成本不该是企业AI落地关注焦点，价值才是丨ToB产业观察

Core Insights - The Chinese government has set a clear roadmap for AI development, aiming for deep integration of AI in six key sectors by 2027, with over 70% application rate of new intelligent terminals and agents [2] - The implementation of AI in enterprises is still in its early stages, with significant challenges remaining, particularly in terms of understanding and readiness among leadership [3][4] - There is a notable gap in understanding AI's potential, with many companies equating AI solely with generative AI, neglecting the broader spectrum of AI technologies [4][5] Industry Challenges - Despite 85% of manufacturing companies in the Asia-Pacific region claiming readiness for AI, only 11% are actually prepared for implementation [3] - Companies face cognitive challenges regarding AI, requiring leadership to have a clear understanding of AI's role in enhancing employee productivity rather than replacing jobs [3][4] - Misconceptions about AI investments and ROI calculations are prevalent, with companies often misallocating costs related to foundational digital infrastructure as AI investments [5] Differences in AI Adoption - Small and medium-sized enterprises (SMEs) have distinct challenges in AI adoption, focusing on cost, adaptability, and implementation speed [7][8] - SMEs often struggle with the high costs and complexity of AI solutions, leading to a need for careful consideration of ROI and the overall impact on business performance [7][8] - The initial phase of AI application in SMEs is characterized by a lack of mature products and services, making trial and error a costly endeavor [8][9] Key Elements for Successful AI Implementation - Successful AI deployment requires a focus on three critical elements: model capability, high-quality data, and application scenarios [10][11] - Data quality is essential for AI success, with companies needing to ensure that their core data is well-integrated and utilized effectively [12] - Identifying the right application scenarios is crucial, as companies should prioritize areas with high technical maturity and data readiness for initial AI implementation [13]