Frost & Sullivan'
Frost & Sullivan in collaboration with LeadLeo release2025Year in ChinaGenAlBest Application Practices in the Industry Research Report
Original Frost & Sullivan' Frost & Sullivan' Frost & Sullivan
Immersive reading in a novel reader
Follow+ Star to mark us
Led by Frost & Sullivan, a global growth consulting firmfrost &The 19th Frost & Sullivan Global Growth, Innovation and Leadership Summit and the 4th New Investment Conference (hereinafter referred to as the '2025 Frost & Sullivan New Investment Conference') hosted by Frost & Sullivan, abbreviated as 'Frost & Sullivan', were held in Shanghai from August 27th to 28th, 2025.
Li Qing, Executive Director of Frost & Sullivan Greater China
On August 28th, at the sub-forum on 'AI Evolution Theory - Building Intelligent Systems in the Physical World', Li Qing, Executive Director of Frost & Sullivan Greater China, released the '2025 Research Report on Best Application Practices in China's GenAl Industry' (hereinafter referred to as the 'White Paper').
This white paper adopts a diversified research methodology to construct an innovative multi-dimensional evaluation system for generative models across various industries.The application practice cases of AI have been objectively and fairly evaluated, and research has been conducted on the latest trends in generative AI for 2025. In-depth analysis has been carried out on high-quality overseas generative AI application practice cases. The research integrates survey data and thematic interviews with generative AI technology providers and enterprise users, and delves into the current application status and development trends of generative AI globally and in China across various industries from multiple perspectives. According to the market research and analysis by Frost & Sullivan, the core findings of the report focus on the practical applications of generative AI in infrastructure, manufacturing, finance, public services, and the internet, as well as its significant achievements in improving operational efficiency and creating non-financial value for enterprises.
01
In 2025, generative AI will see four major trends: technological optimization and cost reduction, agentic AI is creating new value paradigms. Synthetic data has become the core engine driving the continuous iteration and scenario-based implementation of large models. The AI security governance system is accelerating towards systematization and standardization.
In 2025, generative artificial intelligence is reshaping the technological landscape and industrial ecosystem with unprecedented depth and breadth. Its development presents four core trends, collectively constructing a critical path for AI from 'capability breakthroughs' to 'value realization'. Firstly, in terms of technical optimization and cost reduction, the industry has achieved a strategic transformation from 'pursuing parameter scale' to 'emphasizing efficiency and practicality'. With the continuous evolution of the Transformer architecture, the refinement of training algorithms, and the widespread application of sparse architectures such as MoE (Mixed Expert Systems), the training and inference efficiency of large models has significantly improved. It is estimated that under the same performance, the training cost of large models has decreased by more than 90% compared to 2024, making enterprise-level deployment and real-time applications the norm.
Secondly,agentic AI is creating entirely new value paradigms. 2025 is regarded as the year of intelligent agents, where AI is no longer merely a tool that responds passively to commands but has evolved into 'autonomous intelligent agents' capable of setting goals, breaking tasks down, invoking tools, executing autonomously, and providing environmental feedback. Multi-agent systems can complete complex business processes such as market research, code generation, supply chain optimization, to personalized marketing through collaboration, competition, and division of labor. It is expected that by the end of 2025, more than 40% of enterprises globally will deploy at least one core business process as an agent. AI systems are moving from 'enhancing human capabilities' to a new phase of 'autonomous creation of value', profoundly restructuring organizational structures and work modes.
Thirdly, synthetic data has become the core engine driving the continuous iteration and scenario-based implementation of large models. In the face of challenges such as increasingly scarce high-quality real data, stricter privacy compliance requirements, and insufficient data in specific domains, generativeAI has created high-fidelity, controllable, and annotatable synthetic data, which has become a key path for training and validating the next generation of models. By 2025, world models will begin to possess the capability to build a complete 'digital twin data ecosystem,' capable not only of simulating extreme scenarios and generating adversarial samples to enhance model robustness but also supporting model training for high-value applications such as autonomous driving and embodied intelligence without touching on sensitive original information. This has significantly shortened the model iteration cycle, reduced data acquisition costs, and become an important support for large models to achieve a commercial closed loop and competitive differentiation.
Finally,The AI security governance system is accelerating towards systematization and standardization. With the in-depth application of generative AI in key areas, potential risks such as hallucinations in generated content, model bias, data leakage, and compliance blind spots caused by malicious abuse have raised widespread concerns. Over the past few years, major global economies have established relatively complete regulatory frameworks: China has continuously promoted the implementation of the Interim Measures for the Administration of Generative Artificial Intelligence Services, emphasizing the legality of data sources, traceability of content, and security assessments; the EU's Artificial Intelligence Act has officially entered into force, implementing full lifecycle supervision for high-risk AI systems. AI security has shifted from passive response to active defense, with a deep integration of technology, systems, and ethics, laying a solid security foundation for the sustainable and responsible development of generative AI, ensuring a dynamic balance between innovation and risk.
