Header | Filmed on April 18 at the Big Whale AI Summit
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For generative AI, while 100% of people believe that it will be an explosive application, 99% of people choose to stay on the sidelines.
Behind the wait-and-see represents that there are still a lot of unknowns and uncertainties about AI. To this end, the 2024 Big Whale AI Summit - Retail Consumption Special Session launched by Tiger Sniff Think Tank in Beijing is to clarify these uncertainties and challenges, and present the cases and experiences of industry pioneers.
On the afternoon of April 18th, Tiger Sniff Think Tank invited Shi Jianping, investment partner of Bluerun Venture Capital, Luo Yujin, general manager of Big Data Center of Nature Hall Group, Zhang Peng, CDO of Shijiufang, Wang Xiaodong, president of Baiguoyuan Four Modernizations Research Institute, Peng Cheng, founder of Yuzu Investment, Tian Ruifeng, director of the Industry Innovation and Development Department of China Chain Store & Franchise Association, Le Yongbin, CIO of TATA Wooden Door, Xu Bo, general manager of SF Technology Solution Department, Zhang Xinyu, Director of EY Parthenon, and other sharing guests, gave us an in-depth analysis of the application potential of AI in the consumer retail industry, the experience of achieving efficiency improvement through supply and demand matching, and the promotion challenges of AI supply chain logistics scenarios. At the same time, the guests had high-quality exchanges and answered some questions that were discussed behind closed doors.
I hope that this excerpt of the views of the conference can give some inspiration to those who are not present, and readers and friends are welcome to have in-depth exchanges with us.
The following is a summary of the views shared by the panelists:
(Shi Jianping, investment partner of Lanchi Venture Capital)
Generative AI will bring unprecedented productivity gains to the retail industry
I'm sure AI is new to all of you, especially in the retail industry. However, the application of the previous generation of AI is relatively limited, mainly in a few fields such as facial recognition. In contrast, this generation of AI has surpassed humans in several foundational capabilities. At present, many industry leaders are actively embracing AI. It is predicted that the market size of generative AI is expected to reach $1.3 trillion in the next 10 years.
The application of generative AI in enterprise scenarios is essentially based on the exploration and implementation of data intelligence infrastructure. Typical use cases include personalized recommendations, intelligent assistants, customer service, visual "try-on" experiences, product design and development, and trend forecasting. However, GenAI technology is still in its early stages of development, and it will take time to realize its full commercial value.
(Luo Yujin, General Manager of Big Data Center of Nature Hall Group)
An inventory: the business foundation for the growth of the consumer business
An inventory is available in all industries, and it is no stranger. The purpose of this business model is to make offline as agile as online.
A big premise of doing business offline is that if the goods are out, there will be business to do, and if they can't be stored, there will be no business. After the agent ordered, we did not give the goods to the agent after the goods were produced, but put them in our warehouse distribution system to build various sub-warehouses across the country to help the agent to fulfill the contract. In this way, we can know what kind of goods are laid in so many stores across the country? What is the time rhythm of the store that should be stored? Including each store, have we implemented the policy for him? In this way, we can help us turn the offline business from the original sales to an operation. There are great changes to agents and stores, as well as changes in the business system from sales to operations.
The digital transformation of JALAN (the group to which Naturedo belongs) is based on an inventory system and a data middle platform as the brain, connecting consumers, customers, sales delivery, production delivery, products, back-end services and other nodes to quickly and efficiently support various new business formats at the front-end.
(Peng Cheng, founder of Yuzu Investment)
There are four major stuck points in the application of AI in the supply chain, and the biggest problem is that it is like a human
First, the foundation of AI must first be completed in the front-end, middle-office, and back-office supply chain of the business. Many companies don't do it at the last end, and they don't have enough data to see it at the supply chain end, so there is no need to mention the problem of AI. Second, the boss is conscious. It is to transition from relying on people to manage things to relying on data management, of course, not completely relying on data management, everyone in the industry knows that a chain of convenience stores is completely dependent on data management, and the situation is very bad, so we are transitioning to such a process of data plus people to manage. Third, AI must have a dataset and be trained by an expert database, and the biggest question is where is the data? How to govern it? Fourth, evaluate the level of AI. That is, when looking at things from the perspective of the CTO, when you want to train AI, you have to constantly make corrections, it may be the same as people, it will sacrifice long-term interests for short-term goals, and it needs to adjust different weights, that is, what node can be assessed to maximize its production? Otherwise, it will have a counterproductive effect on the business effect.
In the roundtable session, Xu Bo, General Manager of SF Technology Solution Department, proposed that retail enterprises should not use technology for the sake of technology, but try to integrate technology and business when they land. In the short term, it will help the main business reduce costs and increase efficiency, and in the long term, it will help enterprises build competitiveness, otherwise there is no value to talk about.
But in fact, for the implementation of AI, both technology vendors and enterprise applications are still trying small steps, and there are still many pitfalls to be stepped on. In order to improve AI cognition as much as possible, Tiger Sniff Think Tank officially launched the "AI+ Landing Case Collection Project" plan on the spot, announcing that it will release 50 innovative application cases, and will select 10 articles to be analyzed and commented by analysts and experts to summarize successful experiences.
This content is the author's independent view and does not represent the position of Tiger Sniff. May not be reproduced without permission, please contact [email protected] for authorization
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