Summary of the Key Points
This article focuses on the "K-shaped divergence" phenomenon in corporate digital transformation, revealing that the essence of this divergence is not that traditional companies are unwilling to transform, but rather that they encounter real difficulties. It clarifies the boundaries between informatization, digitization, and intelligentization. The article emphasizes that the foundation for corporate AI lies in decades of industry-specific knowledge accumulation, and that experienced professionals (the "old masters") remain crucial. Business models are evolving from selling products to providing services. Finally, it offers advice to young people: find an industry you want to dedicate yourself to and become the future "old masters" in that field.
I. The Truth Behind the K-shaped Divergence: It's Not About Unwillingness to Transform, but Barriers to Change
In recent times, companies leveraging AI have seen rapid growth on the capital market, while traditional firms have fallen behind—this is what constitutes the K-shaped divergence. However, the issue is not that traditional companies are averse to adopting AI; rather, they face two major challenges during the transformation process:
- Challenge 1: Organizational Structures Are Not Keeping Up: Many companies have tried to push digital initiatives by relying on their IT departments, but this has led to them going off track, forgetting the company's core purpose (for example, the core of a steel plant is steelmaking, not developing data systems).
- Challenge 2: Focusing Excessively on Efficiency Improvement: Everyone is trying to automate processes to reduce costs, which often results in reduced profits and even layoffs, making companies hesitant or unwilling to continue with transformation.
In simple terms, transformation is about more than just adopting technology; it's also about addressing issues related to people and the direction of the company's efforts.
II. Don't Mix Up Informatization, Digitization, and Intelligentization
Many people confuse these three concepts, but they each have distinct roles:
- Informatization = Infrastructure: This is like the V2X systems at traffic intersections that provide real-time information (such as when the lights will change). Its role is to collect and transmit data, serving as the foundation for further processes.
- Digitization = Analytical Capability: It takes the raw data (e.g., traffic signals, pedestrians, vehicles) and converts it into digital signals that can be analyzed to draw conclusions (for example, "There's an elderly person ahead; slow down").
- Intelligentization = Value Creation: It uses the analysis results to make automated decisions (e.g., a self-driving system that stops the car automatically), ultimately leading to customers being willing to pay for enhanced services (e.g., a higher price for a smart driving feature).
In other words, informatization provides the foundation, digitization processes the data, and intelligentization generates revenue.
III. The AI Foundation for Corporate Transformation: Experienced Professionals + Knowledge
The AI foundation is essential for successful transformation, just as a person needs a physical body to sit. How can this be established?
- Experienced Professionals Are Crucial: They understand the fundamental aspects of their industry (e.g., the core of steelmaking is the blast furnace, and the key to chicken farming is the health of the chickens) and can help companies find the right direction.
- Unraveling the "Knowledge Black Boxes": In many industries, people know how to do things but cannot explain why (e.g., the chemical reactions in a blast furnace or the optimal conditions for raising chickens). Technology can help break down these mysteries—for example, Beike Yili installed 2,000 sensors in blast furnaces, and Ward Chenlong used machine vision to monitor chicken health, turning experience into quantifiable data.
- AI's Role in Simplifying Complex Processes: Similar to how traditional Chinese medicine relies on empirical knowledge, AI can simplify complex systems by transforming variables into clear rules (e.g., if bacteria levels are too high, change the water). This can solve problems like insufficient production capacity (for example, Quanjude Roast Duck uses AI to increase output).
The essence of the AI foundation is to transform decades of industry expertise into a language that machines can understand.
IV. Evolving Business Models: From Selling Products to Providing Services
Intelligentization ultimately aims at generating revenue, which requires changes in business models:
- Selling Products: In the industrial era, selling products was effective (e.g., selling air separation equipment with users installing their own pipelines).
- Selling Related Services: In the industrial internet era, companies offer additional services alongside products (e.g., Hangyang installs equipment in factories and delivers gas directly).
- Providing Value-Added Services: In the intelligent era, companies create derivative services based on customer needs (e.g., dairy companies customize yogurt based on gut bacteria; gas companies deliver gas to factories that need it based on predictive analysis).
The key to providing value-added services is to meet customer needs that they are not even aware of, thus creating a profitable loop.
V. Advice for Young People: Choose an Industry You Want to Become an Expert In
In the AI era, education should encourage self-discovery. Young people should not be limited by conventional views:
- Some Jobs Cannot Be Replaced by AI: Tasks that require human skills and empathy (e.g., helping the elderly) will continue to be valuable.
- Dedicate Yourself to an Industry You Are Passionate About: Thatcher's Britain initially opposed robots, but they later realized they created new job opportunities. Young people should find industries they love and become the future "old masters" because experience is timeless.
In summary, AI enhances the value of human expertise, not replaces it. Young people should identify their areas of expertise and build a strong foundation for success in their chosen fields.
This article uses practical examples (blast furnaces, chicken farming, and奶茶 delivery) to explain complex transformation processes. The core message is that transformation requires focusing on the fundamental aspects (AI foundation and experienced professionals), upgrading business models (from selling products to providing services), and choosing the right direction for growth.