In May 2026, a leading logistics company's humanoid robot processed over 40,000 packages in a warehouse over 33 hours, with no manual intervention throughout. This is not an experiment, but daily operations already in production.
In the same month, a recruitment system based on AI Agent automatically completed the entire process from resume screening and initial interview invitations to interview scheduling, reducing the HR department's workload by 70%.
These things that are happening point in the same direction:AI Agent——Intelligent agents that can autonomously understand tasks, plan steps, invoke tools, and execute delivery——are truly "taking their posts."
Over the past two years, the leap in large language model capabilities has been impressive, but most enterprises still use AI at the level of "having a dialog box that can chat." What true enterprise-level applications need is: AI that can connect to internal systems, understand business context, and autonomously execute multi-step tasks——this is exactly where AI Agents come in.
Earlier this year, the release of DeepSeek's open-source model triggered a strong response in the global AI community. Approaching the capabilities of top closed-source models at extremely low cost has given more small and medium-sized enterprises the possibility of "being able to afford AI." More importantly, the open-source route allows enterprises to deploy locally, solving the core pain points of data privacy and security compliance.
China's AI technology route has thus gained broader international recognition. Driven by the open-source community, the AI Agent development toolchain based on domestic large models is rapidly maturing.
Another trend worth noting islow-code development platformsdeep integration with AI. Traditional low-code platforms have already greatly lowered the threshold for application development, and after introducing AI capabilities, they have further achieved the ability to "describe requirements in natural language and have the system automatically generate applications."
📊 Data: AI-assisted low-code development can makesoftware development efficiency increase by 300%. In the past, building an inventory management system took 2-3 weeks; now, with AI + low-code tools, a prototype can be completed in one day, and a usable version delivered in three days.For startups that need to quickly validate market demand, as well as traditional enterprises with complex internal informatization needs, this means a cliff-like drop in R&D costs.
Over the past decade, the logic of the SaaS industry was "move offline processes online, turn them into standardized products, and sell them to customers." This logic is being rewritten in the AI era.
AI-native applications are no longer just "adding an AI chat entry point," but are designed from the ground up around "AI can understand my business." A company doing customer service SaaS used to rely on configuring complex dialogue flows to support customer service; now it directly lets AI Agents learn historical customer service conversation records and autonomously generate response strategies——in the first month after launch, it helped customers reduce the rate of escalation to human agents by 40%.
Industry observers believe that within the next two years, SaaS products without AI Agent capabilities will face the risk of being eliminated. The entire SaaS industry is undergoing a reshuffle driven by AI: not "SaaS + AI," but "AI-native SaaS."
Cross-border e-commerce is one of the fastest scenarios for AI Agent implementation. In 2026, AI product selection tools can already automatically recommend high-potential products by analyzing social media trends, competitor reviews, and supply chain data. Combined with AI intelligent customer service, cross-border sellers can use a small team of no more than 5 people to operate omnichannel stores covering the US, Europe, and Southeast Asia.
The accuracy of AI product selection has increased by about 60%, and the response time of intelligent customer service has been compressed from an average of 3 hours to instant replies, reducing the return rate by 15% as a result. For companies going global, these numbers directly correspond to real profits.
The WeChat ecosystem opened more AI capabilities to developers in 2026. With the AI interfaces officially provided by WeChat,mini program developmentteams can add intelligent recommendations, voice interaction, image recognition, and other functions to applications without increasing too much R&D investment.
For teams engaged inWeChat development, this means a new window of opportunity——teams that can be the first to combine AI capabilities with WeChat ecosystem scenarios will gain a significant advantage in competition. For example, mini programs in the education industry have achieved personalized learning path recommendations through AI Agents; mini programs in the retail industry use AI to analyze user behavior and push precise offers at the right time, significantly increasing conversion rates.
Previous roundThe core of enterprise digital transformationis ERP, which solves the problem of "process standardization"—unifying the management of data such as procurement, inventory, finance, and human resources. But ERP's limitations are also obvious: it requires people to fill in data, trigger processes, and make judgments.
The logic of AI-native architecture is completely different: instead of waiting for people to operate it, it actively perceives system status and automatically makes decisions and executes them. For example, a supply chain management system integrated with an AI Agent can automatically initiate purchase requests, compare prices, place orders, and update logistics tracking information when inventory falls below the safety line—all without human intervention.
📊 Trend: In 2026, more than 30% of mid-sized enterprises have launched AI pilot projects, and about 15% have entered scaled deployment. This proportion is expected to exceed 50% by 2027.For most traditional enterprises, switching to an AI-native architecture in one step is neither realistic nor necessary. A more pragmatic path is:
According to Xiangming Technology's observations, in 2026 more than 30% of mid-sized enterprises have already launched AI pilot projects, of which about 15% have entered the scaled deployment stage. This proportion is expected to exceed 50% by 2027.
Digital transformation does not only happen at the software level in the office.IoTand the AIoT market officially broke through the trillion-yuan scale in 2026, driven by three directions:
Smart communities: AI access control systems have upgraded from simple "face-scanning to open the door" to a comprehensiveIoT management platformthat integrates visitor management, delivery notifications, security anomaly alerts, and automatic property work order dispatch. Property owners can complete all operations through a mini program, and property staff work order processing efficiency has increased by more than 3 times.Smart manufacturing: Sensor data on production lines is no longer just "recorded for the file"; instead, AI Agents analyze it in real time, issuing warnings before equipment failures occur and automatically adjusting production parameters.
Smart energy: Building energy management systems combine weather forecasts and historical energy usage data, with AI automatically adjusting equipment such as air conditioning, lighting, and elevators, reducing overall energy consumption by 20%-30%.
For companies involved inIoTandsmart hardware development, this is a period of technological dividend—AI reduces the development complexity of IoT systems, enabling smaller teams to create high-value products.
Apple's iterative version of Vision Pro continued to drive the wave of spatial computing application development in 2026. Spatial computing is not just "wearing glasses to watch 3D videos"; it represents the next paradigm of human-computer interaction: applications are no longer confined within screen borders, but integrated into physical space.
ForAPP development, this means a completely new approach to application design: How can information be presented naturally in three-dimensional space? How can multimodal interaction using gestures, eye tracking, and voice be utilized? These questions will give rise to a batch of excellent spatial-native applications.
At the same time, the combination of AI Agents and spatial computing is being explored—imagine an AI digital human that can "walk" into your office, interact with your real environment through spatial computing devices, help you check data dashboards, operate virtual whiteboards, and share 3D models. This is no longer a scene from a sci-fi movie, but a technological direction that is becoming reality.
In the AI industry of 2026, the most notable change is "demystification"—people no longer treat AI as a distant concept or a cool demo, but begin to use it as a productivity tool that can be implemented.
From robots in logistics warehouses, to AI product selection for cross-border e-commerce, from automatic generation on low-code platforms, to smart communities'IoT management platform—AI Agents are quietly entering every industry and every position.
For companies currently doingAPP developmentand system management, now is the best window of opportunity to deploy AI capabilities. The key to seizing this wave of technological dividends is not chasing the most cutting-edge models, but finding the scenario that best fits your own business and turning AI into real competitiveness.
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