Summary: In the first half of 2026, AI Agents moved from a laboratory concept fully into production environments. Data shows that China's AIoT market is expected to exceed 1.2 trillion yuan, low-code + AI development efficiency has increased by 300%, and AI product selection and intelligent customer service have become standard in cross-border e-commerce SaaS tools. These are not future predictions—they are happening now.
In early June, at the 2026 Summer Game Fest, several tech giants released new products deeply integrated with AI, from smart hardware to cloud tools. AI is no longer a "bonus feature," but the core driving force of products. In the Chinese market, a deeper transformation is quietly advancing on the enterprise side—AI Agents are moving from "able to chat" to "able to work," and enterprise digital transformation has entered a true implementation phase.
For SMEs in Shenzhen and across the country, this means a critical window of opportunity. Whoever first finds the best integration point between AI and their own business will be able to widen the gap with competitors in the next two to three years. The following five implementation scenarios deserve serious consideration from every business decision-maker.
If last year everyone's discussion of AI Agents was still stuck at "it can help me write an email," then this year the answer to that question has become "it can help me complete an entire business process."
A real case: an AI Agent system deployed by an e-commerce platform can automatically handle the entire chain from user order placement to after-sales completion. After a user submits a refund request, the Agent automatically checks the order status, verifies whether the product has been shipped, evaluates the reasonableness of the refund, triggers the refund process, and generates a ticket report after processing is complete. The entire process involves interaction across 6 systems—the order system, warehousing system, customer service system, finance system, logistics system, and data analysis system—all independently scheduled and completed by the AI Agent.
According to data released by the platform, after the AI Agent went live, after-sales processing efficiency increased 4 times, and the manual intervention rate dropped from 100% to 12%, retaining only highly sensitive scenarios such as high-value refunds and customer complaint escalations. First response time was shortened from an average of 27 minutes to within 45 seconds.
The technical logic behind this is: the core capability of an AI Agent is not "knowing the answer," but "knowing how to do it." It understands task goals through large models, executes specific actions through tool calls (APIs, databases, RPA, etc.), and tracks task status through a memory system—this is essentially simulating a qualified junior employee.
According to industry data cited by 36Kr, in the first quarter of 2026, Chinese enterprises' investment in AI Agents increased by more than 250% year-on-year, and is expected to exceed 50 billion yuan for the full year. For SMEs engaged in software development and services, this means huge opportunities—helping enterprises sort out business processes, design Agent workflows, and build Agent invocation infrastructure is becoming an emerging service track.
Low-code platforms truly exploded in 2026. Gartner data shows that 65% of new application development worldwide has already been completed on low-code platforms. The addition of AI has further expanded the capability boundaries of low-code platforms.
In the past, the core value of low-code platforms lay in "drag-and-drop development"—encapsulating common components and logic into visual modules to reduce the amount of handwritten code. But a natural bottleneck was that when encountering complex logic or non-standard functions, dragging and dropping was actually slower than writing code. AI solved this problem—developers only need to describe requirements in natural language, and AI can automatically generate the corresponding logic modules.
A software development team in Shenzhen shared their practice: a client needed a mini-program containing five core modules: product display, online payment, coupon distribution, membership points, and a community forum. The traditional approach required at least 2-3 front-end and back-end people collaborating for a month and a half. Using AI-assisted low-code tools, they completed prototype construction within a week and delivered it online two weeks later. Development cost was only one-third of the traditional approach.
This means two things: first, the threshold for development requirements is lowering—what used to require evaluating "can this requirement be done and how long will it take" has now become "can existing AI tools do it faster"; second, the role of professional developers is upgrading—from "people who write code" to "people who design system architecture and control quality," with AI handling 80% of routine coding work.
For companies engaged in WeChat development and APP development, this is a change that must be embraced. Teams that are the first to master the "AI + low-code" development model are gaining significant efficiency advantages.
The concept of IoT has been discussed for many years, but only with the emergence of AIoT (Artificial Intelligence of Things) did IoT truly enter a stage of value release.
The core problem faced by traditional IoT systems is "lots of data, little insight." A smart community project generates tens of thousands of access control records, energy consumption data, and device operation logs every day, but lacks effective means of analysis. The addition of AIoT fundamentally changed this situation—AI can conduct real-time analysis of massive IoT data, identify patterns, predict trends, and automatically schedule resources.
Taking smart community solutions as an example, the new generation of AIoT platforms can achieve: AI access control systems that determine abnormal entry and exit through behavior recognition and automatically issue alarms; lighting and air conditioning in public areas that are intelligently scheduled in real time based on foot traffic, achieving energy savings of more than 20%; and elevator predictive maintenance systems that use vibration data analysis to warn of potential failures one week in advance.
According to forecasts by industry research institutions, China's AIoT market size will exceed 1.2 trillion yuan in 2026, with smart cities and smart communities being the two largest application directions. For a tech city like Shenzhen, this is both an opportunity and a challenge—how to use AIoT technology to improve urban governance and community service levels has become a real issue.
In 2026, the cross-border e-commerce industry underwent a transformation from a "product-listing model" to a "premium product model," and AI played the role of a booster in this transformation.
AI product selection tools are becoming standard for cross-border e-commerce sellers. By analyzing massive product data from global e-commerce platforms—including sales trends, price fluctuations, sentiment analysis of user reviews, and social media popularity—AI can predict which categories will enter an upward channel in the next 30-60 days. Data shows that sellers using AI product selection tools have a new product success rate 2-3 times higher than traditional methods.
The upgrade of intelligent customer service is equally significant. Traditional automatic replies can only handle simple inquiries like "where is my order," while intelligent customer service based on large models can already handle complex scenarios such as return and exchange negotiations, cross-border logistics exception handling, and multilingual communication. Data from a cross-border e-commerce SaaS platform shows that after integrating AI intelligent customer service, sellers' customer service teams shrank by an average of 60%, while customer satisfaction actually increased by 15%.
For SaaS development companies, this means AI capability is no longer a "bonus item," but a "must-have." E-commerce SaaS tools that can deeply embed AI into product functions are rapidly seizing market share.
If the first four trends are innovations at the "point" level, then the migration of enterprise management software from traditional ERP to AI-native architecture is a systematic transformation at the "surface" level.
The core logic of traditional ERP is "people enter, machines record." The logic of AI-native management systems is "machines do, people supervise." A typical AI-native management system should have the following characteristics: AI can automatically handle routine approvals (such as reimbursement review and purchase order placement), reporting to humans only when anomalies occur; the system can automatically generate business analysis reports and predictive suggestions based on historical data; and workflows can be dynamically invoked and orchestrated by AI Agents rather than being hard-coded.
According to industry analysis, leading domestic SaaS providers have already released product roadmaps for AI-native architecture. Although complete migration from traditional ERP to AI-native will still take 1-2 years, a clear signal has already been sent: when selecting or upgrading management systems, whether they support AI Agent integration and automated workflow orchestration is becoming a core evaluation criterion.
Looking back at the changes in the AI industry in the first half of 2026, several keywords emerge: implementation, efficiency, and reconstruction. AI is no longer just a tool for "chatting," but is penetrating every link of enterprise operations—from after-sales customer service to warehouse sorting, from code generation to community management.
For SMEs in Shenzhen and across the country, this is both the best of times and an era that tests judgment the most. The technology dividend truly exists, but it will not be distributed evenly. The key is: can you find the entry point in your own business where "AI can generate real value," and then quickly validate and quickly iterate.
It may start with integrating AI customer service into a mini-program, or connecting an old warehousing system to AIoT monitoring, or implementing a low-code + AI development process. No matter where the starting point is, the important thing is to take action first.
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