为中型与大型企业量身打造生成式AI落地框架，解决资源与组织双重难题。
A Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises (FAIGMOE)
- 分四阶段整合战略评估、规划、实施与优化，适配不同规模组织。
- 涵盖提示工程、幻觉管理等生成式AI特有治理要点。
- 提供可操作指南与评估工具，适合企业数字化转型决策者使用。
生成式人工智能（GenAI）为组织带来变革性机遇，但中型企业和大型企业面临不同的采纳挑战：前者受限于资源与AI人才，后者则受制于组织复杂性与协调困难。现有技术采纳框架（如TAM、TOE、DOI）缺乏针对GenAI实施的针对性，形成关键文献空白。本文提出FAIGMOE（面向中型与大型企业生成式AI采纳与集成的框架），融合技术采纳理论、组织变革管理与创新扩散视角，构建四个相互关联的阶段：战略评估、规划与用例开发、实施与集成、运营化与优化。每个阶段提供可扩展的指导，涵盖准备度评估、战略对齐、风险治理、技术架构与变革管理，适应不同组织规模与复杂度。框架融入提示工程、模型编排、幻觉管理等生成式AI特有考量，区别于通用框架。作为概念性贡献，FAIGMOE是首个明确覆盖中型企业与大型企业生成式AI采纳的综合性框架，提供可执行的实施协议、评估工具与治理模板，有待未来研究通过实证验证。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (GenAI) presents transformative opportunities for organizations, yet both midsize organizations and larger enterprises face distinctive adoption challenges. Midsize organizations encounter resource constraints and limited AI expertise, while enterprises struggle with organizational complexity and coordination challenges. Existing technology adoption frameworks, including TAM (Technology Acceptance Model), TOE (Technology Organization Environment), and DOI (Diffusion of Innovations) theory, lack the specificity required for GenAI implementation across these diverse contexts, creating a critical gap in adoption literature. This paper introduces FAIGMOE (Framework for the Adoption and Integration of Generative AI in Midsize Organizations and Enterprises), a conceptual framework addressing the unique needs of both organizational types. FAIGMOE synthesizes technology adoption theory, organizational change management, and innovation diffusion perspectives into four interconnected phases: Strategic Assessment, Planning and Use Case Development, Implementation and Integration, and Operationalization and Optimization. Each phase provides scalable guidance on readiness assessment, strategic alignment, risk governance, technical architecture, and change management adaptable to organizational scale and complexity. The framework incorporates GenAI specific considerations including prompt engineering, model orchestration, and hallucination management that distinguish it from generic technology adoption frameworks. As a perspective contribution, FAIGMOE provides the first comprehensive conceptual framework explicitly addressing GenAI adoption across midsize and enterprise organizations, offering actionable implementation protocols, assessment instruments, and governance templates requiring empirical validation through future research.
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