On June 7, 2026, the Beijing Gaokao English essay prompt "Help Li Hua write a letter about AI" trended on social media, and on the same day the whole internet hotly discussed "When I fed this year's Gaokao Chinese essay prompt to AI." This AI practice in the education world reflects a deeper trend: AI is no longer a concept in the laboratory, but has truly entered classrooms, offices, and production lines. At the same time, Japanese and South Korean stock markets suddenly plunged due to AI bubble concerns, and privacy controversy over smart glasses sparked social discussion—the acceleration phase of AI implementation is full of both opportunities and growing pains.
The 2026 Gaokao is especially special. The Beijing Gaokao English essay required candidates to write a letter as Li Hua discussing AI; and after the Chinese exam ended, a large number of candidates and parents immediately "fed" the essay prompt to various large models to compare AI's answering ability. Baidu hot search data shows that related content received more than 200 million views on the first day of the Gaokao.
This is not a coincidence. At the beginning of 2026, the Ministry of Education had already included "Foundations of Artificial Intelligence" as a required module in high school information technology, and more than 3,000 middle schools nationwide have opened general AI courses. From teaching assistance to exam question design, AI is becoming a standard configuration in China's education system.
Behind this trend is a larger industrial logic: when the younger generation starts using AI tools from their student years, their acceptance of AI-native applications after entering the workplace will be far higher than that of the previous generation. For companies engaged insoftware developmentandmini program development, this means that within the next 3-5 years there will be a huge market demand for AI-native applications. Whether it is educational APP development or the intelligent upgrade of enterprise training management systems, AI capabilities will become standard rather than a highlight.
Just as the Gaokao was taking place, Japanese and South Korean stock markets suddenly plunged, with AI concept stocks among the biggest decliners. Market analysis generally believes that behind this round of decline is a reassessment of AI return on investment—over the past two years, major tech companies have invested more than $200 billion in AI infrastructure, but the actual profitable scenarios that have landed are still limited.
This round of adjustment may be exactly a necessary stage for the AI industry to move toward maturity. Looking back at the history of the internet, the bursting of the internet bubble in 2000 eliminated a large number of companies with fake demand, but laid the foundation for the true golden age of the internet. Today's AI market is undergoing a similar "eliminating the false and preserving the true."
So what kind of AI application scenarios can truly create commercial value? Industry consensus is converging on several directions: intelligent customer service, automated processes, assisted programming, and personalized recommendations. In these scenarios, AI Agents are not a "brain" replacing people, but an efficient assistant embedded in specific business processes.
For teams focused on enterprise services inWeChat developmentandAPP development, this means products should be designed with priority under the framework of "human-AI collaboration," rather than pursuing the fully automated goal of "unmanned" operations. According to observations, among AI applications successfully commercialized in the first half of 2026, more than 75% adopted a "human-machine collaboration" model rather than complete replacement.
Another hot news item during the same period was the controversy over Rokid smart glasses being questioned as a "secretly filming artifact." As the integration of AI software and hardware accelerates, privacy compliance issues for devices such as smart glasses, AI voice recorders, and smart cameras have surfaced. This incident reminds the entire industry: the barriers to implementing AI applications are not only technical issues, but also legal and ethical issues.
From the perspective of theIoTindustry, the boundaries of data collection for hardware devices are becoming increasingly sensitive. In the "Administrative Measures for Generative Artificial Intelligence Services (Revised Version)" issued in May 2026, the Cyberspace Administration of China specifically added compliance requirements for AI hardware data collection, clearly stipulating that "real-time audio and video data processing must obtain explicit two-way consent." This will have a direct impact on product design for scenarios such as smart community solutions, intelligent security, and AI access control.
ForShenzhen software developmentcompanies, this is both a challenge and an opportunity—compliance capability is becoming a core indicator in software outsourcing development selection. When choosing software service providers, clients no longer only look at the speed and cost of feature implementation, but pay more attention to the product's level of data security compliance.
In industries such as education, finance, retail, and manufacturing, the implementation of AI Agents is moving from "proof of concept" to the "large-scale deployment" stage. According to industry data, in the first half of 2026 China's AI Agent market size reached 8.2 billion yuan, a year-on-year increase of 240%.
Typical application cases include:
Education sector:After an online education platform integrated an AI Agent, the response time for students' after-class questions was shortened from an average of 24 hours to 3 minutes. The AI Agent not only answers questions, but can also automatically generate personalized practice questions based on students' wrong-answer records, increasing tutoring efficiency by 5 times.
E-commerce retail:During 618, many e-commerce platforms deployed AI customer service Agents based on large models. Unlike traditional keyword-matching robots, the new generation of AI Agents can understand complex semantics and context, handling full-scenario inquiries such as refunds, complaints, and logistics queries, with the first-contact resolution rate increasing from 45% to 82%.
Enterprise management:More and more enterprises are beginning to embed AI Agents into management system development. A typical scenario is: after sales staff enter customer conversation records into the CRM system, the AI Agent automatically extracts key information, generates follow-up suggestions, and updates the opportunity stage—work that previously required 3 person-days for lead cleaning and entry is now reduced to 30 minutes.
