| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28811 |
| 来源ID | Working Paper 28811 |
| AI Adoption and System-Wide Change | |
| Ajay K. Agrawal; Joshua S. Gans; Avi Goldfarb | |
| 发表日期 | 2021-05-17 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | Analyses of AI adoption focus on its adoption at the individual task level. What has received significantly less attention is how AI adoption is shaped by the fact that organisations are composed of many interacting tasks. AI adoption may, therefore, require system-wide change which is both a constraint and an opportunity. We provide the first formal analysis where multiple tasks may be part of a modular or non-modular system. We find that reliance on AI, a prediction tool, increases decision variation which, in turn, raises challenges if decisions across the organisation interact. Modularity, which leads to task independence rather than system-level inter-dependencies, softens that impact. Thus, modularity can facilitate AI adoption. However, it does this at the expense of synergies. By contrast, when there are mechanisms for inter-decision coordination, AI adoption is enhanced when there is a non-modular environment. Consequently, we show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organisational system. |
| 主题 | Other ; Accounting, Marketing, and Personnel ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w28811 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586483 |
| 推荐引用方式 GB/T 7714 | Ajay K. Agrawal,Joshua S. Gans,Avi Goldfarb. AI Adoption and System-Wide Change. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28811.pdf（266KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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