Over the past few years, the development of the AI (Artificial Intelligence) industry has primarily revolved around model capabilities. From parameter scale to inference performance, and from multimodal capabilities to training efficiency, foundation models have continuously pushed the boundaries of technology, propelling AI applications into a new stage of development.
However, as AI truly enters enterprise scenarios, a more realistic issue gradually emerges: using advanced models does not guarantee that enterprises will reap benefits. Enterprises face a more complex real-world environment: How can data accumulated over years be understood by AI? How can a massive number of existing systems be connected with AI agents? How can new AI applications be rapidly built while complying with enterprise engineering standards? These questions determine whether AI can transition from technical demonstrations to business production.
During the 2026 World Artificial Intelligence Conference (WAIC), Primeton Information proposed the concept of an "AI-Native Digital Infrastructure," exploring the evolution path of enterprise digital infrastructure towards the AI era around three major directions: intelligent data governance, AI application development, and comprehensive integration.
This reflects an industry trend: Given the gradual popularization of large model capabilities, enterprise AI competition is shifting from competing on model capabilities to competing on infrastructure capabilities that support the scaled application of AI.
01. AI Enters the Deep Waters of Industrial Application, and the Focus of Enterprise Competition is Shifting
The development of AI is entering a new phase.
In the past, enterprises focused on "whether they have AI capabilities." Whether to integrate large models, build intelligent assistants, or launch AI applications has become a crucial metric for measuring the intelligent exploration of enterprises.
As more and more enterprises begin to practice AI applications, the issues of concern in the industry are changing. Enterprises have found that integrating a large model into business does not automatically generate value. An AI application truly deployed in a production environment needs to meet multiple conditions simultaneously:
It requires high-quality data as a foundation, enabling AI to understand enterprise business;
It requires an efficient software development system to allow AI applications to be rapidly built and continuously iterated;
It requires the capability to connect enterprise systems, enabling AI agents to truly enter business processes.
In other words, the implementation of enterprise AI is a systematic engineering project involving data, software, systems, and business processes.
As Li Sun, Deputy Director of the Platform Department at the AI Institute of CAICT (China Academy of Information and Communications Technology), pointed out at the WAIC, the current AI industry still faces the issue of insufficient collaboration among "data, models, and applications." Although more and more enterprises are beginning to build high-quality industry datasets, how to further promote the closed-loop integration of data, models, and applications remains a critical issue that the industry needs to resolve.
02. Enterprise AI Implementation Requires Crossing Three "Major Mountains"
The First Mountain: The Data Mountain — AI Needs to Understand the Enterprise
Enterprises do not lack data. After years of digital construction, most enterprises have accumulated a massive amount of business data, covering multiple domains such as customers, transactions, supply chains, production, and operations.
However, data scale does not equal data value. In the traditional digitalization stage, enterprise data primarily served the role of recording and analysis. In the AI era, data needs to further become a crucial foundation for models to understand business and generate intelligent results. The reality is that enterprise data is often scattered across different systems and generated by different departments, leading to issues such as inconsistent standards, mismatched metrics, and uneven quality.
For example, the same customer may exist simultaneously in the marketing system, transaction system, and customer service system; the same business metric may also yield different results due to different statistical rules.
These issues might only affect management efficiency in traditional business systems, but they will directly impact model performance in AI applications. This is because the quality of AI output largely depends on the quality of input data.
Therefore, data governance in the enterprise AI era needs to evolve from past data management towards data supply oriented towards intelligent applications. Enterprises need data assets that can be understood, invoked, and reused by AI. This is also a crucial reason why high-quality datasets are receiving significant attention in the industry.
The Second Mountain: The Software Engineering Mountain — AI Needs to Enter the Production System
Beyond data, enterprises face another critical issue: how to rapidly build a large number of AI applications?
As AI enters business scenarios, what enterprises need to build is no longer a small number of experimental applications, but a massive amount of intelligent assistants, AI agents, and AI workflows targeting different roles and processes. This poses new requirements for software production methods.
In the past, software development processes usually revolved around requirements analysis, architecture design, coding and testing, and deployment and operation. In the AI era, application requirements change faster, and business scenarios are more fragmented, posing efficiency challenges to traditional development models. Therefore, AI Coding has become a crucial direction of attention in the industry.
For developers, AI-assisted code generation can significantly improve coding efficiency. However, for large enterprises, code generation is only the most visible part of the value of AI Coding. What truly determines whether AI Coding can enter the enterprise production environment is the engineering issues hidden beyond the code.
For individual developers, the most intuitive value of AI Coding is improving code writing efficiency. But upon entering enterprise scenarios, writing logical code is only the tip of the iceberg. Beneath the surface, enterprises need to solve more complex issues:
First, architectural consistency.
Enterprise software has formed a complete technical architecture, development standards, and governance system over years of construction. Even if AI-generated code can run, it may not comply with the enterprise's existing architectural requirements, increasing subsequent maintenance costs. Therefore, what enterprises need is not just an AI that can simply write code, but an AI that understands enterprise architectural constraints.
Second, asset reuse.
After years of digital construction, large enterprises have accumulated a massive amount of business models, process components, interface services, and software assets. If AI starts from scratch every time it generates code, it cannot leverage the value of the enterprise's existing digital assets. Enterprise-level AI development requires enabling AI to understand and reuse existing software assets rather than recreating them.
Third, compliance control.
In individual development scenarios, code efficiency and function implementation may be the primary goals. However, for industries such as finance, energy, and government enterprises, software systems need to meet strict security, audit, and management requirements. Enterprises need to ensure that the AI generation process is traceable, code changes are manageable, and system operations are auditable.
The Third Mountain: The System Connection Mountain — AI Needs to Enter Business Processes
Beyond data and development capabilities, the implementation of enterprise AI faces another critical issue: how can AI truly enter the business?
