With the rapid development of technology and changes in the global economic environment, Hong Kong enterprises are facing increasing pressure and challenges in digital transformation. In order to improve operational efficiency, reduce costs, and improve customer service, more and more enterprises are looking for new technologies and solutions. Artificial Intelligence (AI), as a cutting-edge technology, is rapidly rising and showing strong application potential in various fields. This article will explore the role of AI in Hong Kong enterprises’ digital transformation and provide relevant recommendations.
Due to the continued popularity of ChatGPT, generative AI, as artificial intelligence that can create new content and ideas, has made breakthroughs in the fields of creating conversations, stories, images, videos, and music. The entire world has focused on generative AI. In terms of AI, different market participants have launched various large-model AI platforms. In daily enterprise applications, AI that can help us is divided into many different categories:
Symbolic AI: Symbolic AI is the earliest and most traditional AI technology that uses manually written rules and logic to perform tasks. For example, expert systems or knowledge graphs are AI applications based on this technology.
Machine Learning: Machine learning uses a large amount of data and algorithms to train itself to complete tasks, such as image recognition or natural language processing. The currently widely used natural language processing, OCR image recognition, etc. are all based on machine learning technology. Machine learning can be subdivided into subcategories such as traditional machine learning, deep learning and reinforcement learning. Before generative AI was released, deep learning has always been the most popular field of AI development, Google’s Tensorflow, Facebook Caffe, Apple’s PyToych, etc. were once popular.
Generative Model (Generative AI): This kind of AI can generate new data or content based on input or random information, such as text, images, music or code, etc. Generative models can be subdivided into subcategories such as Variational Auto-Encoder (VAE), Generative Adversarial Networks (GAN), and Autoregressive Model (AR). In the field of generative AI, OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, etc. are all leaders in this field.
As a highly urbanized area, Hong Kong has experienced tremendous changes in its population structure in recent years, with the working-age population decreasing year by year. According to the Census and Statistics Department of the Hong Kong Government, as of mid-2023, the total population of Hong Kong is approximately 7.49 million, and the working-age population aged 20-49 has dropped from 3.1 million in 2020 to 2.98 million in mid-2023. Generally speaking, Hong Kong enterprises are facing the problem of high labor costs caused by talent shortage, and the trend is becoming more and more obvious. These challenges require enterprises to manage them more intelligently and efficiently. Digital technology (Switching Circuit Theory), especially AI, as a technology with automation and intelligence characteristics, can give enterprises better capabilities to respond to demographic trends and cost pressures. Hong Kong enterprises have also gradually realized that in today’s general situation of technological development, in order to maintain competitive advantages, they must make breakthroughs in high-tech industries represented by technologies such as the Internet, cloud, and AI, and rely on the development of high value-added industries such as technology. Only by creating uniqueness can achieve better development. All walks of life are discussing the impact of digital technology development on industry development and the possible help:
As one of the global financial centers, Hong Kong’s financial industry has a particularly urgent need for digital transformation. AI can be applied to financial risk management, smart investment advisory, transaction automation, etc. Financial institutions can use AI to build predict risk models to help enterprises reduce risks and improve business security. In addition, AI can also assist investors in data analysis and trading decisions through big data analysis and machine learning, improving investment accuracy and profitability.
With the rise of e-commerce and changes in consumer demands, Hong Kong’s retail industry needs to undergo digital transformation to adapt to market competition and personalized needs. AI can help the retail industry achieve personalized marketing and smart supply chain management. By analyzing big data and consumer behavior, AI can provide retailers with personalized product recommendations and promotions to improve consumers’ purchasing intentions, experience and satisfaction. In addition, AI can improve inventory management and order fulfillment efficiency by predicting and optimizing supply chain processes.
Logistics and Transportation Industry
With the development of e-commerce and the acceleration of timely requirements for global transportation, Hong Kong’s logistics and transportation industry has an increasing demand for digital transformation. AI can help logistics companies improve the efficiency and safety of cargo transportation. Through the Internet of Things (IoT) and Sensing Techniques, AI can realize intelligent monitoring and dispatching of logistics equipment, improving cargo tracking and transportation efficiency. In addition, AI can also help logistics companies optimize cargo handling and distribution plans by analyzing and predicting cargo demand and transportation routes, reducing transportation costs and improving customer satisfaction.
AI Improves the Operational Efficiency of Enterprises
In traditional enterprise management, a large amount of human and material investment and lengthy processes make enterprise operations inefficient. However, with the help of AI technology, Hong Kong enterprises can realize automation and intelligence in operations and decision-making, improving operational efficiency and decision-making accuracy. AI can provide enterprises with market demand forecasts and product recommendations by analyzing a large amount of data, allowing enterprises to better adjust production and sales strategies and improve market competitiveness. In addition, AI can also realize the optimization and refinement of enterprise workflow through automated Robotic Process Automation (RPA) technology, improving work efficiency and quality. For example, AI can help corporate customer service centers answer most common questions and handle daily repetitive tasks, thereby greatly reducing the workload of customer service personnel. This allows customer service staff to focus on handling more complex and personalized issues, providing a higher level of service while also improving customer satisfaction.