The popularity of AI Agents has also accelerated demand forlow-code development platforms. Enterprises hope that business personnel can independently build and manage AI Agents through low-code tools, without having to rely on technical teams every time. This is consistent withThe major trend of enterprise AI transformation is highly aligned—AI is no longer an exclusive tool for the IT department, but a productivity lever for every business line.V. WeChat Mini Program Ecosystem: The Next Explosion Point for Open AI Capabilities
For service providers engaged in WeChat mini program development, this is a huge incremental opportunity. In the past, the core competitiveness of mini program development lay in interface design and interactive experience; now, the integration of AI capabilities is redefining the standard for a "good product." If an e-commerce mini program can generate personalized recommendation copy in real time based on a user's browsing history, its conversion rate may increase by 3-5 times.
According to a 36Kr report, within the first week after WeChat opened its AI capabilities, more than 2,000 mini programs had integrated AI capabilities. Feedback from leading developers shows that after AI features were integrated, average user dwell time increased by 40%. For APP development teams looking for business growth points, this also means they can replicate AI capabilities from the WeChat ecosystem into their own APPs, forming a cross-platform intelligent experience.
VI. From ERP to AI-Native Architecture: The Next Decade of Enterprise Digital Transformation
The core logic of traditional ERP is "process solidification"—freezing an enterprise's management processes in software. But the logic of AI-native architecture is the opposite: it gives processes adaptive capability. An AI-native CRM system can automatically learn the behavior patterns of the sales team and continuously optimize recommendations; an AI-native inventory management system can adjust replenishment strategies in real time based on weather, holidays, and competitor dynamics.
This kind of architectural upgrade poses
e-commerce platform developmentandandwith entirely new technical requirements: the data middle platform needs to support real-time feature engineering, the front end needs to adapt to dynamically generated content, and the back end needs to bear the computing power consumption of large model inference. According to estimates by leading companies in the industry, migrating from traditional architecture to AI-native architecture requires 1.5 to 2 times the overall development investment of the former, but the improvement in operational efficiency can reach 5-10 times.For the implementation of large model applications, 2026 is at a critical stage—the pace of capability improvement in foundation models is slowing, but innovation at the application layer is accelerating. This means the release of technological dividends is shifting from "building large models" to "using large models." For small and medium-sized enterprises that do not have the capability to develop their own large models, this is the best time: they do not need to invest in the underlying arms race of hundreds of billions of parameters, but only need to find suitable application scenarios to embed AI capabilities into products.
VII. Xiangming Technology's Perspective: A New Paradigm for Technical Services in the AI Era
This is not a simple stacking of features. AI integration means that a product's architecture must change from "deterministic logic" to "probabilistic logic"—the response is no longer a definite database query result, but a dynamic output based on model inference. This places new demands on full-stack
software developmentsoftware developmentThe
Shenzhen software developmentindustry is undergoing this round of capability reshaping. As a major hub of China's software industry, Shenzhen's dual advantages in hardware supply chains and software ecosystems make it a natural testing ground for AI application implementation. From IoT terminals for smart homes to cloud-based AI for enterprise-level management systems, from AI access control in smart communities to intelligent customer service for cross-border e-commerce—the role of Shenzhen IT companies in the AI era is shifting from "code factories" to "AI solution providers."
Smart community solutions are the frontier of this transformation. Traditional smart community projects focus on hardware installation and data collection; smart community solutions in the AI era need to achieve a closed loop of "perception-decision-response." Take property management systems as an example: AI Agents can automatically analyze high-frequency issues in owners' repair records, determine whether they are caused by equipment aging or improper use, and then provide suggestions for batch repairs or enhanced training—analysis that in the past required a project manager to spend 2 days manually organizing.
Based on current industry trends and practical project experience, enterprises promoting AI application implementation in 2026 can follow these three principles:
Principle One: Start with high-frequency, low-complexity scenarios.Do not try a fully "unmanned" end-to-end AI solution from the beginning. Start with high-frequency scenarios with relatively high error tolerance, such as customer service assistance, document summarization, code completion, and anomaly detection, and gradually expand after accumulating AI application experience.
Principle Two: The controllability of AI Agents is more important than intelligence.At present, large models still have hallucinations and unpredictability, so "manual approval nodes" should be set up in key business processes. A good AI Agent design does not pursue 100% automation, but automates 80% of routine scenarios while leaving the remaining 20% of complex scenarios to human handling.
Principle Three: Data infrastructure comes first.The underlying support for AI applications is high-quality data. Before introducing AI, first complete data cleaning, labeling, governance, and knowledge base construction. Without structured, high-quality data, even the best large model cannot output valuable business insights.
Although the few days of the college entrance examination are over, the AI topics that repeatedly appeared in the exam questions foreshadow that this generation of young people's way of thinking and working will be completely changed with the companionship of AI. For enterprises and developers, AI is not only a technological upgrade, but also a cognitive upgrade—from understanding what AI can do to designing what AI should do.
In this transformation, what truly matters is not the scale of model parameters, but the ability to convert AI capabilities into actual business growth. Do a good job insoftware development、WeChat development、mini program development、APP developmentandIoTtechnology service provider, is evolving from a "tool provider" to an "intelligent solution builder." There is no shortcut on this path, but the direction is already in sight.
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