In the second quarter of 2026, two landmark events occurred in the global SaaS sector: Salesforce officially embedded Einstein GPT into the core interaction layer of all its product lines, while a group of domestic mid-sized SaaS vendors with annual revenues between 30 million and 100 million yuan began proactively delisting products and transforming to AI-native architectures. According to the latest IDC data, as of June 2026, in China's SaaS market,more than 42% of vendors list "AI-native" as the primary strategic direction in their product roadmaps, nearly tripling compared with 17% in early 2025. This is not a slow incremental iteration,this is a reshuffle measured by "intelligent decision-making capability"。
Over the past decade, the core value of enterprise software was "digitalization"—moving paper processes to the cloud and replacing Excel with dashboards. But SaaS products in this era were essentiallypassive tools: data had to be entered by people, reports had to be interpreted by people, and anomalies had to be discovered by people. Now, however, the combination of large language models, multimodal models, and Agent frameworks is turning software from a "passive recorder" into an "active decision-maker." When a CRM system not only tells you that the customer churn rate is 12%, but automatically analyzes the root causes of churn, generates retention strategies, and triggers execution actions, the product definition of traditional SaaS is completely rewritten.
There is currently a widespread misunderstanding in the industry: adding an AI chat window to an existing SaaS product, or integrating a large model API to provide "intelligent Q&A," is considered completing an AI upgrade. In fact,there is an essential structural difference between AI-native applications and the "AI wrapper" of traditional SaaS。
The traditional SaaS plus AI approach usually follows this technical path: add an LLM-calling middleware on top of the existing RESTful API layer, convert the user's natural language input into SQL queries or API calls, and then return the results in natural language. This looks like AI in terms of user experience, but the underlying data model, permission system, and business logic are still deterministic and rule-driven. The ceiling of this approach is obvious—when the AI's suggestions conflict with preset business rules, the system cannot handle ambiguous decisions.
The architectural logic of AI-native applications is the reverse:the model is a first-class citizen, and rules are second-class citizens. Data is no longer stuffed into fixed relational tables, but is also stored in vectorized form in vector databases for real-time retrieval and reasoning by the model. The business logic layer changes from deterministic "if-else" rules to an Agent workflow of "intent recognition + context injection + multi-step reasoning." A typical example is an AI-native customer service ticketing system: when a user submits a "logistics delay" complaint, the system does not assign the ticket to a customer service group according to fixed rules, but instead an Agent automatically pulls the order trajectory, warehouse inventory, and historical similar cases, and comprehensively determines whether escalation is needed or automatic compensation should be triggered—for the first time, software has "judgment"。
This distinction has a major impact on technology selection. The AI-native tech stack requires advance design for capabilities such as concurrency management for LLM calls, throughput optimization for vector databases, and secure sandboxes for tool calls in Agent orchestration. This cannot be accomplished by adding a few lines of API calls to existing code—it involves refactoring backend middleware, changing the frontend interaction paradigm, and implementing bidirectional synchronization mechanisms at the data layer.
There used to be a view that the AI-native wave would first land in large enterprises, while SMEs, limited by technical capability and budget, would be the last wave. But the actual trend in 2026 is exactly the opposite.
According to a report released in June 2026 by the SME Development Promotion Center of the Ministry of Industry and Information Technology,the proportion of domestic SMEs adopting AI tools increased by 218% year-on-year, with more than 60% of them using AI capabilities through SaaS subscriptions. Compared with the internal system transformation cycles of large enterprises, which often exceed 18 months, SMEs have lighter technical debt and shorter decision-making chains, making them instead the most active testing ground for AI-native applications. Insoftware development, this is especially obvious: a set of AI-assisted code generation tools plus an automated testing pipeline can enable a 5-person frontend team to produce what used to require 15 people.
In the fields ofWeChat developmentandmini program development, this trend is also accelerating. The WeChat mini program ecosystem continued to open AI capability interfaces in 2026—from intelligent customer service to product recommendations, from image recognition to voice interaction—allowing small and medium-sized merchants to integrate machine learning capabilities into their own mini programs without building their own AI teams. At the same time, the field ofAPP developmentis also being restructured by AI orchestration tools: in traditional mobile development, UI slicing, multi-platform adaptation, and API integration—the most time-consuming tasks—can now be made 2-4 times more efficient with AI tools.
