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Science & Technology Management and Innovation Management

Towards AI-native: Maturity Assessment Framework and Strate⁃ gic Priorities for AI-native Enterprises 

Yin Ximing1, 2 , Zhang Jihan2 , Jin Jun3 , Chen Tailun3 

(1.Department of Management Engineering, Beijing Institute of Technology, Beijing 100081, China; 2.School of  Global Governance, Beijing Institute of Technology, Beijing 100081, China; 3.School of Management, Zhejiang  University, Hangzhou 310058, China)

Abstract: With the elevation of the "AI+" initiative to a national strategy, enterprise intelli⁃ gence is undergoing a fundamental transition from tool-based adoption toward native reconstruc⁃ tion, giving rise to a new organizational form: the AI-native enterprise. Unlike traditional firms  that treat artificial intelligence as an auxiliary technology, AI-native enterprises embed AI  deeply into their organizational architecture, operational processes, and value-creation logic, achieving a paradigm shift from linear " +AI" enhancement to exponential "AI× " growth. This  study aims to clarify the conceptual connotation of AI-native enterprises, develop a systematic  maturity assessment framework, and identify strategic priorities for enterprise evolution toward  AI nativeness. Methodologically, the study integrates theories of technological innovation, organi⁃ zational management, and context-driven innovation to construct a Technology—Organization— Context (TOC) maturity assessment framework. Based on this tripartite perspective, the paper  proposes an AI-native maturity ladder consisting of five levels (L0—L4), ranging from tradi⁃ tional enterprises to fully AI-native enterprises. The framework emphasizes the dynamic interac⁃ tion among technological capabilities, organizational structures, and scenario development, con⁃ ceptualized as a self-reinforcing "intelligence flywheel" that drives continuous learning, adapta⁃ tion, and value creation. The analysis reveals that AI-native enterprises exhibit "born-with-AI"  characteristics, where AI functions as organizational DNA rather than a supplementary tool. At  lower maturity levels, enterprises rely on fragmented AI tools and hierarchical governance, with  limited scenario innovation. As maturity increases, firms progressively establish unified data in⁃ frastructures, human—AI collaborative decision-making mechanisms, and context-driven value  creation models. Fully AI-native enterprises achieve autonomous evolution, liquid organiza⁃ tional forms, and ecosystem-based value co-creation, enabling AI to define products, processes, and business models endogenously. The study further identifies key strategic priorities for AInative evolution. Enterprises should avoid "AI-for-AI's-sake" approaches and instead adopt a  value-oriented path characterized by scenario anchoring, system-level reconstruction, and hu⁃ man—AI symbiosis. Importantly, progress across the TOC dimensions need not be synchronous; firms should leverage their unique resource endowments to identify high-impact entry points  while maintaining dynamic balance among technology, organization, and context. The research  contributes theoretically by advancing a structured maturity model for AI-native enterprises and  practically by offering actionable guidance for firms navigating intelligent transformation. It un⁃ derscores that AI-native evolution is not merely a technological upgrade but a profound organiza⁃ tional metamorphosis essential for sustainable competitiveness in the intelligent economy era. quality productivity 

Key words: AI-native enterprise ; Technology—Organization ; context-driven innovation —Context framework ; Human-AI symbiosis; new