# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 6 Discussion  
This paper presents a comprehensive examination of integrating AI-driven assistants within SADMs. We have identified and emphasized a range of significant findings and insights resulting from our chosen research method--SLR. Our research focused primarily on the incorporation of AI-driven assistants into a specific SADM, known as SAFe, with the aim of comprehending the potential advantages and implications of their utilization in software development. Firstly, we investigated how AI-driven assistants can address the common challenges encountered by SADMs when managing large-scale projects effectively. Secondly, we delved into the potential benefits associated with the integration of AI in SADMs. Lastly, we explored how AI-driven assistants can enhance specific aspects of SADMs. **RQ1: _How can AI-driven assistants be used effectively to address the most common challenges faced by SADMs in managing large-scale projects?_**  
In our research, we have found that AI-driven assistants hold significant promise in addressing the challenges encountered in managing large-scale projects within SADMs. As presented in Section 4.1, the consolidated list of challenges we identified in the literature scope is substantial. Examples of such challenges include balancing organizational structure and method, maintaining developer autonomy, common system understanding, etc. However, it is important to note the positive impact that the integration of AI-driven assistants can have in the solution of these challenges. Given the adaptability of SAFe to diverse organizational sizes, our initial aim was to categorize the identified challenges according to the SAFe levels (essential, large-solution, and portfolio) where they are most likely to occur. Our SLR unveiled a multitude of papers (citation rates defined in Figure 6) that either tackled these challenges explicitly through the deployment of specialized AI-driven assistants, or provided overall guidance on how organizations could address these challenges effectively by integrating AI. In Section 5, we demonstrate a connection between the identified challenges, their potential solutions involving AI-driven assistants, and recommendations for the implementation of AI. This comprehensive analysis encompasses all identified challenges at every SAFe level, with the complete set of challenges spanning the entirety of the SAFe hierarchy, including the full level, which is the highest and most extensive one. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Challenge** & \
\begin{tabular}{l} **AI-Driven** \\ **Solution** \\ \end{tabular} &
\begin{tabular}{l} **Suggestions and Recommendations** \\ **for Implementing AI** \\ \end{tabular} \\ \hline P1: Comparing and contrasting methods & / & [7; 14] \\ P2: Balancing organizational structure and method & [13] & [14; 20; 23] \\ P3: Top-down instead of bottom-up approach & / & [22; 23] \\ P4: 100\% adherence to the method & [13] & [20; 22] \\ P5: Lack of evidence-based use & / & [7; 22; 23] \\ P6: Organizational aspects & [13; 27] & [20; 22] \\ P7: Inefficient prioritization and management & [13; 15] & [24; 29; 30] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Suggestions for the resolution of challenges at the portfolio level. **RQ2: _What are the potential benefits and challenges of incorporating AI in SADMs?_**  
In the course of our research, we placed particular emphasis on identifying the advantages and challenges associated with the integration of AI within the context of SADMs. The results of our research, which relied on the limited literature gathered during the SLR process, are presented in Section 4.2. During our research, we identified seven distinct advantages, encompassing aspects such as process automation, dynamic planning and scheduling, as well as iterative enhancement, among others. In addition to this, we have delineated six challenges, spanning from critical thinking, biases and unfairness in data, and challenges in integration.