| Summary: | Decisions have been automated since the early days. Ever since the rise of AI, ML and DataAnalytics, algorithmic decision-making has experienced a boom time. Nowadays, using AI withina company is said to be critical to the success of a company. Considering the point that it can bequite costly to develop AI/ ML and integrating it into decision-making, it is striking how littleresearch was put into the identification and analysis of its cost drivers by now. This thesis is acontribution to raise and the awareness of possible cost drivers to algorithmic decisions. Thetopic was divided in two subgroups. That is solely algorithms and hybrid decision-making. Asystematic literature review was conducted to create a theoretical base for further research. Thecost drivers for algorithms to make decisions without human interaction, the identified costdrivers identified can be found at Data Storage (including initial, floor rent, energy, service,disposal, and environmental costs), Data Processing, Transferring and Migrating. Additionally,social costs and the ones related to fairness as well as the ones related to algorithms themselves(Implementation and Design, Execution and Maintenance) could be found. Business Intelligenceused for decision making raises costs in Data quality, Update delays of cloud systems, Personneland Personnel training, Hardware, Software, Maintenance and Data Storage. Moreover, it isimportant to say that the recurrence of some costs was detected. Further research should go inthe direction of applicability of the theoretical costs in practice.
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