## 5 Discussion  
### Summary of Findings  
In the framework of the conducted systematic literature review, the authors identified, classified, and analyzed 3946 studies on the subject of adopting AI in management information systems that were published between 2006 and March 2023. Of these, 60 were identified as primary studies after an exhaustive sorting and filtering process. The presented systematic literature review aims at filling a knowledge gap created by previous literature reviews in the field of artificial intelligence adoption in management information systems. While previous systematic literature reviews have focused on specific aspects of the influence of AI on organizational-level automation and information systems, such as the impact of AI on organizational strategy [3], and studied individual user interactions with AI tools [52], this review focuses on creating a contemporary overview of research contributions in the field of AI adoption in MIS and their practical implications. This review grouped the selected primary studies based on the AI capabilities identified in the researchers' own definitions of AI. This sorting procedure was implemented due to a concerning trend of a lack of cohesiveness in the definition and scope of artificial intelligence functionalities used in the research community, which was also identified by a previous systematic literature review [4]. While the majority of the research is focused on the application of AI for intelligent process automation, there is a notable increase in the number of studies focused on predictive analytics and natural language processing. A previous literature review on AI in information systems research [4] reported that NLP was a relatively unexplored field and did not consider predictive analytics as a large enough field to explore outside of machine learning; however, in recent years, the focus of research has evidently shifted toward providing business insight based on predictive analytics and developing NLP models for human-AI interaction. While most AI implementations are facilitated by using cloud or on-premises data centers for the necessary training and data aggregation, an exciting new research trend has  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source of Business Value** & **Benefits** & **Challenges** & **Primary Sources** \\ \hline \multirow{8}{*}{Analytical Insight} & Deep learning and machine & \multirow{4}{*}{Some AI technologies rely on human experts to establish a working hypothesis and to identify relevant features, but the fear of job elimination can drive human operators to be unwilling to share knowledge and provide expertise for AI model creation.} \\  & learning techniques can extract patterns from large volumes of generated data at a speed that a human being cannot achieve. & & [85; 97], features, but the fear of job elimination can drive human operators to be unwilling to share knowledge and provide expertise for AI model creation. & [109; 110; 111; 112; 113; 114; 115] \\  & learning techniques can extract patterns from large volumes of generated data at a speed that a human being cannot achieve. & & [116; 117], \\  & AI tools are much more suited than human operators to perform predictive analytics for high-velocity data generation & & [118; 119; 120; 121] \\  & learning techniques can extract patterns from large volumes of generated data at a speed that a human being cannot achieve. & & [116; 117], \\  & learning techniques can create competitive advantages by improving customers’ experience and engagement through digital assistants and conversational agents. & & [118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 14  
focused on AI applications at the edge of industrial networks and the development of federated learning methodologies. This trend has not been brought up in previous systematic literature reviews in the field of AI adoption in management information systems.