Core Viewpoints - The vertical industry platforms are the main force for large-scale application of AI technology [2][3] - The platform-based procurement and sales process is the most popular business scenario for AI applications [2] - 51% of AI technology applications are concentrated in the top 10 business scenarios [2] Industry Insights - The report identifies the top three hot scenarios: industry knowledge inquiry, demand forecasting and trend insight, and product search, screening, and recommendation, which excel in readiness, value, usability, and universality [2][23] - The top 10 potential scenarios include industry knowledge inquiry, marketing, product design, price generation, production collaboration, warehouse logistics status monitoring and anomaly alerting, warehouse logistics resource matching and scheduling, production finance, customer service, and multi-business flow collaboration, showing high readiness and value [2][35] - Vertical industry platforms have a natural advantage in promoting large-scale AI applications due to their data availability and market connections [14][15] Data and Application Landscape - As of now, 196 vertical industry platforms have released 503 AI applications covering 13 major business scenario categories and over 60 subcategories [3][11] - The AI applications are primarily driven by self-owned data (60%), user data (24%), and third-party data (16%) [8][10] - The average industry penetration rate of vertical industry platforms is estimated at 2%-3%, serving over 7 million industry enterprises [15][16] Challenges in AI Implementation - The difficulty of AI technology landing in industrial fields is significant, with only about 10% of applications in industries like electronics, automotive, steel, and energy [6][8] - High-quality data acquisition is challenging due to limited channels and high processing costs, alongside slow digitalization processes in small and medium enterprises [8][10] Future Directions - The industry is witnessing a shift towards "Agent as a Service" as a new trend driven by AI [2][29] - The report emphasizes the need for further research on AI application maturity and development guidelines for vertical industry platforms [35]

Group 1 - The rapid development of artificial intelligence (AI) technology is profoundly changing people's lives and work, with applications expanding from simple automation to complex decision-making support [1] - "Model as a Service" (MaaS) is evolving into "Agent as a Service" (AaaS), marking a significant paradigm shift in AI service architecture [1] - 2025 is anticipated to be the "Year of AI Agents," transitioning from concept to reality and from single-function to multi-integrated applications [1] Group 2 - AI Agents are defined as intelligent entities or software systems that autonomously make decisions and execute tasks based on environmental perception and learning from experience [2] - The core features of AI Agents include goal-driven behavior, environmental awareness, autonomy, and adaptability [2] Group 3 - AI Agents can be classified based on their technical implementation paths, including rule-based agents, machine learning-based agents, and large language model (LLM)-based agents [3][4] - LLM-based agents are currently the mainstream direction in AI agent development, leveraging natural language understanding and generation capabilities [4] Group 4 - AI Agents can be categorized by their product functionalities, such as information retrieval and analysis, task automation, personal assistance, decision support, content creation, and entertainment interaction [6][7] Group 5 - AI Agents are widely applied across various sectors, including customer service, financial services, education, healthcare, retail, content creation, software development, and smart manufacturing [8][9][10] Group 6 - The AI Agent industry structure consists of a multi-layered ecosystem, including infrastructure, core algorithms, agent components, and end-user applications [10][11][12][13][14] Group 7 - The global development of AI Agents has evolved through several phases, from theoretical exploration to practical applications, with a current focus on large model-driven advancements [15][20] Group 8 - Chinese AI Agent companies are increasingly targeting overseas markets for growth opportunities, leveraging product innovation and understanding of specific scenarios [21] - HeyGen, a company specializing in AI video generation, has shifted its focus to the overseas market, achieving significant revenue growth after relocating its headquarters to the U.S. [22][23][24] - Laiye Tech, a provider of AI and robotic process automation solutions, has also expanded its presence in international markets, recognizing the advantages of higher profit margins and mature business environments [26][28][29] - Waveform AI is exploring overseas markets for its long-text generation models, focusing on user willingness to pay for content creation tools [30][31][32] Group 9 - The development of AI Agents faces challenges related to computing power, including high training costs, insufficient supply of high-end AI chips, and energy consumption concerns [33] - Solutions being explored include algorithm optimization, dedicated AI hardware, edge computing, and the development of green computing solutions [34]