Currently, most enterprises already possess a massive number of digital systems, including ERP, CRM, production systems, data platforms, and various industry applications. These systems carry the core business processes of the enterprise and are also crucial scenarios for AI to create value. If AI can only stay in chat windows, it is hard for it to truly change the enterprise's operational methods; it needs to possess stronger business connection capabilities:
Sales AI agents need to connect with customer systems and order systems to assist in sales forecasting and customer operations;
Production AI agents need to connect with manufacturing systems and supply chain systems to participate in production scheduling and process optimization;
Operations AI agents need to connect with data platforms and business applications to help enterprises improve decision-making efficiency.
This means that enterprises need to establish a new type of infrastructure connecting AI capabilities with business systems.
On the one hand, it is necessary to break down the long-standing information silos within the enterprise through capabilities such as API management, system integration, and intelligent gateways; on the other hand, it is necessary to uniformly manage model invocations, AI agent operations, and data flows to ensure that AI applications are secure, stable, and controllable.
03. Primeton Information Takes the Lead in Building the AI-Native Digital Infrastructure and Deploys Three Types of Core Capabilities
From domestic basic software to SOA architecture, and then to cloud application platforms, big data, and data governance, Primeton has long accumulated capabilities around core enterprise software. These technological precipitations have become an important foundation for its exploration of digital infrastructure in the AI era.
In the AI era, the digital infrastructure needs to take on a new mission. It not only needs to support the operation of business systems but also needs to support AI capabilities entering the enterprise. This is precisely the background against which Primeton proposed the "AI-Native Digital Infrastructure."
Based on observations of the challenges in enterprise AI implementation, Primeton Information has focused the construction of the AI-Native Digital Infrastructure on three directions: intelligent data governance, AI application development, and comprehensive integration.
Intelligent Data Governance: Making Data an AI-Usable Asset
For enterprises, data value does not depend on quantity, but on whether it can be effectively utilized by AI.
Primeton Information defines this direction as "Yi Shu Zhi" (Easy Data Governance).
The core objective is to help enterprises transform scattered data resources into data assets that AI can understand and use.
Centered on this goal, Primeton helps enterprises build a unified data governance system through capabilities such as master data management, data standard governance, data quality management, and data asset management. At the same time, combined with large model capabilities, it promotes the intelligence of data generation, cleaning, enhancement, and labeling processes, improving the efficiency of building high-quality enterprise datasets. During WAIC, Li Shuchao, Dean of the Primeton Data Intelligence Research Institute, demonstrated relevant practices, exploring a human-machine collaborative data governance model by assisting the data engineering process with large models.
At the product level, Primeton YiShu is a new-generation data hub built precisely in this context. Through data governance, data asset management, and intelligent integration capabilities, it helps enterprises unblock the data value chain, transforming data from accumulated resources into business innovation capabilities.
AI Application Development: Driving the Upgrade of Enterprise Software Production Systems
Given the rapid growth in AI application demand, enterprises need a new software production method.
Primeton Information defines this direction as "Yi Kai Zhuan" (AI-Native Application Development), which refers to enterprise-oriented AI-native application development capabilities.
Unlike traditional AI Coding tools that primarily focus on code generation, enterprise-level AI development places more emphasis on the completeness of the software engineering system.
Primeton believes that AI should deeply penetrate the software lifecycle, achieving full-process collaboration from requirements analysis, architecture design, and code development to testing and operations. By involving AI agents in the software R&D process and combining them with the enterprise's existing architectural standards, software assets, and engineering governance system, enterprises can further improve software construction efficiency while ensuring the development process meets enterprise requirements.
This means that AI's impact on the software industry is not just about adding a development tool, but driving a change in the entire software production method. In the future, the role of software developers will also gradually change. Developers will no longer just write code, but manage the software production process through AI, integrating AI capabilities with the enterprise engineering system.
At the product level, Primeton YiKai follows the concept of an "Enterprise-Level Intelligent Software Factory," driving enterprises to form a more intelligent software production system by involving AI agents in the entire software development process.
Comprehensive Integration: Enabling AI to Enter Enterprise Business Processes
If AI cannot connect with enterprise systems and can only stay at the level of information query and simple interaction, it is hard to change the enterprise's operational methods. Therefore, enterprises need to establish a new type of integration system connecting data, applications, and AI capabilities.
Primeton Information defines this direction as "Ji Lian Tong" (Instant Connection).
Its core objective is to achieve unified connection between existing enterprise systems and AI capabilities through comprehensive integration capabilities.
Specifically, this includes three aspects:
First, achieving the interconnection of business systems. The massive number of systems formed through long-term enterprise construction need to achieve data flow and business collaboration through integration capabilities.
Second, achieving unified governance of interfaces and services. As enterprises deploy more and more AI applications, the importance of API management and service reuse is steadily improving.
Finally, achieving secure access to AI capabilities. Enterprises need to uniformly manage large model invocations, AI agent operations, and data access to ensure the secure and stable operation of AI applications.
At the product level, Primeton JiLian is precisely Primeton Information's exploration in this direction. Through capabilities such as system interconnection, API governance, and AI gateways, it helps enterprises connect applications, data, and AI capabilities, enabling intelligence to truly enter business processes.
04. Conclusion: AI Competition Enters the Era of Infrastructure
The AI-Native Digital Infrastructure is becoming a crucial infrastructure for the intelligent transformation of enterprises. It connects the accumulation of digital construction over the past years and also carries the possibilities for future intelligent development.
What WAIC showcases is not only the latest advancements in AI technology but also the next stage direction of industrial development: enabling data to better serve intelligence, enabling applications to respond to business more quickly, and enabling AI to truly enter the enterprise production system.