02
Overseas generativeHigh-quality AI cases are analyzed and presented from three dimensions: efficiency improvement, technological innovation, and industry depth. They comprehensively reveal the practical achievements and leading experiences of generative AI applications in cost reduction, efficiency enhancement, driving business transformation, leading technological frontiers, and deep engagement in vertical fields. These cases provide benchmark paradigms for generative AI applications.
GenerativeAI mainly includes the following aspects in terms of efficiency improvement: reduced cost-effectiveness, such as significantly reducing labor and time investment through automated content generation, code writing, or document processing; increased production efficiency, reflected in faster task completion speed, shortened process cycles, and increased output per unit time; and optimized resource utilization, which refers to improving the efficiency of key resources such as manpower, computing power, and equipment through intelligent scheduling, predictive maintenance, or data-driven decision-making.
GenerativeTechnological innovation in AI practice refers to advancements in model architecture, training methods, multimodal fusion, or inference optimization; application model innovation is reflected in creating entirely new use cases, such as AI agents autonomously performing complex tasks or generating personalized services in real-time; business model innovation focuses on redefining product forms, service methods, or revenue structures through AI, such as transitioning from traditional subscription models to demand-based elastic service models.
GenerativeIn AI cases, the industry depth refers to the breadth and integration of AI applications across R&D, production, marketing, service, and other links; industry specialization is reflected in the model's ability to understand and accurately apply domain knowledge (such as medical terminology, financial rules, engineering specifications); industry influence includes the capacity to promote the formation of industry standards, improve overall digitalization levels, or lead peers to follow suit.
Data source: Frost & Sullivan analysis, LeadLeo research institute
03
The four core capability dimensions of functional value and applicability, technical performance and innovation, implementation and support, and customer experience and satisfaction feedback are comprehensively examined to generate insights.Value of AI industry application practice solutions
GenerativeThe industry application of AI requires a comprehensive multi-dimensional evaluation system to fully examine its value and potential. Frost & Sullivan's market research integrates traditional survey methods and has innovated a multi-dimensional evaluation system that encompasses functional value and applicability, technical performance and innovation, implementation and service support, as well as customer experience and satisfaction feedback. Each dimension is an indispensable part of a successful solution.
The applicability of features and value includes: Requirement Adaptability, which focuses on whether the product can accurately match users' business needs and scenario logic, evaluating performance in terms of goal achievement, revenue realization, cost consideration, and strategic alignment to ensure its adaptability and solution to market demands. Core Feature Integrity, which examines whether the product has complete core feature modules, including the synergy and maturity of core modules such as Natural Language Processing, Image Generation, and Voice Interaction, to meet the actual business closed-loop. Scenario Feature Generalization, which measures the adaptability and scalability of the product in different application scenarios to achieve seamless adaptation across business scenarios. Generalization ability determines the reuse value and long-term sustainability of technology and is a key indicator for measuring system scalability.
Technical performance and innovation dimensions include multimodal fusion capabilities: focusing on whether the product can efficiently process multi-modal data such as text, images, audio, and video, and achieve cross-modal content generation capabilities. This capability is the core support for realizing intelligent handling of complex scenarios. Stability and robustness of generated content focus on the consistency and reliability of output content under different input conditions, as well as fault tolerance in abnormal inputs (such as fuzzy instructions or noisy data), ensuring that generated results always meet expectations. At the same time, attention is also paid to solutions for improving generation quality. Compliance and security of generated content focus on legal, ethical, and data security risks during the content generation process, model compliance status, automated monitoring, and compliance assurance measures. It also includes fault tolerance capabilities and emergency response systems in abnormal scenarios.
The implementation and support dimension includes cost optimization through deployment, operation, and maintenance of generative services.The full lifecycle cost of AI systems, including hardware resource consumption, algorithm iteration costs, and labor input. Focus on cost reduction solutions such as lightweight models and cloud-native architectures to enhance technology penetration and cost-effectiveness. Agent applications: Pay attention to the actual effectiveness of agents in simulating human decision-making and automated processes, verify their response efficiency and accuracy in customer service, operations, and other scenarios, as well as their contribution to business process optimization. Focus on agent capabilities and the transformation of technical value. Training and support: Pay attention to user understanding and operational barriers, as well as the resources and service support provided by manufacturers after implementation for continuous empowerment, real-time technical support, and case libraries. Reduce the complexity of technology applications and accelerate their implementation process.
Data source: Frost & Sullivan analysis, LeadLeo research institute
04
GenerativeAnalysis of AI (GenAI) investment status quo: Sales and marketing account for the majority, but there is great potential for backend automation.
Over the past year, approximately50% of the generative AI budget is invested in sales and marketing, indicating that companies are keen on using generative AI technology to enhance customer experience and optimize sales processes. However, in reality, backend automation often yields higher returns on investment.
