AI失败主因是组织学习能力不足，而非技术不够。
Why AI Readiness Is an Organizational Learning Problem, Not a Technology Purchase
- 提出SIO模型，系统评估企业AI能力五个维度。
- 仅6%企业获显著收益，主因在文化与治理缺陷。
- 适合想提升AI落地效果的管理者和决策者。
2024年全球企业AI投资达2523亿美元，但仅有6%的企业报告了显著的收益影响。本文指出，AI项目失败本质上是组织学习问题，而非技术短板。基于对19个大型产业与学术来源的系统性综述，包括近万名组织领导者调查，识别出两类失败：组织性（文化、领导力对齐、治理及人机学习缺口）和技术性（语义瓶颈与输出管理挑战）。提出Siloed-Integrated-Orchestrated（SIO）演进模型，涵盖文化与领导力、人力资本与运营、数据架构、系统基础设施、治理与合规五大支柱，提供各阶段进阶的可操作指引。研究呼吁企业将AI投资重新定位为能力建设，而非单纯的技术采购。
原文摘要 · Abstract (English)
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.
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