CAICT, AWS and SalesEasy Release Report Defining AI-Native CRM Standards

On Oct 12, 2026, CRM vendor SalesEasy, together with the Cloud Computing and Digital Research Institute of the China Academy of Information and Communications Technology (CAICT) and Amazon Web Services (AWS), released the AI-Native CRM Industry Development Research Report (2026). The report covers the development basis, core concepts, evaluation framework, technical architecture, core capabilities, open ecosystem, application scenarios and evolution trends of AI-native CRM, and includes case studies from Meinian Health and Shanghai Sansi.

The report draws a distinction between AI-native CRM and the common "AI+CRM" approach. It says traditional AI+CRM often bolts intelligent recommendations or summarization onto existing CRM systems, which does not resolve underlying problems such as fragmented data semantics or the inability to execute tasks autonomously. AI-native CRM, by contrast, is designed with AI as the starting point, rebuilding data models, business processes, interaction modes and execution systems, and natively supporting business perception, semantic understanding, assisted decision-making, controlled autonomous execution and continuous optimization.

The report defines AI-native CRM as a new generation of CRM systems built on a dual foundation of "business semantic ontology and Agent runtime". It proposes six golden standards, including AI rooted at the architecture level rather than API calls, business semantic understanding rather than field-level form reading, autonomous Agent loops, AI-ready data governance, AI-native interaction, and open cross-Agent ecosystems. It also outlines a six-layer technical architecture (PaaS, data, business semantics, execution, interaction and ecosystem), eight core capabilities (five foundational and three advanced), four application scenarios (prospecting, faster deal closing, sales forecasting and management, and sales enablement and coaching), and five evolution trends.

The report argues that clarifying AI-native CRM as a systematic reconstruction rather than simple feature stacking can help enterprises identify genuine AI-native capabilities, and provides a common framework for procurement, vendor benchmarking and ecosystem integration. It also notes that business models may shift from traditional per-seat fees toward task-based, capability-call or attributable-outcome-based pricing.