Intralogistics is a part of organization’s logistics and supply chain management, which are central functions of modern organizations. Effective management of intralogistics helps an organization to gain competitive advantage over its competitors. Automation systems which replace human manual labor with machines are one way to make intralogistics functions more efficient. The rapid advancements of different artificial intelligence technologies and their successful applications in various domains has piqued the interest, whether artificial intelligence could also be used to make automation systems more efficient in the field of intralogistics. The purpose of this thesis is to research the aforementioned topic, and the research question is to figure out how artificial intelligence could be used in controlling intralogistics automation systems. The thesis is done as a commission for a Finnish company which makes intralogistics automation systems. Research method applied was Design Science Research Methodology process. The resulting artifact assesses the feasibility of genetic algorithms, one of the technologies included in the field of artificial intelligence, to solve the warehouse product assignment problem in a simulated portal robot warehouse. The created artifact proves the potential of genetic algorithms in a real world automation systems but using them in a production environment would still require making the simulation more accurate. In addition, the results of the thesis show that the use of artificial intelligence has mainly be researched in automation systems using high bay warehouses or self-driving AGVs. These studies use multi-agent systems, genetic algorithms and reinforcement learning among other technologies. All in all the topic has not received much research interest. As far as is known, the thesis offers the first literature review where the application of artificial intelligence is examined broadly in the domain of intralogistics automation systems.