Algorithmic decision support (ADS), using Machine-Learning-based AI, is becoming a major part of many processes. Organizations introduce ADS to improve decision-making and use available data, thereby possibly limiting deviations from the normative "homo economicus" and the biases that characterize human decision-making. However, a closer look at the development and use of ADS systems in organizational settings reveals that they necessarily involve a series of largely unspecified human decisions. They begin with deliberations for which decisions to use ADS, continue with choices while developing and deploying the ADS, and end with decisions on how to use the ADS output in an organization's operations. The paper presents an overview of these decisions and some relevant behavioral phenomena. It points out directions for further research, which is essential for correctly assessing the processes and their vulnerabilities. Understanding these behavioral aspects is important for successfully implementing ADS in organizations.