Ernst&Young China Chen Jianguang: The big model platform will help enterprises optimize the entire chain operation process

Tech 2023-11-09 09:45:46 Source: Network

Global Network Technology Reporter Zheng XiangqiThe annual CIIE not only provides an important bridge for communication and cooperation among numerous industries at home and abroad, but also creates a wind vane for the cutting-edge development trend of a thousand industries. During this CIIE, Ernst&Young (China) Enterprise Consulting Co

Global Network Technology Reporter Zheng XiangqiThe annual CIIE not only provides an important bridge for communication and cooperation among numerous industries at home and abroad, but also creates a wind vane for the cutting-edge development trend of a thousand industries. During this CIIE, Ernst&Young (China) Enterprise Consulting Co., Ltd. (hereinafter referred to as "Ernst&Young China") released a series of solutions focusing on topics such as artificial intelligence, sustainable development, and healthcare, in order to explore new paths for high-quality development with all sectors.

In the field of artificial intelligence, Ernst&Young China has launched a one-stop domain model platform EYMETIS based on AI technology. This product, as an end-to-end solution for AI planning, implementation, and management applicable to various business forms and scenarios, can help enterprises fully utilize AI for technological innovation, thereby accelerating the process of digital transformation.

Chen Jianguang, Partner of Data Intelligence Consulting Services at Ernst&Young (China) Enterprise Consulting Co., Ltd., stated in an interview that, Ernst&Young China involves a large amount of professional knowledge in providing various specialized services such as taxation, consulting, and auditing. We have found that existing knowledge management systems only support keyword retrieval, making it difficult to provide queries for complex problems. At the same time, due to the fact that these content and knowledge retrieval often involve processes (such as bidding processes) Existing systems are difficult to integrate with process systems. With the rapid development of AI technology, we have found that utilizing new technologies can effectively solve the aforementioned difficulties

In the process of building applications, Ernst&Young China has gradually formed the METIS "1+4" platform to more comprehensively and effectively support internal application of AI technology. This measure not only effectively solves the challenge of internal implementation of large models, but also helps customers better utilize AI technology and reduce the cost of adopting large models and building enterprise AI applications. Moreover, on this platform, high-speed, convenient and customizable AI applications can be provided to external enterprises, effectively empowering their digital transformation.

According to Chen Jianguang, for the capability architecture of METIS "1+4", "1" refers to an integrated AI base that can achieve multi model compatibility and also integrate knowledge from multiple vertical fields such as finance and taxation, enterprise management, and compliance. '4' refers to four major categories of AI capability tools, including daily office assistants, knowledge base retrieval, decision support, and solution generation.

Currently, based on these four types of capability tools, METIS has integrated nine application scenarios, including "System Assistant EYassistant", "Security Consulting Platform", and "Intelligent Risk Management", to optimize the enterprise's operational processes throughout the entire chain. At the same time, the platform will also provide API services to facilitate better integration of enterprise business systems and proprietary applications with large models.

Relevant institutions predict that the market size of China's large model industry will reach 117.9 billion yuan by 2028, with an average growth rate higher than the world average. When it comes to the current situation of domestic enterprises applying big models, Chen Jianguang frankly stated that more and more domestic enterprises are enthusiastic about applying big models. However, many enterprises do not have a complete understanding of AI, and the decision-making level of enterprises may also face confusion such as how AI matches the company's business and operational maintenance, and how big models integrate with existing digital systems or AI platforms.

Regarding this, Chen Jianguang suggests that the decision-making level of enterprises can form a strategic and coordinated construction approach for AI, including studying the latest technological trends, application examples, and authoritative statistical data related to AI, summarizing the inspiration of technological trends for enterprise development, and providing guiding suggestions for AI capability planning and development paths.

In addition, enterprises can also have a comprehensive understanding of the core needs and expectations of the decision-making, technical, and business departments of the enterprise, and consider factors such as business value, scope of influence, and implementation complexity in each scenario. They can comprehensively evaluate different strategies and development paths for building enterprise AI capabilities in the short, medium, and long term.

In terms of implementing enterprise AI projects, Chen Jianguang stated that based on the needs of the project, enterprises can generally choose to use pre trained large models or self train enterprise specific large language models based on enterprise and external public data. This decision depends on the specific needs of the project, including resource availability, as well as comprehensive considerations of cost, safety, and performance.

Enterprise business experts and AI experts need to integrate traditional business and large model technology to find the best solution that fits the business. Chen Jianguang also told reporters that for traditional enterprises, application comes first. Enterprises can adopt POC and rapid iteration models for small-scale pilot applications before quickly promoting them, helping employees better understand the practical effects of AI technology, thereby better addressing pain points and achieving effective application of enterprise AI.

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