Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms and explore the organizational requirements of implementing AI. The resulting topology of generative mechanisms contributes to the research on AI management by offering rich insights into the cause-effect relationships that shape the implementation process. Moreover, it demonstrates how causal loop diagraming can improve the modeling and analysis of generative mechanisms.

中文翻译：

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人工智能实施的生成机制：预测性维护的批判现实主义视角

人工智能 (AI) 带来了创造和利用商业价值的各种新机会。然而，许多组织——尤其是传统行业的组织——难以抓住这些机会。为了揭示根本原因，我们研究了更多传统行业如何实施预测性维护，这是人工智能在制造组织中的一个有前景的应用。在我们的分析中，我们采用了多案例设计，并采用批判现实主义的视角来确定人工智能实施的生成机制。总的来说，我们发现了五个相互依赖的机制：实验；知识构建和整合；数据; 焦虑; 和灵感。使用因果循环图，我们充实了这些机制的社会技术动态，并探索了实施人工智能的组织要求。由此产生的生成机制拓扑通过提供对塑造实施过程的因果关系的丰富见解，有助于人工智能管理的研究。此外，它还演示了因果循环图如何改进生成机制的建模和分析。