Of course, a lower technical threshold means a higher competitive threshold. When "fast development" is no longer a differentiated advantage,deep understanding of enterprise business scenarios and the ability to implement intelligencebecome the new moat. This is also why pure software outsourcing is being replaced by a "solutions + continuous operations" model—what customers want is no longer just a system that runs, but an intelligent system that can help the business continuously evolve.
If the boundary of traditional SaaS ends at screens and databases, then the combination of AI-native + SaaS +IoTis extending that boundary into the physical world.
The actual commercial value of this direction has already been verified in some scenarios. Insmart community solutionsIn the past, property management systems did things like registering visitors, recording payments, and dispatching repair orders—essentially one-way information collection. But after integrating AI Agents and IoT devices, the system achieves a closed decision loop: after security cameras identify abnormal personnel trajectories, they automatically notify the nearest security guard and retrieve access control records for the corresponding area; after elevator sensors detect abnormal operation, they automatically generate a maintenance work order and match the nearest certified maintenance personnel. From "recording what happened" to "automatically responding to what is happening," this is a fundamental shift in the value proposition of enterprise software.
This shift also imposes new requirements on the technology stack for enterprise software development. The request-response model of traditional web applications no longer applies—IoT devices produce continuous streaming data, and AI Agents need a low-latency, event-driven architecture. This means the backend architecture needs to shift from synchronous REST APIs to a dual-channel model of asynchronous message queues (such as Kafka/Pulsar) + WebSocket/SSE. The data layer then needs to simultaneously support time-series data (device metrics), relational data (user information), and vector data (semantic retrieval)—this is nearly impossible to achieve in a monolithic application, and it must move toward microservices or even a mesh-based deployment architecture.
Looking back from this point in July 2026, the transformation the SaaS industry is undergoing is no less profound than "cloud replacing on-premises deployment" ten years ago. Over the next 18 months, there are three key variables worth continuously tracking:
First, the standards battle among Agent orchestration frameworks.Platforms such as LangChain, AutoGPT, MetaGPT, and ByteDance's Coze are competing for the position of "the operating system for AI Agents." Whoever becomes the de facto standard for enterprise-grade Agent development will control the gateway to the next generation of enterprise software.
Second, the declining curve of model inference costs.In the first half of 2026, the inference cost of mainstream large models has already fallen by about 60% year over year. If this trend continues, by 2027, the economics of "letting the model make all decisions" in a single business scenario will be fully viable—this will allow AI-native products to deliver a double blow of price + capability against traditional SaaS.
Third, the speed at which industry know-how becomes datafied.No matter how strong a general model is, when it lands in a vertical industry it requires the injection of domain knowledge. Manufacturing, healthcare, construction, agriculture—the degree to which these industries' data assets are capitalized and structured basically determines the penetration speed of AI-native applications in those fields.
For enterprises currently observing this wave, there are several practical suggestions:
Don't wait for "the technology to mature" before acting. The AI-native technology stack iterates every day, but the cost of waiting is far higher than the cost of trial and error. You can start with a specific business bottleneck—such as customer service efficiency, report generation, or inventory forecasting—and use an AI Agent to do a small closed-loop validation, then expand after it works.
Don't overestimate the model itself or underestimate engineering capability. Model capabilities are public and converging; what truly creates differentiation is the engineering system built around the model: data cleaning pipelines, prompt version management, and quality monitoring and rollback mechanisms for model outputs. These "boring" engineering tasks are the foundation that determines whether an AI-native product can operate stably over the long term.
Choose a technology partner with industry implementation experience. There is a huge transformation gap between general AI capabilities and specific business scenarios. A partner that understands industry business logicsoftware developmentteam is more valuable in practice than a star-studded AI algorithm team—the latter is good at training models, while the former is good at connecting models into real business systems, enabling technology to truly create measurable value.
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