On September 11, the “2026 Financial Institutions Annual Meeting and Securities Industry Wealth Brokerage Forum” hosted by the Securities Times was held in Shenzhen. Xiao Wen, chairman of Yingmi Fund, said that AI will reshape the buyer's investment and wealth management industry, and that the transformation of institutional AI is not chasing the trend, but remains on the table. Organizational AI transformation usually goes through four stages, including personal efficiency improvement, process embedding, task acceptance, and model innovation. Currently, most organizations are still in the first two stages. The key is to let AI actually enter the business process, not just answer questions. According to Xiao Wen, the competitiveness of organizations in the AI era depends on AI talent density and AI leverage, divided by organizational friction, so organizational friction should be as small as possible. She proposed that data governance, knowledge structuring, closed-loop scenarios, and human-machine division of labor are underwater projects that cannot be circumvented by AI transformation. In addition to the general model, vertical industry awareness, professional expertise, unique business data, and workflows that are deeply tied to the business are real moats for financial institutions.

On September 11, the “2026 Financial Institutions Annual Meeting and Securities Industry Wealth Brokerage Forum” hosted by the Securities Times was held in Shenzhen. Xiao Wen, chairman of Yingmi Fund, said that AI will reshape the buyer's investment and wealth management industry, and that the transformation of institutional AI is not chasing the trend, but remains on the table. Organizational AI transformation usually goes through four stages, including personal efficiency improvement, process embedding, task acceptance, and model innovation. Currently, most organizations are still in the first two stages. The key is to let AI actually enter the business process, not just answer questions. According to Xiao Wen, the competitiveness of organizations in the AI era depends on AI talent density and AI leverage, divided by organizational friction, so organizational friction should be as small as possible. She proposed that data governance, knowledge structuring, closed-loop scenarios, and human-machine division of labor are underwater projects that cannot be circumvented by AI transformation. In addition to the general model, vertical industry awareness, professional expertise, unique business data, and workflows that are deeply tied to the business are real moats for financial institutions.