Core Insights - The WISE 2025 conference emphasizes the transition of AI from theoretical models to practical applications, focusing on the challenges of productization in the AI industry [2][6] - The essence of enterprise-level AI is not just a generic model but a precise replication of specific job knowledge and data permissions [3][10] - Dipo Technology, as a representative in the Data+AI sector, aims to deepen the integration of AI technology with business scenarios to enhance industrial value [2][12] Group 1: AI Productization Challenges - The main challenge in AI productization is the ability to handle non-standardized data, cross-knowledge modeling, and ensuring 100% accurate data integration [3][10] - Dipo Technology has implemented AI solutions across various industries, including manufacturing, retail, transportation, and healthcare, demonstrating the technology's integration into business processes [3][12] - The transition from a sample to a product requires systematic refinement and collaboration across technology development, scenario adaptation, data governance, and knowledge accumulation [6][7] Group 2: Specific Applications and Solutions - Dipo Technology's AI solutions include DataDense for decision-making roles and specialized models for professionals in fields like construction and mechanical processing [12][13] - The company is also developing AI applications for frontline operational roles, aiming to optimize workflows and lower operational barriers [13] - The successful implementation of AI in enterprises hinges on three key factors: complex data governance, effective modeling, and accurate data assembly [13]