Firstly, in terms of internal process optimization, generativeAI can help enterprises achieve a high degree of automation in business processes from order processing to inventory management, reducing human errors while significantly increasing processing speed. Secondly, document management and data processing have become more intelligent and efficient due to the application of generative AI, such as automatic summarization and information extraction functions, which can greatly shorten the time for information retrieval and processing.
In addition, generativeThe potential of AI in dynamic resource allocation cannot be underestimated. By analyzing real-time and historical data, AI systems can predict future demands and optimize resource allocation accordingly to ensure maximum resource utilization efficiency. This forward-looking resource planning is crucial for reducing operating costs.
Data source: Frost & Sullivan analysis, LeadLeo research institute
05
Classical case analysis
Zhongguancun KEGIN and Ningxia Jiaojian jointly launched the country's first large-scale transportation infrastructure model"Lingzhu Intelligent Engineering" focuses on the vertical field of transportation infrastructure, precisely matching key business scenarios such as construction, accounting, and bidding, significantly improving target achievement efficiency and corporate strategic synergy. Based on the Dehu Large Model Platform, through training with tens of thousands of industry specifications and engineering technical documents, the model's professionalism is enhanced. An intelligent agent platform covering knowledge Q&A, document writing, report generation, data analysis, intelligent bidding, etc., is constructed to meet the urgent needs of the transportation infrastructure industry for professional intelligent understanding, efficient document processing, and intelligent decision support.
Data source: Frost & Sullivan analysis, LeadLeo research institute
Recommended Reading
Copyright Notice
Thank you for your attention and support to Frost & Sullivan. If you wish to reprint articles from our WeChat official account, please:
· Leave a message in the original text, indicating the name, introduction, and WeChat official account of the reprinting institutionID;
· Fill in the author on the editing page"Frost & Sullivan";
· All reposts must not alter, misinterpret, interpret or adapt the original text in any way. Frost & Sullivan reserves the copyright and the right to interpret all original articles. In case of violation, Frost & Sullivan reserves the right to pursue corresponding legal responsibilities in accordance with the law.
About Frost & Sullivan
Global growth consulting firm, Frost & Sullivanfrost &Frost & Sullivan, abbreviated as 'Frost & Sullivan', integrates 64 years of global consulting experience and has dedicated 27 years to serving the rapidly developing Chinese market. With a global perspective, it helps over 10,000 clients accelerate their business growth, assist them in achieving growth, innovation, and leading benchmarks within their industries, and realize capital operation goals such as financing and listing.
Frost & Sullivan has deep roots in the global capital markets and corporate consulting services, and its innovative propositions"Global investment management(Total Investment Management,TIM) provides comprehensive investment and financing services as well as various other professional consulting services for enterprises, including investment and financing CDD, valuation services, technology consulting, financial consulting, ESG, fundraising and investment feasibility studies, bond issuance industry consulting, industry consulting, assessment services, award services, industry white papers, strategic and management consulting, planning consulting, technical insights, etc.
The investment and financing business of Frost & Sullivan Greater China has achieved full industry coverage of China's national economy, including a high level of attention to all economic hotspots such as the new economy and new infrastructure. It covers digital infrastructure, consumer electronics, dual-carbon new energy, healthcare and life sciences, catering and new retail, semiconductors and integrated circuits, smart homes, automotive and mobility, health care services, food and beverages, information and communication technology, fintech, real estate and property management, mining and smelting, beauty and fashion, big data and artificial intelligence, logistics and supply chain, construction technology and decoration, special new materials, culture and entertainment, enterprise-level services, cross-border e-commerce trade, infrastructure construction, environmental protection and energy-saving technology, education and training, etc.
Since the Frost & Sullivan team began providing investment and financing advisory services to corporate leaders and their management teams, it has helped nearly 3,000 companies successfully list on the Hong Kong and overseas stock exchanges. It is a leading enterprise in the field of investment and financing strategy consulting in China. RecentlyFor the past 10 years, Frost & Sullivan has continuously ranked at the forefront of the market share for professional industry advisory services for Chinese companies listing in Hong Kong and overseas. In recent years, Frost & Sullivan reports have also been widely cited in the prospectuses of leading A-share and STAR Market companies, as well as primary and secondary market research reports and other capital market disclosure documents.
For over 64 years, Frost & Sullivan has helped a large number of clients (including Fortune Global 1,000 companies, top domestic and international financial institutions, and other leading enterprises) achieve strategic goals through its nearly 50 offices around the world. Leveraging its powerful database and expert library, rich professional knowledge, and consulting tools, Frost & Sullivan has completed work including but not limited to due diligence, valuation analysis, and third-party evaluations. It has established a series of market position research tools and methodologies, innovatively proposed the "FSBVFrost & Sullivan Brand Value Model," and has provided market position research and brand value assessment services to over 1,000 enterprises, continuously helping many Chinese brands achieve growth strategies both domestically and internationally.
