开放创新·生态繁荣｜森博科技入选首批“人工智能产业生态合作伙伴计划”

Summary 1 Conference and selection

1. On November 4, the 2026 China Listed Company AI Development Conference and the Launch Ceremony for the First Batch of AI Industry Ecosystem Partners were held at Tsinghua University in Beijing. The theme was open innovation and ecosystem prosperity, and the event was hosted by the Global Securities Market Research Institute of Tsinghua University.

2. Senbo Technology was invited to attend as one of the first batch of selected companies and the only company in the AI marketing sector.

Summary 2 Practical points for building an AI-native organization

1. To build an AI-native organization, companies need to establish a business-validated R&D system (BER), closely linking R&D with real business and using business practice to test R&D direction and application value.

2. Business-raised questions should enter the R&D process, and methods and outcomes developed through R&D should return to the business for validation, maintaining continuous interaction between business and R&D so that AI applications better fit companies' actual needs.

3. Companies should identify and develop MEE methodology-establishing engineers, who drive exchange between business and R&D, turn practical experience into testable and improvable methods, and ensure those methods continue to play a role across the organization.

Summary 3 Future cooperation directions

1. Through the AI Industry Ecosystem Partner Program, exchange views with various parties on enterprise AI applications, organizational transformation, and talent development.

2. Going forward, continue to ground work in AI marketing business practice and deepen exploration of the BER R&D system and MEE talent development.

Summary 1 Integration of AI marketing and brand business

1. Senbo Technology is the only AI marketing company among the first batch of invited companies, and the article emphasizes that AI marketing should start from specific business problems.

2. AI marketing should focus on whether technology applications can improve actual work and whether relevant methods can be validated in business practice.

Summary 2 Implications of the R&D system for product and marketing innovation

1. To build an AI-native organization, companies need to establish a business-validated R&D system (BER), closely linking R&D with real business.

2. Business questions enter the R&D process, and R&D methods and outcomes return to the business for validation, bringing AI applications closer to companies' actual needs.

3. For brand companies, both product R&D and the implementation of marketing technology should be tested against business validation.

Summary 3 Talent and ecosystem cooperation

1. Identify and develop MEE methodology-establishing engineers to drive exchange between business and R&D and turn practical experience into testable and improvable methods.

2. Through the AI Industry Ecosystem Partner Program, exchange with industry, academia, and investors on enterprise AI applications, organizational transformation, and talent development.

3. In the future, brands can follow how BER and MEE exploration can more closely connect technology R&D, business validation, and organizational growth.

Summary 1 Cooperation opportunities and positive signals

1. The conference launched the first batch of the AI Industry Ecosystem Partner Program. Senbo Technology became one of the first batch of invited companies and the only one in the AI marketing sector.

2. The program provides a platform for exchange and cooperation among companies and research forces in different fields, with discussions centered on enterprise AI applications, organizational transformation, and talent development.

Summary 2 Latest business models and practices to learn from

1. Build an AI-native organization and establish a business-validated R&D system (BER), allowing business questions to enter R&D and R&D outcomes to return to the business for validation.

2. Identify and develop MEE methodology-establishing engineers, who drive exchange between business and R&D and turn practical experience into testable and improvable methods.

3. AI marketing should start from specific business problems and focus on whether technology applications can improve actual work and whether methods can be validated in business.

Summary 3 Opportunities and risk-response notes

1. Opportunities lie in ecosystem cooperation and exchange against the backdrop of AI marketing and enterprise AI transformation.

2. In terms of risk response, the article stresses that R&D and application cannot be separated from real business and must continuously undergo validation through business practice.

3. The future direction is to connect technology R&D, business validation, and organizational growth more closely.

Summary 1 Implications for product R&D and design requirements

1. Enterprise AI transformation requires building a business-validated R&D system (BER), closely linking R&D with real business and using business practice to test R&D direction and application value.

2. Business-raised questions should enter the R&D process, and R&D methods and outcomes should return to the business for validation, so AI applications stay close to actual needs.

Summary 2 Production organization and digitalization

1. Build an AI-native organization and connect technology R&D, business validation, and organizational growth more closely.

