[论文] Pilot Early, Commit Late: A Real-Options Model of Enterprise AI Adopti...

论文概要

研究领域: ML 作者: Gaurav Tewari 发布时间: 2026-09-14 arXiv: 2609.15919

中文摘要

人工智能给企业带来了一个不同寻常的时机问题。技术前沿在快速改进，实施部分不可逆，组织特定能力通过行动积累。本文构建了一个在不确定性下AI部署的两期决策模型，企业在立即部署、有限试点和等待之间选择。部署获得当前运营价值，但使企业面临架构过时风险；等待保留了在前沿被观测后采用的期权；试点牺牲当前运营价值以在不完全承诺的情况下建立组织特定学习。模型产生五个中心时机结果和一个关于学习发生位置的比较结果。第一，前沿不确定性的均值保留增加提高了等待和试点的价值，但在其收益对前沿仿射时，对立即部署无影响。第二，当部署架构仅捕获未来改进的有限份额时，更快的前沿进展预期可能降低立即部署的相对吸引力。第三，当试点所建能力的期望值超过其成本时，试点严格优于等待。第四，足够有价值的组织特定学习创造了一个"早试点、晚承诺"最优的非空区域。第五，存在一个闭式模块化阈值，超过它立即部署支配最优外部选项。第六，生产学习和试点特定学习对时机边际的影响不同。连续时间扩展恢复了标准结果：不确定性提高采用阈值，而能力和模块化降低它。本文区分了部署、实验和等待，展示了为什么快速进展可以合理地增加实验而不证明不可逆承诺的合理性。

原文摘要

Artificial intelligence presents firms with an unusual timing problem. The technology frontier is improving rapidly, implementation is partly irreversible, and organization-specific capabilities are accumulated through action. This paper develops a two-period decision model of AI deployment under uncertainty in which a firm chooses among immediate deployment, a limited pilot, and waiting. Deployment earns current operating value but exposes the firm to architectural obsolescence; waiting preserves the option to adopt after the frontier is observed; a pilot sacrifices current operating value to build organization-specific learning without full commitment. The model yields five central timing results and a sixth comparative result on where learning occurs. First, a mean-preserving increase i...

#论文 #arXiv #ML #小凯