The true use of AI in traditional industries: from cost savings to operational improvement

1、 What does AI mean for traditional industries?
In the past two years, various concepts surrounding AI have emerged one after another: big models, intelligent assistants, automated decision-making, unmanned factories
Many bosses and teams in traditional industries are worried about falling behind while also being afraid of stepping into pitfalls.
We often hear three typical voices when communicating with enterprises:

AI is a trend, if we don't go up, we will fall behind, but we don't know how to go up. ”AI may seem powerful, but it's too far away from us. We are in the factory/trade/service industry and don't need it. ”I have bought some systems before, but I couldn't use them in the end. Now I am very cautious about anything new
The basic judgment of Health Wisdom Hong Kong Company is:
For traditional enterprises, AI is not used to "overturn everything", but to do "tool based upgrades" - first helping people do things better, and then attempting automation in local scenarios.

2、 The four most suitable AI application scenarios for traditional enterprises
In practical projects, we do not recommend companies to immediately embark on a 'full stack AI transformation', but rather focus on the most common and effective scenarios.
（1） Customer service and after-sales Q&A: Reduce repetitive labor and improve response speed
For many manufacturing, trading, and service enterprises:
The customer service team spends a lot of time answering repetitive questions; The after-sales team often needs to search through historical records in order to understand the process of the problem; Language and time difference are also difficult issues in international customer communication.
In this area, what AI can do is:
By using a 'Q&A robot', handle a large number of standardized questions (such as logistics progress, basic parameters, after-sales policies, etc.); Transforming historical dialogue content into a knowledge base, allowing robots to gradually learn how to answer common questions; When encountering complex problems, automatically transfer them to manual personnel and present relevant background information to customer service at the same time to reduce repetitive communication.
In this way, frontline customer service and after-sales personnel can spend more time on complex scenarios that truly require manual judgment and communication.
（2） Demand forecasting and inventory management: avoiding "overstocking" or "insufficient inventory to sell"
Many traditional enterprises have had this experience:
Senior executives often complain that there is too much inventory in the warehouse, all for money; Frontline sales often give feedback that 'this popular product is always out of stock'; Finance is sandwiched between controlling inventory and meeting business requirements.
What can be done by utilizing AI's ability in data analysis is:
Collect historical sales data, seasonal patterns, promotional activities, channel differences, and other information; Establish a simple and usable predictive model to estimate the future demand for key products; Based on the predicted results, provide a "suggested stocking interval" for procurement and production reference.
We do not advocate relying solely on models to make decisions, but rather:
Make AI a 'decision reference tool' and give management a 'closer starting point to the facts'.
In the long run, companies can see:
Increase in inventory turnover rate; The situation of frequent stockouts or large backlogs has decreased; The financial explanation logic for inventory is clearer.
（3） Quality inspection and process control: making quality inspection more stable and replicable
In some industries, such as parts processing, electronic products, packaging printing, etc., quality inspection work is often repetitive and tedious, while also being very important.
AI image recognition, pattern recognition and other technologies can play a role in the following areas:
Assist manual detection of obvious defects (such as scratches, gaps, color differences, etc.); Structurally record the test results to provide a data foundation for subsequent quality analysis; By analyzing massive detection data, help enterprises identify certain systemic issues (such as problems with specific processes, equipment, or material batches).
What we emphasize is that AI plays an "assistant role" in quality inspection, namely:
Let AI do the work that is highly repetitive, easily fatigued by the human eye, but with relatively clear rules, while humans are still responsible for final judgment and handling special situations.
（4） Document organization and communication in both Chinese and English: improving overall team efficiency
For many traditional enterprises that need to connect with overseas customers, document work, although not a "core business", is very time-consuming:
Email correspondence requires writing and translation in both Chinese and English; The contract terms need to be repeatedly compared; The report materials and plan need to be revised multiple times.
The value of AI lies in:
Assist in quickly generating initial drafts of emails and documents (including both Chinese and English versions); Based on the key points, outline and structure the report to reduce the psychological burden of starting from scratch; Assist in preliminary translation and word optimization, and then have the team make final confirmation.
