Global corporate AI investment reached $252.3 billion in 2024, yet only 6% of firms report significant earnings impact. This article argues that AI project failure is fundamentally an organizational learning problem rather than a technology deficit. Drawing on a systematic synthesis of 19 large-scale industry and academic sources, including surveys of nearly 10,000 organizational leaders, we identify two categories of failure: organizational (culture, leadership alignment, governance, and human-AI learning deficits) and technical (semantic bottlenecks and output management challenges). We introduce the Siloed-Integrated-Orchestrated (SIO) progression model, which maps enterprise AI capability across five pillars -- Culture & Leadership, Human Capital & Operations, Data Architecture, Systems Infrastructure, and Governance & Regulatory Compliance -- and provides prescriptive guidance for advancing between stages. The implications challenge organizations to reframe AI investment as capability development rather than technology procurement.

翻译：2024年全球企业人工智能投资额达2523亿美元，但仅6%的企业报告显著收益影响。本文认为，人工智能项目的失败本质上是组织学习问题而非技术缺陷。通过对19项大规模行业与学术资料（涵盖近万名组织领导者调查）的系统性整合，我们识别出两类失败原因：组织层面（文化、领导力协同、治理与人机学习缺陷）与技术层面（语义瓶颈与输出管理挑战）。我们提出"孤岛-整合-编排"（SIO）演进模型，该模型从五大支柱——文化与领导力、人力资本与运营、数据架构、系统基础设施、治理与法规合规——对企业人工智能能力进行映射，并提供阶段间推进的规范性指导。其启示要求组织将人工智能投资重新定义为能力发展而非技术采购。