Summary of Key Points
During the 2026 World Artificial Intelligence Conference (WAIC), the WAIC Entrepreneurs Forum focused on three core issues regarding corporate AI transformation: "strategy, tactics, and value." The forum invited executives and experts from industries such as manufacturing, ICT, and consumer goods. Through speeches, roundtable discussions, and interactive sessions, they shared practical experiences in implementing AI and provided guidelines for avoiding common pitfalls. It was also announced that subsequent initiatives, including scenario presentations and industry matchmaking events, would be organized to move AI from a stage of conceptual exploration to actual industrial application. The main takeaway from the forum is that AI is not merely an upgrade of existing tools; it represents a critical opportunity for companies to reshape themselves and stay ahead in the digital age. To successfully implement AI, it is essential to choose the right use cases, integrate it with organizational structures, and combine it with industry-specific knowledge in order to convert investments into tangible benefits.
I. AI Is Not Just a Tool; It’s the Key to Reshaping Enterprises for the Future
The most significant strategic consensus from the forum was that AI should not be seen as a simple addition of "intelligence" to existing business processes but rather as a means to fundamentally transform business models and management approaches:
- Professor Zeng Ming on “Intelligent Compound Interest”: AI systems, like snowballs, become more efficient with use. For example, when used for customer service, they learn from each interaction and provide more accurate responses. This characteristic of continuous improvement is the opposite of traditional industrial equipment, which tends to degrade over time. In the next five years, those who can effectively leverage AI will emerge as industry leaders.
- Shift in Management Logic: While companies have traditionally focused on managing complexity aspects such as processes, supply chains, and globalization, the focus now shifts to leveraging AI for enhanced efficiency—such as coordinating with employees or transforming industry expertise into usable knowledge for AI. Only those willing to reinvent themselves will avoid being left behind by technological advancements.
II. From Pilots to Full-Chain Integration: How Can Companies Make AI Practical?
Many companies are still in the pilot phase of AI implementation, but the forum provided practical examples of how to overcome these challenges:
- L’Oréal: L’Oréal has integrated AI throughout its entire value chain as a driver of growth. Approximately 70% of its consumers use AI in their purchasing decisions. The company uses AI for everything from product development (optimizing beauty formulas) to supply chain management (using AI vision inspection and digital twins in its factories in Suzhou) to customer service (personalized recommendations), significantly improving innovation and delivery speed.
- DingTalk: DingTalk’s transformation involved three steps: first adding AI to existing functions (e.g., intelligent attendance systems), then breaking down the platform into modular components for AI agents to use, and finally launching a corporate-level AI platform called “Qianwen Office.” They even predict that the largest users of AI in the future will be other AI systems, such as those that can automate processes.
III. No Returns on AI Investments? The Problems Lie in Organization and Implementation Details
Many companies struggle to see returns on their AI investments. The forum highlighted key issues:
- Dell’s Lessons: Although 2000 engineers using AI programming assistants increased their efficiency by 20%-40%, the company’s financial performance did not change because individual improvements did not translate into overall organizational effectiveness. For example, if team collaboration processes remain unchanged, the time saved by AI could be wasted due to internal inefficiencies.
- Industry Experts’ Guidelines for Avoiding Pitfalls:
- Education: AI should move from answering questions to completing tasks (e.g., automatically grading assignments and generating personalized learning plans), and existing fragmented tools should be integrated into a unified AI system.
- Manufacturing: AI should not replace expert human knowledge; if the accuracy of models is below 99%, it may not be worth using. Real work processes need to be broken down into manageable tasks suitable for AI, and industry-specific knowledge should be utilized as training data.
- Physical AI: Model development takes time. In 2023, AI might only be suitable for basic tasks; by 2025, it could reach the level of expertise required for senior managers.
IV. For Chinese Manufacturing: Choosing the Right Use Cases Is More Important Than Showcasing Technology
The manufacturing sector is a critical area for AI implementation. Forum participants offered practical advice:
- Haier’s “Three Intensities” Principle: Prioritize use cases that are process-intensive, labor-intensive, or information-intensive. Avoid three common pitfalls: AI solutions that solve only minor issues, AI systems that do not contribute to core business operations, and large-scale models that are poorly suited for specific tasks (e.g., controlling machinery directly). Haier has developed over 4,700 mechanism models for AI quality inspection, with an accuracy rate of over 99%.
- Gree’s Practical Approach: Instead of developing humanoid robots, Gree focused on industrial robots. By producing more than 2,000 intelligent devices, they increased production efficiency by 200% and were recognized as a “pioneer-level smart factory.” The key is to ensure that AI complements existing business processes and addresses real problems.
V. Sharp Questions Reveal Real Answers; the Forum Is Just the Beginning
The interactive sessions of the forum attracted much attention, with questions from observers (such as Starbucks, Fudan University, and investors) addressing common challenges faced by companies:
- Do middle managers still have value in the AI era?
- What are the real needs for AI services?
- Where is the ultimate limit of AI in manufacturing?
Guests provided straightforward answers: Middle managers need to shift from traditional roles to those of AI collaboration facilitators; the real need for AI is to solve specific business problems, not just to showcase technology. In manufacturing, AI cannot replace the expertise and judgment of experienced workers.
In conclusion, the organizers stated that this was just the first step in a series of actions. Future initiatives, such as scenario presentations and industry meetings, will help transform the ideas discussed at the forum into concrete actions, enabling AI to truly create value in the industrial landscape.
The value of this forum lies in its practical nature. It provides companies with a practical guide for AI transformation, emphasizing the importance of clear strategy, selecting the right use cases, integrating AI with organizational structures, and leveraging existing knowledge to achieve tangible results.