2. Identify and develop MEE methodology-establishing engineers, drive exchange between business and R&D, turn practical experience into testable and improvable methods, and ensure those methods continue to work.

Summary 3 Business opportunities

1. As the only AI marketing company among the first batch of invited companies, Senbo Technology participates in the AI Industry Ecosystem Partner Program.

2. Factories can watch for exchange and cooperation opportunities under this platform related to enterprise AI applications, organizational transformation, and talent development.

3. The article emphasizes advancing AI applications from real business needs, which offers a reference for factories pursuing digitalization.

Summary 1 Industry trends and customer pain points

1. The article presents the trend of integrating AI with enterprise business and organizational transformation. The conference theme was open innovation and ecosystem prosperity, and it launched the first batch of the AI Industry Ecosystem Partner Program.

2. Customer pain points include how AI can be integrated with actual business after entering the enterprise, how to continuously drive organizational transformation, and who should drive exchange between business and R&D.

Summary 2 Solutions and methods

1. Build a business-validated R&D system (BER), let business questions enter the R&D process, and return R&D methods and outcomes to the business for validation, maintaining continuous interaction between business and R&D.

2. Identify and develop MEE methodology-establishing engineers, who ensure the BER system runs efficiently and turn practical experience into testable and improvable methods.

3. AI marketing solutions should start from specific business problems and focus on whether technology applications can improve actual work and whether methods can be validated in business practice.

Summary 3 Cooperation ecosystem

1. Through the ecosystem partner program, exchange with industry, academia, and investors on enterprise AI applications, organizational transformation, and talent development.

2. In the future, deepen BER and MEE exploration to connect technology R&D, business validation, and organizational growth more closely.

Summary 1 Ecosystem platform needs and practices

1. The conference was hosted by the Global Securities Market Research Institute of Tsinghua University, bringing together representatives from industry, academia, and investment to explore new paths for AI and enterprise development.

2. The conference launched the first batch of the AI Industry Ecosystem Partner Program, providing a platform for exchange and cooperation among companies and research forces in different fields.

Summary 2 Platform recruitment and operations management

1. The first batch of invited companies includes Senbo Technology, the only AI marketing company among them, reflecting the platform's open cooperation with companies across multiple fields.

2. Platform exchange operations can center on enterprise AI applications, organizational transformation, and talent development.

3. The platform can follow companies' approaches to building AI-native organizations, including the BER R&D system and MEE talent development.

Summary 3 Trend-risk avoidance

1. The article emphasizes close links between R&D and real business and using business practice to test R&D direction and application value, so that AI applications do not drift from actual needs.

2. Business questions entering R&D and R&D outcomes returning to business validation can serve as a reference for platforms promoting enterprise AI applications.

3. Platforms can use ecosystem cooperation to help connect technology R&D, business validation, and organizational growth more closely.

Summary 1 New industry developments and questions

1. The 2026 China Listed Company AI Development Conference and the Launch Ceremony for the First Batch of AI Industry Ecosystem Partners were held under the theme of open innovation and ecosystem prosperity. Senbo Technology became one of the first batch of invited companies and the only one in the AI marketing sector.

2. New questions focus on how AI can be integrated with actual business, how to continuously drive organizational transformation, and who should drive the operation of the R&D system.

Summary 2 Business models and methodology

1. Senbo Technology proposes building an AI-native organization and establishing a business-validated R&D system (BER), using business practice to test R&D direction and application value.

2. Business questions enter the R&D process, and R&D methods and outcomes return to the business for validation, creating continuous interaction between business and R&D.

3. It proposes identifying and developing MEE methodology-establishing engineers, turning practical experience into testable and improvable methods, and ensuring those methods continue to play a role across the organization.

Summary 3 Implications

1. Ecosystem cooperation platforms can promote exchange among industry, academia, and investors on enterprise AI applications, organizational transformation, and talent development.

2. Enterprise AI transformation requires closer links among technology R&D, business validation, and organizational growth, offering a reference for practice.

3. BER and MEE respectively address how to establish an R&D system and what kind of people should drive its operation, making them important observation points for building AI-native organizations.