Core Insights - The article emphasizes the challenges industries face in integrating AI technology into core production processes, highlighting the need for practical solutions to unlock commercial value from AI investments [1][4] - Huawei proposes a systematic approach to address these challenges, focusing on five key discoveries and a three-step "ACT" implementation path to facilitate industry-wide AI adoption [3][6] Group 1: Key Discoveries - The importance of selecting the right scenarios for AI integration, as its value lies in deep integration with core production processes to reshape workflows and deliver intelligent products and services [5] - The quality of vertical data is crucial for building competitive industry models, necessitating the training and tuning of general models with high-quality, industry-specific data [5] - The rapid scaling of AI agents is driving a strong demand for large-scale reasoning capabilities [5] - Human-machine collaboration is emerging as a new organizational paradigm [5] - Systematic governance and risk management are essential to ensure the safe, sustainable, and trustworthy application of AI [5] Group 2: ACT Implementation Path - The first step, assessing high-value scenarios, involves a framework that evaluates commercial value, scenario maturity, and the integration of business and technology, helping identify over 1,000 core AI production scenarios [6][7] - The second step focuses on calibrating AI models using vertical industry data, transforming raw data into actionable knowledge and models through Huawei's AI development and data governance platforms [7] - The final step is the large-scale deployment of AI agents to reshape key business operations, facilitated by Huawei's versatile platform that automates the generation of intelligent agents and business processes [7]

Core Insights - The article discusses how Huawei is addressing the challenge of implementing AI in business to create real commercial value, emphasizing the need for a structured approach to AI integration in various industries [2][5][12]. Group 1: Huawei's Approach to AI Implementation - Huawei has developed a "three-step" path for enterprises to transform their operations through AI, which includes assessing high-value scenarios, calibrating models with vertical industry data, and scaling AI agents to reshape key business processes [8][9]. - The company has released nine industry-specific solutions covering sectors such as government, education, healthcare, finance, and manufacturing, establishing a replicable standard for industry transformation [4][5]. Group 2: Key Findings on Intelligent Transformation - Five critical findings were shared regarding intelligent transformation: the importance of scenario selection, the necessity of high-quality domain data for model capability, the rapid scaling of AI agents, the emergence of human-machine collaboration as a new organizational paradigm, and the need for systematic governance and risk management [5][6][12]. - The emphasis on scenario selection highlights that technology must serve the essence of business rather than merely adding superficial enhancements [6][12]. Group 3: AI as a Growth Engine - AI is positioned as a key engine for business growth, integral to the entire value chain from research and development to production and service [12][16]. - Huawei's AI capabilities have been validated through extensive applications in its own operations, providing valuable industry know-how and a comprehensive end-to-end solution from foundational computing to industry applications [13][15]. Group 4: Role of Partnerships - Huawei's extensive partner ecosystem, comprising over 6,300 Kunpeng partners and 2,700 Ascend partners, plays a crucial role in translating generic AI technologies into customized solutions that address specific industry needs [15][16]. - The collaboration with partners is essential for bridging the gap between technology and business value, ensuring that AI solutions are effectively tailored to meet the unique challenges of different sectors [15].

Core Viewpoint - Huawei Cloud Stack aims to provide a hybrid cloud solution that better understands the needs of government and enterprise users in the era of intelligence, focusing on AI integration and data utilization [1][3]. Group 1: Huawei Cloud Stack Features - Huawei Cloud Stack will become the first hybrid cloud to adapt to CloudMatrix 384 super nodes, enabling enterprise customers to have their own cloud super nodes locally, enhancing AI computing power for intelligent transitions [3]. - Currently, Huawei Cloud Stack offers over 120 cloud services and more than 50 scenario-based solutions, maintaining the leading market share in the hybrid cloud sector across government, finance, and manufacturing for several consecutive years [3][4]. Group 2: User Segmentation and Solutions - Huawei Cloud Stack recognizes that government and enterprise users are not a monolithic group but can be categorized into four distinct roles: data center engineers, data engineers, AI algorithm model application engineers, and application development engineers [3][4]. - The platform supports users throughout the entire cloud lifecycle, from building to managing cloud resources, enabling efficient resource allocation, data governance, model training, and application development [4]. Group 3: Case Studies - In finance, Huawei Cloud Stack helped a state-owned bank establish a unified computing power platform, allowing data center engineers to deploy 106 DeepSeek R1 instances in just two days, improving efficiency by 70% compared to traditional bare-metal deployments [4][5]. - In manufacturing, Huawei Cloud collaborated with XCMG to create a robust big data platform, enhancing data analysis efficiency and enabling value extraction from operational data of construction machinery [4][5]. - In the steel industry, Xianggang utilized Huawei Cloud Stack to develop a one-stop AI development platform, achieving quality improvement and cost reduction through the deployment of a steel model across over 30 scenarios [5]. - In the energy sector, CNOOC implemented CodeArts to develop a digital platform, reducing development time by 30% and streamlining the deployment of intelligent oilfield management systems from one week to one day [5].