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  The service industry imports AI data to build core competitiveness first   Data first refers to the use of data as a decision-making guide, after collection, definition and analysis, to achieve the predictive function. Online and offline data can be combined to carry out precise marketing, improve customer experience and reduce decision-making errors. However, data is not enough for the service industry, and field personnel must be able to grasp the insights provided by the data as an important basis for interacting with customers. Therefore, in addition to IT, data, and marketing teams, on-site personnel must also have data thinking and a basic understanding of artificial intelligence. How to do it? StarHub Aesthetics Group has long used internal book clubs to enhance colleagues' awareness and build consensus. For example, recently they read the book "Artificial Intelligence in Taiwan" together, watched the video about AI explained by Chen Shengwei, the late chairman of the Artificial Intelligence Technology Foundation, and asked each executive to share their experience. Lin Xinyi, founder of StarHub Aesthetics Group, said that from the reading experience of his colleagues, it can be seen that data-driven and AI applications are by no means limited to IT systems. In the service industry, the customer journey (Customer Journey) and customer experience are even more important. Therefore, in order to create the best customer experience with customers as the core and data as the guide, each department must jointly invest, use imagination and creativity, and then be able to bring out the value of AI applications. As a supervisor mentioned in his experience, it is necessary to clearly define the goals first, and after further framing the business, process, data and requirements, find out the problems suitable for AI to solve. "For the medical aesthetics industry, the integration of online and offline customer data can create a customer experience map. This is an obvious key point." In addition, internal communication is also very important, but a key task that is easily overlooked by enterprises. For example, many people think that artificial intelligence will replace manpower. When enterprises introduce AI, it is a harbinger that jobs may not be guaranteed. In addition, before the full introduction of artificial intelligence, there will inevitably be major adjustments in organization and processes, which will easily cause colleagues to resent or even resist privately. Therefore, a supervisor believes that colleagues must first have a correct idea, "Artificial intelligence is not born to replace any colleagues, but requires us to think from experience-oriented thinking, and use objective data, facts and scientific methods to assist Making decisions, achieving human-machine collaboration, improving efficiency and increasing the probability of correct decision-making will allow us to effectively own resources and successfully achieve the goal of digital transformation.” So, when many things are done by AI, where is the value of employees? It can be viewed in two parts. First, the service industry is originally a people-oriented industry, and there is no substitute for listening and caring when people come into contact with each other. It will be of unique value if every contact with customers can have a quality that exceeds the original expectation. Secondly, artificial intelligence will not be 100% correct. When there are "exceptions" or "abnormalities", it must rely on human experience and judgment to resolve them. At the same time, when the exception is handled by humans, the cause and solution of the problem can also be fed back to the system to optimize and improve system performance. Although collectively referred to as the service industry, there are actually great differences due to the different products and services sold. In addition, Taiwan’s consumer market is small and corporate resources are relatively limited, so it is difficult to introduce data systems and AI. Lin Xinyi gave an example, medical cosmetology provides "low-frequency, long-chain" services, while retail businesses such as supermarkets provide "high-frequency, short-chain" services. The consumption frequency of medical cosmetology is low, but the consumption process takes a long time and has many details. The retail industry's data needs vary widely. Therefore, he suggested that the service industry should first understand the nature of its own industry, formulate a correct operating model, and then gradually collect and analyze data, so as to grasp the correct direction and continue to create value through the application of data and AI technology.  

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