# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 4 Review Results  
From the primary literature list of 30 papers, we identified **15** papers that addressed the use of AI-driven assistants or were use cases on their own. Within this selection, we were interested in the specific usage domains of AI assistants in SADMs (see Table 4). The largest proportion of the reviewed literature presented in Table 5 addressed human assistance. Others included risk prediction, problem solving, and organizational success and efficiency. Figure 1: Systematic literature review process. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Usage Domain** & **Sources** \\ \hline Human assistance & [9; 11; 12; 13; 14; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26] \\ Risk prediction & [7; 11; 12; 23; 27; 28; 29] \\ Problem solving & [7; 9; 14; 21; 23; 24; 26] \\ Organizational success and efficiency & [10; 14; 15; 27; 30] \\ Organizational creativity & [7; 10; 22; 23; 30] \\ Development & [23; 25; 27; 29; 31] \\ Planning & [17; 23; 24; 27] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Usage domains of AI assistants. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**ID** & **Title** & **Authors** & **Source** &
\begin{tabular}{l} **Identified** \\ **Assistant** \\ \end{tabular} \\ \hline
1 & A cautionary tale about the impact of AI on human design teams & [PERSON] et al. & [24] & ✓ \\
2 & A classification and review of tools for developing and interacting & [PERSON] et al. & [29] & ✓ \\
3 & A taxonomy of scaling agility & [PERSON] et al. & [32] & \\
4 & AI Information Architecture & [PERSON] et al. & [33] & ✓ \\
5 & AI-boosted software automation: learning from human & [PERSON] et al. & [13] & ✓ \\
6 & Applications of ML/AI for Decision-Intensive Tasks in Production & [PERSON] et al. & [15] & ✓ \\ Planning and Control & & & \\
7 & Artificial intelligence and the future of work: Human-AI & [PERSON] & [14] & ✓ \\ symbiosis in organizational decision making & & & \\ Artificial intelligence capability: Conceptualization, measurement & & & \\
8 & calibration, and empirical study on its impact on organizational & [PERSON] et al. & [30] & ✓ \\
9 & Artificial Intelligence Technology & Huawei Technologies Co. & [28] & \\
10 & Building the AI-Powered Organization & [PERSON] et al. & [34] & ✓ \\
11 & Comparing Methods for Large-Scale Agile Software Development: & [PERSON] et al. & [18] & \\ A Systematic Literature Review & & & \\
12 & Computer-aided mind map generation via crowdsourcing and & [PERSON] et al. & [22] & ✓ \\ machine learning & & & \\ Decoding the agility of artificial intelligence assisted human & [PERSON] et al. & [26] & ✓ \\ design teams & & & \\ Documenting recurring concerns and patterns in large-scale & [PERSON] et al. & [4] & \\ agile development. & & & \\
15 & Empowering Software Engineering with Artificial Intelligence & [PERSON]. & [17] & ✓ \\
16 & Hybrid Intelligence & [PERSON] et al. & [20] & ✓ \\ Identifying and Structuring Challenges in Large Scale Agile & [PERSON] et al. & [6] & \\ Development based on a Structured Literature Review & & & \\ Implementing Large-Scale Agile Frameworks: Challenges & [PERSON] et al. & [9] & \\
18 & Issues in the adoption of the scaled agile framework. & [PERSON] et al. & [8] & \\ Needs and challenges for a platform to support large-scale & [PERSON] et al. & [12] & \\ requirements engineering: a multiple case study. & & & \\ Predicting failures in agile software development through & [PERSON] et al. & [19] & ✓ \\ data analytics & [PERSON] et al. & [10] & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Primary literature list. The categorization of the AI assistants mentioned in Table 4 underscores their versatility, indicating that they are not limited to a single specific domain of application, but are designed to serve a broad range of purposes. As a result, organizations looking to integrate AI-driven assistants can frequently derive benefits across multiple dimensions rather than solely within their primary area of focus.