## 2 Background and Related Research
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Capabilities * & **Description** \\ \hline Predictive Analytics & A branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques, and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities [33]. \\ \hline Intelligent Process Automation & The application of neural networks and related new technologies, including computer vision and cognitive automation, for robotic process automation, i.e., technology that simplifies the processes of building, deploying, and managing software robots (bots), and the processes of deploying and managing context-aware robots in an industrial setting [34]. \\ \hline Machine Learning & The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data [35]. \\ \hline Natural Language Processing & NLP combines rule-based modeling of human language (computational linguistics) with statistical, machine learning, and deep learning models to enable computers to process human language in the form of text or voice data and to analyze and synthesize a relevant response in the form of natural language and speech. NLP is the foundation of speech recognition [36]. \\ \hline Machine Vision & All industrial and non-industrial applications in which a combination of hardware and software provide operational guidance to devices in the execution of their functions based on the capture and processing of images [37]. \\ \hline Expert Systems & A computer program that is capable of simulating the judgment and behavior of a human or an organization that has expertise and experience in a particular field [38]. \\ \hline \hline \end{tabular}  
* Studies regarding the use of different AI capabilities in robotics have been summarized within the category of AI capability used by the study.  
\end{table} 
Table 1: List of AI capabilities as identified by the referenced primary sources. business decisions, as well as employing AI for process automation of repetitive low-value-added tasks [40]. Here, the term business analytics is used to describe a set of disciplines and technologies for solving business problems using data analysis, statistical models, and other quantitative methods, involving an iterative, methodical exploration of organizational and experience data, with an emphasis on statistical analysis, to drive decision-making. AI-powered business analytics tools allow organizations to utilize machine learning algorithms and predictive analytics to identify trends and extract insights from complex data sets across multiple organizational sources [41]. Based on the provided definitions of AI capabilities and the defined scope of MIS, a systematic literature review of the existing applications of AI in MIS was prepared and will be presented in this paper. However, before the findings of the review are discussed, it is appropriate to consider previously conducted literature reviews with similar agendas. Literature studies that have been conducted with respect to the implementation of AI in an organizational or business setting, can be summarized in the following categories based on the main research topic:  
* Finance and Accounting [42; 43];
* Information management [44];
* Human resource management [45; 46; 47];
* Decision-making [48; 49];
* Forecasting [50];
* Conversational agents [51]. It is evident that each of the above-mentioned literature reviews has focused on a narrow segment of AI applications in management information systems.