AI Provides Personalized Customer Service
Modern consumers are increasingly pursuing personalized and customized services. However, traditional customer service models often suffer from problems such as information asymmetry and unstable service quality. Through AI technology, enterprises can accurately analyze customer needs and preferences, provide personalized services and recommendations, and improve customer satisfaction and loyalty. AI can use machine learning and deep learning technology to understand customers’ preferences, needs and behavior patterns from a large amount of data, predict customers’ purchase intentions and provide corresponding recommendations and services. For example, AI can use speech recognition and natural language processing technology to promptly identify and understand customer problems during customer consultations, and provide relevant solutions and suggestions. In addition, AI can also provide customers with 24-hour consultation and explanation through virtual assistants, improving the convenience and efficiency of customer service.
AI Realizes Intelligent Risk Management and Security Assurance
In the digital age, enterprises face increasing risks of information security and data leakage. Traditional security assurance methods often fail to allow enterprises to detect and respond to security incidents and network threats in a timely manner. AI technology can help enterprises instantly monitor and analyze various security events and threats, and respond quickly. AI can use machine learning and data collection technology to identify and analyze network attack behaviors and abnormal traffic, provide early warning of risks that enterprises may face, and provide corresponding protective measures and solutions in a timely manner. In addition, AI can also use behavioral analysis and pattern recognition technology to promptly discover and identify security vulnerabilities and malicious behaviors within the enterprise, preventing the leakage of the enterprise’s core assets and information. AI can implement smart firewalls and anti-intrusion detection systems in enterprise data centers and networks to promptly discover and block potential security threats.
AI Promotes Enterprise Innovation and Development
As market competition intensifies and technological advancement continues, enterprises need to continuously innovate and improve products and services to maintain competitive advantages. AI technology can help enterprises better understand market trends and consumer needs, and explore new business opportunities and innovation possibilities. Through big data analysis and machine learning technology, AI can mine and analyze huge market data and consumer behavior, and provide accurate market predictions and product recommendations based on market demand. AI can use data collection and machine learning technology to discover new needs and consumption habits in a specific market segment, and provide corresponding products and services to achieve rapid market occupation. In addition, AI can also accelerate the launch of new products and technologies by automating R&D and production processes, improving the efficiency and success rate of innovation. For example, AI can accelerate the design and verification process of new products through automated robots and intelligent equipment in the enterprise’s R&D center, improving development speed and product quality.
Hong Kong enterprises have a deep understanding of the importance of digital transformation and AI technology, and have gradually understood its application scenarios and specific roles. However, there are still some challenges in implementing relevant technologies in Hong Kong’s corporate environment and operations. AI technology is developing rapidly, but its limitations are still very obvious. How to achieve a balance between costs and benefits currently mainly encounters the following problems:
Enterprises have Higher Requirements for Professionalism and Accuracy of AI Output Content and Low Error Tolerance Rate
The advantages of AI within enterprises are mainly reflected in the analysis and processing of massive amounts of data. By aggregating relevant and effective answers, the efficiency is greatly improved. AI is still difficult to truly understand complex logical relationships, especially those involving a large amount of industry background knowledge and characteristics. These products still have complex business logic. Only with a deep understanding , can answer and solve problems truly effectively. For example, for Hong Kong’s catering industry, the biggest difficulty faced by the industry is human resources issues. When using AI to order food, AI must accurately and quickly identify various requirements of guests and accurately place orders, which requires very high accuracy.
AI is not Cost-Effective in the Short Term
Enterprises know that applying new technologies and new tools requires cost investment, and they need to calculate the return on investment (ROI). Although the general large model of generative AI uses the public cloud to provide services, even if the input price of a single token drops rapidly, if it is to be truly applied to a specific industry or enterprise, it is necessary to fine-tune the model of industry characteristics and enterprise characteristics, as well as industry characteristics. the cost and price of continuous training of corporate knowledge are not low. For example, the cost of training a single large model of GPT-3 can easily reach hundreds or tens of millions of dollars. For ordinary small and medium-sized enterprises in Hong Kong, the cost-benefit does not match.
Problems with Industry and Enterprise Data Barriers
There are some well-known enterprises in every industry. In the historical operation process of the enterprise, a large amount of industry and competitive advantage data have been accumulated. If these data are handed over to public large models for training, how to maintain the core data and uniqueness of the enterprise? If private deployment of large models is implemented, the cost is very expensive. How to maintain a balance between these two aspects is a real problem faced by enterprises.
Personal Information and Corporate Data Security Issues
Hong Kong has a privacy protection law, and AI applications must strictly abide by the law. How to ensure that real data is used in enterprise applications to train models in industry verticals to achieve the required results while ensuring the security of personal privacy information and data? This is a question worth exploring.
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