In 2026, China's ERP market surpassed 48.7 billion yuan, a year-on-year increase of 16.8%. The market share of domestic vendors jumped from 56% in 2024 to 68.4%, the cloud-native penetration rate exceeded 50% for the first time, and the proportion of products with deep AI integration surpassed 65%. However, the real turning point was the first-ever establishment of a national evaluation benchmark for the effectiveness of AI agents.
Market Landscape: How Local Manufacturers Captured Market Share
Data speaks:
¥48.7 billion domestic ERP market size (16.8% year-on-year growth) 68.4% domestic vendor market share (↑12.2 percentage points) 65%+ AI deep integration product share 51.2% cloud-native ERP penetration rate (exceeding 50% for the first time) 72% enterprise AI application coverage rate (Q1 2026) 90%+ mainstream vendors completed cloud-native architecture upgrade
The surge in local manufacturers' market share is driven by three forces simultaneously:
Domestic substitution policy is the greatest political correctness. In recent years, the information technology application innovation industry has expanded from party and government agencies to key industries, and ERP, as the core system of enterprises, bears the brunt. Using domestic ERP is not only a business decision but also a compliance requirement.
Rapid catch-up in AI capabilities. Kingdee, Yonyou, and Digiwin have not been dabbling in AI investments over the past two years; AI assistants, intelligent analytics, and predictive models have been rolled out one after another. The domestic market share of the ERP segment in manufacturing has already exceeded 75%.
Cloud-native architecture is fully in place. Over 90% of mainstream vendors have completed cloud-native architecture upgrades, and the operational costs and deployment efficiency of cloud-based ERP are on an entirely different scale compared to a few years ago.
⚠️ The data source is a third-party industry monitoring report, with some data provided by manufacturers. There is a certain risk of deviation, and it is for trend reference only.
What AI Has Actually Brought to ERP: 28% Cost Reduction, 40% Efficiency Improvement
The numbers are straightforward: AI helps enterprises reduce costs by 28% and increase efficiency by 40%. But behind these numbers lie different interpretations from different vendors.
Some have turned AI into a conversational assistant—you ask for data in natural language within the ERP, and it returns reports for you. This is an improvement at the experience level, useful, but not a qualitative change.
Some embed AI into business processes — automatically conducting supplier risk assessments, intelligently generating procurement suggestions, and dynamically replenishing inventory based on historical data. This is what true deep integration means.
🔮Intelligent Prediction Layer
Sales forecasting, cash flow forecasting, demand forecasting—generating dynamic models from historical data and automatically correcting deviations.
⚡Process Automation Layer
Approval flow intelligent routing, automatic anomaly tagging, cross-module data consistency checks, reducing manual intervention points.
🛡️Risk Control Layer
Real-time updates of supplier ratings, pre-identification of contract risks, and early warnings of compliance anomalies.
Over 85% of enterprises rank "industry adaptability" as the top consideration in ERP selection. This means that ERP's AI capabilities must be tied to industry know-how — generic AI wrapper solutions are losing competitiveness.
National AI Agent Evaluation Standards: The Beginning of Industry Reshuffling
If market data describes the current situation, a seminar on May 21, 2026, will indicate what will happen next.
On this day, the "Enterprise-Level AI Agent Application Effectiveness Evaluation Specification," managed under the guidance of the China Electronic Chamber of Commerce and organized by the Zhihe Standards Center, entered the final review stage. This standard has the joint endorsement of three departments—the Cyberspace Administration of China, the National Development and Reform Commission, and the Ministry of Industry and Information Technology.
This is the first national group standard focusing on the effectiveness evaluation of AI agents. It answers not just "Is the AI easy to use?" but "Is the AI worth the money?"
Enterprise AI deployment is trapped in the dilemma of "daring not to use, unable to evaluate, and difficult to scale" — with the release of a new standard, AI agent effectiveness assessment has been incorporated into top-level regulations at the national level for the first time.
The four core dimensions of the standard:
📐 Value Quantifiable
Task success rate, processing time reduction ratio, cost reduction rate, ROI—all become quantifiable hard metrics, rather than "it feels effective."
🏭 Scenarios are evidence-based
The appendix covers seven major industries including intelligent customer service, industrial manufacturing, financial services, and legal compliance, each with a specific list of evaluation elements.
🔒 Clear Security Boundaries
What data can intelligent agents access and when human intervention is needed — transforming from vague principles into testable evaluation criteria.
🔄 Full Lifecycle Closed Loop
Offline evaluation → grayscale testing → adversarial testing → operational monitoring → bias improvement, forming a complete evaluation closed loop throughout the pre- and post-launch process.
What does this mean? With a unified benchmark, the strengths and weaknesses of ERP vendors' AI agent capabilities become clear at a glance—projects relying on PPTs and demo presentations will face substantive performance evaluations.
Selection logic is being rewritten: AI is not a module, but the new core of ERP
In the past, when selecting an ERP, companies focused on a few key issues: whether the functionality was comprehensive, whether it was suitable for the industry, whether the implementation team was reliable, and whether the cost of secondary development was high.
The selection logic for 2026 is adding a new dimension: the native depth of AI capabilities — not an external AI assistant, but whether AI truly drives the core business processes.
The proportion of contract value from full-chain integrated solutions has reached 62%, up 25 percentage points year-on-year. This indicates that enterprises are increasingly unwilling to buy a bunch of scattered components and integrate them themselves, but instead hope that ERP vendors will package AI capabilities directly for use.
For local ERP vendors, this is both an opportunity and a pressure. The opportunity lies in the fact that the speed of catching up on AI capabilities is fast enough, and with localization and policy advantages, the window for market share expansion is still open. The pressure lies in the fact that the AI foundations and globalization experience of international vendors like SAP and Oracle are not easily caught up with—once their AI capabilities are localized, the competitive landscape will be reshuffled again.
Advice for ERP Selectors
Based on market data and national standard trends, a few practical suggestions:
First, when evaluating AI capabilities, don't look at demos; look at ROI data. Require vendors to provide quantified benefits from similar clients—how much processing time was reduced, how much costs were lowered, and whether there are verifiable ROI figures.
Second, prioritize industry depth over functional breadth. General AI features are now available from various providers, but only those vendors that truly understand your industry logic and embed your business rules into the AI decision-making chain are worthy of long-term cooperation.
Third, incorporate data security into the AI evaluation framework. National standards have clear requirements for security boundaries. During the selection process, ask clearly about "which data AI has accessed" and "who has the authority to intervene in AI decision-making."
Fourth, focus on the implementation team rather than just the product.The success or failure of AI ERP depends 50% on the product and 50% on implementation—especially during the stages of industry adaptation and business process reengineering. Finding an implementation team with deep industry experience is more important than choosing a product with stronger features.
