How do configurations of humans and algorithms evolve as firms adopt artificial intelligence (AI) capabilities, and what are the implications for work and organization? We explored these questions through a two-year long case study of an organization in the international maritime trade that introduced automated algorithmic support for data analysis and prediction work. Drawing on a human–machine configuration perspective, we found that humans and the algorithm were configured and reconfigured in multiple ways over time as the organization dealt with the introduction of algorithmic analysis. In contrast to replacing human work, the emergent configurations required new roles and redistribution of extant expertise to augment and improve the accuracy of the algorithm. Our analysis suggests that the new configuration resembled a human-in-the-loop pattern, comprised of both the augmentation work of auditing (i.e. the generation of a ground truth and assessment of the algorithmic output against this) as well as the work of altering the algorithm and the data acquisition architecture. Our research points to the strategic importance of a human-in-the-loop pattern for organizational reflexivity to ensure that the performance of the algorithm meets the organization’s requirements and changes in the environment.

随着公司采用人工智能（AI）功能，人员和算法的配置将如何演变，这对工作和组织有何影响？我们通过对一个国际海事贸易组织的为期两年的案例研究探讨了这些问题，该组织为数据分析和预测工作引入了自动算法支持。从人机配置的角度出发，我们发现随着组织处理算法分析的引入，随着时间的流逝，人们和算法以多种方式进行了配置和重新配置。与取代人工相比，紧急配置需要新的角色并重新分配现有的专业知识，以增强和提高算法的准确性。审核（即生成基本事实并据此评估算法输出），以及更改算法和数据采集体系结构的工作。我们的研究指出了人为循环模式对于组织反思的战略重要性，以确保算法的性能满足组织的要求和环境的变化。