Core Insights - Huawei emphasizes the importance of industry intelligence transformation and presents the "ACT three-step" implementation path to promote this transformation [1][4]. Group 1: Key Discoveries for Industry Intelligence - AI technology is developing at an unprecedented speed, and companies must address three key questions regarding AI investments, proprietary data utilization, and scaling AI applications [2][3]. - The five key discoveries for advancing industry intelligence include: 1. The critical importance of scenario selection for AI integration into core production processes [3]. 2. The quality of domain-specific data determines the capability of industry models, necessitating the training and tuning of general models with high-quality data [3]. 3. The rapid scaling of intelligent agents is driving a strong demand for large-scale reasoning [3]. 4. Human-machine collaboration is becoming a new organizational paradigm [3]. 5. Systematic governance and risk management are essential to ensure the safe, sustainable, and trustworthy application of AI [3]. Group 2: ACT Three-Step Implementation Path - The "ACT three-step" path consists of assessing high-value scenarios, calibrating models with vertical industry data, and scaling AI intelligent agents to reshape key business processes [4]. - Huawei has developed an "AI scenario selection evaluation framework" to help identify and implement over 1,000 core production scenarios based on commercial value, scenario maturity, and business-technology integration [4]. - To achieve the ACT path, companies need AI-oriented ICT infrastructure covering the entire process from data preparation to model training and inference, with Huawei providing integrated products in data storage, computing, and networking [4]. Group 3: Industry Solutions - Huawei, in collaboration with partners, launched nine major industry intelligence solutions, including urban intelligence hubs, digital healthcare, AI in banking, and smart manufacturing [5].

Group 1 - The 2025 World Artificial Intelligence Conference (WAIC) showcased a shift from homogeneous competition among large model vendors to differentiated strategies, with companies focusing on long text processing, multimodal capabilities, and vertical scene development [2] - The boundaries between models and applications are becoming increasingly blurred, with leading vendors transitioning from pure model providers to comprehensive platforms that integrate generation, retrieval, and tool invocation capabilities [2] - The industry is exploring a hybrid model of open-source and closed-source, with some companies like OpenAI and Zhipu releasing open-source models, while others like Meta are developing advanced closed-source products [2] Group 2 - Internet cloud vendors are building model-centric full-stack capabilities, offering "Model as a Service" (MaaS) platforms that may change the logic of enterprises moving to the cloud, especially for small and medium-sized enterprises facing challenges with private AI cloud setups [3] - The progress of domestic computing power is highlighted by Huawei's Ascend 384 super node cluster, which boasts double the computing power of NVIDIA's GB200 NVL72 system, although domestic GPUs still lag in key inference performance metrics [4] - The demand for private deployment is reflected in the popularity of AI integrated machines, with domestic GPU manufacturers seeking breakthroughs through collaborative innovation [4] Group 3 - Despite high interest in smart robots and AR glasses, edge AI is still in a preparatory stage, facing challenges in multimodal perception, interaction, and autonomous decision-making capabilities [5] - The smartphone is seen as a potential primary carrier for AI agents due to its advantages in computing power, interaction, and application scenarios, with a cautious approach from manufacturers indicating the need for further technological maturity [5] - Continuous investment in the industry chain is laying the groundwork for future developments in edge AI, suggesting a positive outlook despite the current limitations [5]