Core Insights - The emergence of ChatGPT has showcased the power of large models, but companies often find that these models lack precision and industry-specific expertise when applied to their unique business needs [1][2] - Data accumulation is identified as a critical factor for the effective application of AI in enterprises, as without a solid data foundation, AI remains superficial and cannot serve as a reliable productivity tool [1][3] Group 1: Challenges in AI Application - Companies face a "last mile" dilemma when applying general large models, as the suggestions provided are often too theoretical and not directly applicable to specific operational contexts [2] - The lack of suitable processes and quality data resources hinders the promotion of AI technologies in businesses, revealing that advanced technology cannot compensate for inadequate data [2][3] Group 2: Importance of Data Accumulation - Data accumulation is likened to "dark matter" in physics, being unseen yet crucial for the success of AI applications in enterprises [3] - Minglue Technology has built a significant data asset over nearly 20 years, which includes vast amounts of marketing data that are deeply integrated with business scenarios [3][4] Group 3: Overcoming Data Silos - The challenge of transforming data into actionable intelligence is compounded by the existence of data silos within organizations [5] - Minglue has developed a systematic technical framework to integrate and manage disparate data sources, creating a unified data asset that supports AI applications [6] Group 4: Trustworthy AI Foundations - The quality and depth of data are essential for ensuring the reliability of AI outputs, as general models often lack the necessary depth in specific fields [7] - Minglue's data, derived from real business scenarios and validated over years, enhances the accuracy and reliability of AI models [7] Group 5: Data Barriers as Competitive Advantage - In the AI era, possessing high-quality, industry-specific data is becoming a core competitive advantage, with Minglue's data barrier being a key strength [8] - The long accumulation period, high data quality, broad scenario coverage, and continuous updates contribute to Minglue's competitive edge in the market [8][9] Group 6: The Value of Data in AI Industrialization - The importance of data is often overshadowed by technological advancements, yet Minglue emphasizes that data accumulation is essential for sustainable AI applications [10] - Without a robust data foundation, AI applications are likened to castles built on sand, lacking the resilience needed to deliver lasting business value [10]