## 4 Results  
The reported outcomes of the literature review are presented in the form of an analysis of the 60 selected primary studies (marked as [62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 101; 102; 103; 104; 106; 107; 108; 109; 110; 111; 113; 114; 115; 116; 117; 118; 119; 120; 121] in the References section) based on the identified research questions. Each research question is discussed in a separate section. **RQ. 1**_What AI capabilities are utilized by AI researchers?_  
The aim of this question is to categorize the studies based on the main type of AI capability that was investigated in the study. Since it is possible to combine a variety of capabilities in a single AI system or toolbox, such studies were categorized by singling out the main capability used in the research. Figure 2 shows the distribution of AI capabilities utilized in the examined studies. Evidently, research efforts are focused on exploring intelligent process automation and machine learning capabilities. A rise in interest in natural language processing and predictive analytics can be noted with respect to the outcomes from previous literature reviews [4], which showed NLP as a relatively unexplored field and did not consider predictive analytics as a large enough field to explore outside of machine learning. **RQ. 2**_What types of platforms are utilized by AI researchers?_  
The aim of this question was to identify the types of platforms used by AI researchers for their applications. The classification focuses on isolating whether the application works on a cloud-based platform (SAP Core AI/Conversational AI, Oracle AI services, etc. ), an on-premises server, or edge devices, where the data are generated (smartphones, tablets, computers, etc.). The studies which present hybrid architectures are counted in a separate category. Figure 3 shows that the majority of the examined studies focused on cloud-based or on-premises applications, while only two studies investigated edge device AI applications and one study focused on hybrid architectures. A potential future research direction would be to focus on developing edge AI applications and investigate possibilities for federated learning and heterogenous data silo structures. Edge AI applications allow for greater data privacy and security since the use of federated learning means that the training of a smaller AI model is performed locally on an edge device, where the data are generated, and only aggregated data outcomes are presented for training to the large model based on the cloud. Such implementations also mitigate concerns regarding data accessibility in cases when the cloud service becomes temporarily unavailable. Figure 2: Distribution of primary studies by AI capability. The choice of platform for AI integration can have a significant effect on data privacy and security and thus can be used to help organizations comply with the increasing number of data regulation and protection laws and policies implemented by the European Union (EU). One such instance is the General Data Protection Regulation (GDPR), which came into effect for all EU and EEA (European Economic Area) member countries on 25 May 2018. The GDPR requires organizations to implement appropriate technical and organizational measures to ensure the security of personal data generated on edge devices. AI can support GDPR compliance by automating data protection tasks and identifying potential data breaches in real time. Some considerations regarding the impact of AI on data privacy and protection have been summarized in [122]. **RQ. 3**_What kind of contributions are made by studies of AI in MIS?_  
The aim of this research question is to identify and categorize the contributions of the primary studies and thus single out categories in which academic discourse on AI in MIS should be encouraged.