Summary of Key Highlights
At the 2026 Amazon Web Services China Summit, the AI applications demonstrated by companies were no longer vague plans in the “exploration” or “pilot” phase; they represented tangible results with real numbers—such as an AI code coverage rate of over 70% and the ability to complete video editing in just one minute. This marks the turning point for the explosion of Agentic AI (agent-based AI): advancements in large-scale model technology combined with a mature agent engineering framework have transformed AI from a “supporting tool” into a “core driver of business transformation.” Companies from various industries (automotive, transportation, consumer electronics, and AI exports) are using AI to revamp their internal processes, eliminate user barriers, and break through the limitations of their business scale. Amazon Web Services’ five-layer technology stack, ranging from computing power to agent applications, provides the critical support for these implementations.
1. AI is no longer just empty promises; companies present real results
In previous years, when companies talked about AI, they often used terms like “exploration” and “pilot.” This year, those phrases were largely absent, replaced by concrete performance data:
- Xpeng Group: The AI code coverage rate exceeded 70%, allowing non-technical personnel (such as product managers and operations staff) to directly use AI to generate product prototypes.
- Hello: The accuracy of fuel gauge recognition for car rental services increased from 70% to over 90%, improving efficiency by 50% in complex business processes and by 67% in innovative use cases.
- Insta360: The AI-based video editing feature has a 50% adoption rate, with one out of every two users using it for content creation, and videos can be produced in as fast as one minute.
- Moonlight: Overseas revenue surpassed domestic revenue, with 20 days’ worth of income matching the entire year’s earnings in 2025.
These figures indicate that AI has evolved from a “lab toy” to a productive tool that companies are actually using for real work. The focus has shifted from asking whether AI is possible to how to implement it more comprehensively.
2. AI helps companies with two main tasks: increasing internal efficiency and simplifying user experiences
Companies use AI in two primary ways: improving internal operations and optimizing the user experience:
Improving Internal Operations:
- Xpeng: Using AI to manage AI itself has addressed the bottleneck of code review. While AI can write code faster than humans, manual review is slower and becomes a bottleneck. Xpeng’s “Lingxi Platform” allows AI to write code, identify errors, test, and deploy it, freeing engineers from the task of writing code and enabling them to focus on supervising the AI process. Non-technical personnel can now directly turn ideas into prototypes—developing capabilities are no longer the exclusive domain of the technical department.
- Hello: With multiple business lines such as car sharing and ride-hailing, coordinating resources across teams was previously slow. Using a unified AI development tool, the development cycle has been reduced from days to hours, significantly breaking through resource constraints.
Optimizing User Experiences:
- Insta360: The “Moment Pro” feature eliminates the need for users to edit videos manually. Users can simply take photos and specify their requirements in natural language, and AI will produce the final product in one minute.
- Hisense: With its AI agent, users don’t need to learn how to operate appliances; they just need to take a photo of the item they want to wash (e.g., “wash a woolen sweater”), and the AI recommends the appropriate washing method. The interaction has shifted from users learning about products to products understanding user needs.
3. For AI to be effective, a solid technical foundation is essential
Many companies want to use AI, but having models alone is not enough. To move from “being able to do something with models” to “implementing them in business operations,” five issues need to be addressed: computing power costs, model selection, data management, scalability, and task orchestration. Amazon Web Services’ five-layer technology stack addresses these challenges:
1. AI Infrastructure: Stable computing power (e.g., elastic computing to avoid waste).
2. Model Layer: Choosing the right foundational models (e.g., Amazon Bedrock supports multi-model integration).
3. Data and Knowledge Layer: Handling large amounts of data (e.g., using S3 Vectors to store video materials).
4. Agentic Platform: The ability to orchestrate AI agents (e.g., Bedrock AgentCore reduces concurrent processing time from 30 minutes to 1 minute).
5. Agent Applications: Specific AI tools for different use cases (e.g., Insta360’s “Moment Pro”).
Lacking any one of these layers would prevent AI from moving beyond the prototype stage. For instance, Insta360’s ability to produce videos in one minute is due to the complete implementation of this entire process, which avoids the waste of computing power.
4. AI not only improves efficiency but also changes the way work is done
Amazon Web Services’ Chu Ruisong stated, “In the past, humans were the main focus, and technology was a tool; in the future, humans and AI will collaborate to create value together.” This highlights a profound change:
- Changing Roles: Engineers at Xpeng have shifted from executing tasks to supervising processes, and scientists at Fosun Pharma have been freed from tedious paperwork (AI has reduced the review of clinical trial reports from weeks to 5 minutes).
- Breaking Through Human Capacity Limits: HOLLA Group’s video social platform matches users 30 million times daily; manual review would be impossible, but AI intercepts illegal content with an accuracy rate of 99.7%. Ad optimizers at Yidian Tianxia have gone from managing 30 ads to 300.
- Changing Organizational Structures: Non-technical personnel at Hello and Xpeng can now use AI for development, spreading development capabilities throughout the company and unlocking organizational potential.
These changes are more than just about improving efficiency; they represent a shift in the paradigm of corporate organization and value creation. AI is no longer an optional extra but a core capability essential for business success.
5. AI implementation enters a new phase: from “single-point breakthroughs” to “full-chain integration”
The cases presented at this year’s summit all share one common theme: AI is no longer a limited pilot project in a single department but a comprehensive initiative across the entire business. For example:
- Hello uses AI not just in car rental but as a foundational technology across all its services.
- Insta360 aims to create a “photography robot” where users only need to take photos, and AI handles the rest of the editing process.
- Moonlight sells models but also leverages Amazon Web Services’ compliance and market reach to export its AI capabilities globally.
For companies, the competition is no longer about whether they have AI or not but whether they can integrate it seamlessly across all business processes. Those that succeed in doing so will accumulate data and experience, creating a competitive advantage that later entrants may struggle to match. Companies still hesitating may fall behind.
In Conclusion
AI has moved beyond the stage of being just an interesting concept; it is now being put into practical use. It not only helps companies save time and effort but also quietly transforms the way we work and live. This is not a future prospect but a reality that is already happening.