outreach, and lead conversion [1] - The establishment of a "scenario-based data asset pool" is recommended to enhance industry data governance and circulation mechanisms [1] Group 2: Data Standardization and Sharing - Companies are encouraged to develop standardized and controllable sharing protocols for the vast amounts of call, text, and behavioral data they accumulate, facilitating the transformation of industry know-how into intelligent capabilities [1] Group 3: AI Demonstration Projects - The establishment of AI demonstration projects in key industries is proposed to create replicable "intelligent model showrooms" [1] - Specific applications are highlighted, such as promoting data analysis, conversational insights, and risk control quality inspection in the financial sector, and achieving omnichannel customer contact and intelligent decision-making linkage in the retail service industry [1]