Frost & Sullivan in collaboration with LeadLeo release 'Best Application Practices for China's Generative AI Industry in 2024'

Since 2012 when deep learning algorithms made significant breakthroughs in speech and image recognition, generative AI has entered a period of rapid development. It is gradually penetrating into all walks of life and becoming a new engine driving social progress. According to information from the Cyberspace Administration of China, more than 190 generative AI large models have been filed and launched in China, with a cumulative registered user base exceeding 600 million, indicating the rapid development of China in this field. These generative AI large models use technologies such as deep learning and natural language processing to provide diverse services including product design, code generation, smart marketing, etc., for enterprises and users in various industries, greatly enriching enterprise creation and business processing methods and significantly improving the efficiency of industry business processes. Coupled with the macro environment of accelerating enterprise digitization, enterprises in various industries are beginning to build and deploy generative AI in different ways to create more strategic benefits.

Based on this development background, Frost & Sullivan, in collaboration with LeadLeo, has integrated leading cases of generative AI use across various industries, aiming to provide enterprises and markets with an opportunity to showcase technical capabilities, inspire innovative thinking, and popularize generative AI empowerment methods.

Source: Frost & Sullivan analysis, LeadLeo Research Institute

The industry application of generative AI needs to be comprehensively examined through a comprehensive full-dimensional evaluation system to fully assess its value and potential. Frost & Sullivan's market research integrates traditional survey methods and innovates a full-dimensional evaluation system, which covers 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 the successful solution.

The functional value and applicability dimension emphasizes the adaptability of enterprise customer needs to the final solution, the specificity of data supply, and the generalization ability of scenario functions, etc., ensuring a precise alignment between the solution and industry needs. The technical performance and innovation dimension focuses on the controllability of the content generated by the solution, reasoning efficiency, and compliance and security services of the solution, with technical performance driving the innovation and efficient execution of the solution. The implementation and service support dimension evaluates the deployment and time cost in the early stage of solution execution, as well as the training and support provided by manufacturers to enterprise customers, ensuring the economy and sustainability of the solution and guaranteeing the smooth deployment and continuous optimization of the solution. Finally, the customer experience and satisfaction feedback dimension starts from the user perspective, measures the scenario value, experience customization, and performance innovation of the solution, reflects the user's acceptance and satisfaction with generative AI applications, and measures the actual benefits of the solution from the perspective of end-users. The four capability evaluation dimensions not only provide a comprehensive decision support framework for industry decision-makers but also provide a solid evaluation basis for the in-depth application and innovation of generative AI technology.

Source: Frost & Sullivan analysis, LeadLeo Research Institute

At present, generative AI is gradually penetrating into industries such as healthcare, finance, and consumer retail, thanks to the strong demand of these industries for improving work efficiency and innovative solutions, as well as their advantages in data accumulation and processing. The summary analysis of best application practice solutions shows that the value benefits of generative AI in these fields are mainly concentrated in improving work efficiency, significantly increasing productivity and response speed through automated processes and intelligent decision support.

With continuous technological progress, the application of generative AI in various industries will become more extensive and in-depth. It is expected that in the future, in addition to efficiency improvement, generative AI will show greater value benefits in personalized services, predictive analysis, strategic planning, etc. These capabilities will further drive enterprise innovation, optimize customer experience, and bring long-term competitive advantages to enterprises.

Source: Frost & Sullivan analysis, LeadLeo Research Institute

The potential of generative AI in improving efficiency and innovative thinking has been widely recognized by various industries and has become an important means for various industries to bridge the efficiency gap. It is a key technology for industries such as healthcare, transportation, and finance to improve efficiency. In the future, with continuous technological maturity, generative AI is expected to play an increasingly important role in personalized services and strategic planning, further driving the transformation of enterprise operation models and industry ecosystems.

With the continuous maturity of generative AI technology, research has observed that the development trend of the industry tends towards intelligence and automation. Most industry decision-makers recognize the long-term value brought by generative AI. The application of generative AI, especially in industries such as healthcare, transportation, and finance, not only improves work efficiency but also promotes the innovation and optimization of business processes, bringing revolutionary influence depth and strength.

At present, although the application of generative AI in various industries is still in its growth stage, its potential has been verified in improving efficiency and innovative thinking. In the future, generative AI is expected to play a more significant role in personalized services, predictive analysis, strategic planning, etc. With further technological development and improvement in cost-effectiveness ratios, the penetration rate of generative AI is expected to increase significantly, becoming an indispensable part of enterprises.

Ultimately, the development of generative AI will not be limited to breakthroughs at the technical level but will profoundly affect the operation models of enterprises and the industry ecosystem. With the deepening understanding of the value of generative AI by enterprises, more cross-industry cooperation and innovative models will emerge in the future, jointly promoting society towards a more efficient and intelligent direction.