Purpose of the thesis This study aims to propose an AI maturity model in order to be able to evaluate to whatextent large-scale manufacturing companies utilize AI in their inbound logistical operationsand also to reveal the challenges related to the implementation of AI.  Methodology This study employs a qualitative research method with the use of a survey conducted on 10companies within 5 industries. Additionally, semi-structured interviews were employed attwo out of these 10 companies. Theoretical framework The theoretical framework provides a deeper theoretical understanding of inbound logisticsoperations, AI implementations and their suggested operationalization in inbound logisticsoperations at large-scale manufacturers. Further, it also provides theory of the challengesassociated with implementing AI in inbound logistics operations. Empirical data The empirical section illustrates the data that were collected through survey and interviewstudy. First, data is presented based on the respondents' various industries that are grouped.Within each grouped industry, data is structured according to the various RQ’s and proposedframeworks, meaning that data will be related to the different levels of AI implementation,maturity levels as well as challenges. Analysis and discussion In this section, the industries are analysed and discussed with the help of the theoreticalframework and collected empirical data. This was first established with an individual analysisof industries where one research question is covered at a time. After this is done, across-analysis is carried out on the involved industries in relation to the research questions. Conclusion This chapter gives a concluding discussion and presents theoretical and practicalcontributions. The main results of the three research questions are highlighted. Finally, further research opportunities and limitations are discussed.