## 4 Results
There are five contribution categories, namely: framework, method, and technique; lessons learned; model or tool; guidelines; and implication or advice. A graphical representation of the contributions of the 60 primary studies is presented in Figure 4. Analysis of the 60 primary studies also shows that contributions were largely made as lessons learned (29 studies) and models or tools (15 studies), whereas advice or implication (7 studies), methods (6 studies), and guidelines (3 studies) comprised a smaller amount of the examined sample. Where more than one type of contribution was identified, the main contribution, as stated by the authors of the given primary study, was assumed as the type of contribution to be counted for that study. It is important to note that contributions of the type 'lessons learned' are context-specific, and given the large variety of applications that are examined, accumulating universally applicable knowledge from these types of contributions is time-consuming. The need for more studies focusing on guidelines and frameworks, especially in terms of ethics, data privacy, and security for AI adoption in MIS is highlighted. A rise in research  
Figure 4: Distribution of primary studies by type of research contribution. Figure 3: Distribution of primary studies by platform type. studies contributing models and tools is evident, in contrast with reported outcomes from previous literature reviews [4], which called on the research community to contribute more specifically to the categories of models and tools. **RQ. 4**_What business value has been identified by studies of AI in MIS?_  
As noted in previous literature reviews [4; 123], the qualitative approach to the classification of AI applications in MIS based on the type of business value generated by the research is more appropriate than arbitrary measurements of business value in a specified currency, which are subject to inaccuracies due to the rapid pace of market changes in the technology domain. The following business value types are defined:  
* Process Automation, i.e., the value generated due to the automation of business processes and elimination of low-value-added tasks from human workflows. * Analytical Insight, i.e., the value generated due to business insight based on predictive data analytics and forecasting techniques. * Cognitive Interaction, i.e., the value generated due to the engagement of employees or customers with digital assistants, AI tools and interfaces, or NLP agents. A visual representation of the distribution of primary studies based on business value type is presented in Figure 5.  
Future research efforts can shift focus away from process automation and toward applications that generate value with respect to analytical data insights and cognitive interaction. **RQ. 5**_What are the challenges and benefits of the adoption of AI in MIS?_  
The identified benefits and challenges within the primary research studies are summarized in Table 4. The classification is based on the source of generated business value. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source of Business Value** & **Benefits** & **Challenges** & **Primary Sources** \\ \hline \multirow{6}{*}{Process Automation} & \multirow{6}{*}{
\begin{tabular}{c} Ease of implementation and rapid return on investment. \\ Allows human resources to focus their attention on value-added tasks. \\ \end{tabular} } & To achieve benefits through the use of AI in automation, a unified digital business strategy must be developed and implemented at all company & [84; 85; 92; 94], [95], [95], [96], [97], [98], [99], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [99], [93], [95], [96], [97], [98], [99], [99], [100], [101], [102], [103], [104], [105], [106], [107] \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Challenges and benefits of adoption of AI in MIS. Figure 5: Distribution of primary studies by business value type.