Core Insights - 79% of surveyed enterprises believe that generative AI will have a disruptive impact on their business within the next 18 months, which is 12 percentage points higher than the Asia-Pacific average [2] - 37% of enterprises have deployed generative AI in production environments, while 61% are in the testing and proof-of-concept stages, indicating a shift from the "PPT stage" to the "practical stage" in AI implementation [2] - The key focus for 2023-2024 is the "large model parameter competition," with enterprises pursuing "hundred billion-level parameters" and "multimodal capabilities" [2] - By 2025, the emphasis will shift to "scenario implementation," where businesses seek to solve real-world problems with AI [2] Infrastructure Strategy - Enterprises in the Asia-Pacific region recognize that centralized cloud architectures cannot meet the growing demands for scale, speed, and compliance, necessitating a rethink of infrastructure strategies to include edge services [2] - The reliance on public cloud for production applications has exposed shortcomings, particularly in the context of generative AI [4] - 37% of enterprises that have deployed generative AI report that over 60% experience unexpected delays in real-time interactive applications, with conversion rates dropping by 40% due to latency issues [4] Edge Computing Emergence - The traditional reliance on public cloud is insufficient for all enterprises to embrace AI, leading to a need for a modernized digital foundation that integrates edge computing [5] - Edge computing is becoming a core technology for building the next generation of digital infrastructure, enabling distributed deployment to reduce latency and improve responsiveness [5][6] - By 2024, the global market for edge cloud is expected to reach 185.1 billion yuan, with China accounting for approximately 70% [6] Investment Directions - Future investments in edge IT will focus on supporting digital operations, ensuring business continuity when disconnected from core or cloud resources, and reducing connectivity costs [7] - The integration of generative AI and edge computing is bridging the gap between centralized cloud resources and distributed edge environments, ensuring scalability and performance [10] Six Pillars of AI-Ready Infrastructure - The report outlines six core pillars for building AI-ready infrastructure, emphasizing a holistic approach that extends from core to edge [11] - Pillar one focuses on making infrastructure adaptable to AI, enhancing efficiency and user experience through hardware optimization and personalized application support [12] - Pillar two highlights the shift from large model competition to edge adaptation, requiring hardware investments in edge-level GPUs and heterogeneous computing chips [14] - Pillar three emphasizes modernizing edge IT to extract value at the data source, reducing data transmission volumes significantly [15] - Pillar four addresses the need for a unified scheduling of distributed resources to avoid "edge island" scenarios [16] - Pillar five advocates for extending existing public cloud investments to edge deployments, emphasizing interoperability and data consistency [17] - Pillar six focuses on autonomous operations driven by AI, enhancing monitoring, resource allocation, and fault recovery capabilities [18]

Group 1 - The 2025 ITValue Summit focused on the theme "The Truth of AI Scene Implementation," addressing ten core issues in AI application for enterprises, including strategy, reliability, data challenges, scenario selection, model selection, industry implementation, knowledge base construction, security compliance, human-machine collaboration, and talent bottlenecks [1] - During the summit, five closed-door meetings were held covering various topics and industries, allowing participants to discuss specific industry challenges in depth [1] Group 2 - Many small and medium-sized manufacturing enterprises face challenges in digital transformation, with 90% of their data remaining "asleep" due to a lack of unified data and business process standards [2][3] - The digitalization of supply chains is evolving from merely moving procurement online to achieving end-to-end collaboration and optimization through data integration [2] Group 3 - Companies like Shenzhen Genesis Machinery are integrating AI large model technology to break down data silos and enhance data sharing and value release [3] - The lack of standardization in business and data processes is a fundamental issue, particularly in non-standard manufacturing, where unique project characteristics complicate data integration [3] Group 4 - AI and data technologies are increasingly being applied to enhance supply chain transparency, responsiveness, and risk management [5] - Companies are utilizing AI to analyze historical sales and inventory data to predict risks, such as chip price increases, allowing proactive inventory management [6] Group 5 - The manufacturing sector's AI application differs significantly from the internet industry, focusing on "small data" and "scenario closure" rather than large models [6][7] - The core of successful digital transformation in manufacturing lies in standardization, followed by system implementation, data collection, and AI modeling [4] Group 6 - The financial sector is exploring AI infrastructure to address industry pain points, with companies like JD Cloud leveraging their diverse data advantages to enhance AI model training and application [10] - The successful application of AI in enterprises hinges on data quality, identifying suitable business scenarios, and establishing a supportive organizational structure [11][12] Group 7 - The retail industry is undergoing significant changes, with CIOs emphasizing the need to adapt to evolving consumer behaviors and market trends [19][20] - Successful retail operations require a focus on creating value for consumers and leveraging technology to enhance customer engagement [21] Group 8 - The hospitality and airline industries are integrating AI into their operations, with companies like East China Airlines deploying AI applications to improve efficiency and customer service [22][24] - The transition to AI-driven solutions in these sectors involves overcoming initial high costs and ensuring leadership commitment to AI initiatives [23][24] Group 9 - The CIOxCFO closed-door meetings highlighted the importance of collaboration between IT and finance leaders in driving AI implementation [25][26] - Key factors for successful AI application in enterprises include high-quality data accumulation, focusing on high-value business scenarios, and continuous operational improvement [27][30]

Core Insights - The visit of Eden Software's executives to NVIDIA's Beijing EBC signifies a strategic collaboration focused on AI technology and its application in various industries [3][5][6] - Eden Software aims to enhance its AI capabilities and product offerings through partnerships with NVIDIA and Lichan Technology, leveraging their technological resources and market influence [5][6] Group 1: Company Overview - Eden Software has over 20 years of experience in the industry and is a leading provider of AI solutions and cloud services in China, having served over 10,000 clients [3] - The company's self-developed products, such as Yi AI and eCopilot, have successfully been implemented in top brands across retail, finance, and technology sectors [3] Group 2: Strategic Collaboration - Eden Software and Lichan Technology have agreed to collaborate on developing AI solutions tailored for vertical industries, including the launch of eCopilot software on Lichan Cloud [5] - Lichan Cloud CGC is a one-stop AI microservice resource platform designed for Chinese developers, which will enhance the integration of technology, resources, and market capabilities [5] Group 3: Technological Integration - The partnership will enable Eden Software to customize AI solutions that align with specific business logic, data architecture, and industry characteristics, transitioning from "general AI" to "exclusive AI" [6] - Eden Software plans to embed AI capabilities seamlessly into core business processes, aiming to unlock AI value and drive growth efficiency [6]