The goal in this area is not to 'make AI finish writing all the content', but to:
Let AI take on the 'repetitive and formatted' part, with humans responsible for key content and details.
3、 Three principles for promoting the implementation of AI: small step pilot, human-machine collaboration, and compliance priority
In order to avoid "good concepts turning into bad projects", we will adhere to three principles in practice.
（1） Small step pilot: solve a specific problem first
First, choose the most perceivable scenario, such as automated customer service Q&A;
Set 2-3 simple and clear goals, such as:
How much reduction in manual processing volume? How much has the customer response time been reduced? Has the complaint rate improved?
Validate the effectiveness within 2-3 months, rather than seeking large-scale deployment across the entire company from the beginning.
The benefits of doing so are:
The investment is controllable and won't cost a lot of money right away; The team can truly feel the value of AI through small changes around them; After seeing the actual results, the management is more confident in pushing for the next step.
（2） Human machine collaboration: making AI a 'deputy' rather than a 'stunt double'
We do not encourage companies to simply equate AI with "substitutes" in any project.
In contrast, a more reasonable approach is to:
Hand over the parts with high repeatability and clear rules to AI;
Reserve the parts related to judgment, communication, negotiation, and comprehensive understanding to business personnel; Through process design, ensure that critical nodes are still reviewed or reconfirmed by individuals.
For example, in customer service scenarios:
AI is responsible for answering standardized questions and guiding customers to provide more information when necessary; Once identified as a possible escalation to a complaint or complex issue, immediately transfer it to manual personnel; After processing, humans deposit new problems and solutions back into the knowledge base, allowing AI to answer better next time.
（3） Compliance priority: special emphasis on data security and privacy protection
The training and use of AI models inevitably involve data.
In Hong Kong and other major markets, the protection of personal privacy, customer data, and trade secrets is a regulatory priority.
Therefore, when designing AI projects, Health Smart Hong Kong companies will focus on:
What data should be used? Is the source compliant? Have necessary authorizations been obtained? Is the model deployment method local, private cloud, or third-party platform? Has the data been leaked?
How to set permissions for different positions to avoid 'everyone can see all data'.
This is not only due to compliance requirements, but also out of responsibility to customers and partners.
4、 The Bridge Value of Hong Kong as a Landing Site for AI Applications
Hong Kong has several unique advantages in promoting the implementation of AI in traditional industries:
Multilingual and multicultural environment
Capable of collaborating with mainland teams, overseas clients, and third-party technology suppliers in the same project; It is beneficial to translate customers' business needs into language that the technical team can understand.
Mature professional service system
Legal, accounting, consulting, IT services and other supporting facilities are complete, and solutions can be designed from the perspective of "business technology compliance" integration; It is beneficial to ensure that the project can be implemented without crossing regulatory red lines.
Close connection with mainland industries
Many projects have business scenarios in mainland China, and technology and capital can be connected through Hong Kong; Hong Kong can play both a design role and a central hub for data and management.
The positioning of Health Smart Hong Kong Company is to play the role of "translator and integrator" between enterprises, technology providers, and financial institutions
Understand the business language of traditional industries;
It can also break down technical solutions into executable business steps; Taking into account both compliance and risk perspectives of financial institutions.
5、 From 'using AI or not' to 'is it more stable and stronger': What do banks value?
From the perspective of enterprises, whether to use AI or not seems to be a multiple-choice question;
From the perspective of a bank, what is more concerned is whether this enterprise is seriously improving its operational quality and management level
Traditional enterprises that implement AI through small pilot projects, human-machine collaboration, and compliance priority often have the following characteristics:
Having a clear understanding of one's own business and not blindly following hot topics; Willing to invest in processes and systems, willing to pay for long-term efficiency; There is an increasing standardization in data recording, process management, and risk control.
Such enterprises are more likely to gain the long-term trust of banks, even if they do not currently have a very impressive growth story.
Healthy Smart Hong Kong companies are willing to stand with these enterprises and make AI a tool to "make good businesses better and stable businesses more sustainable", rather than creating short-term gimmicks.