# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 4 Review Results
### Challenges in Scaled Agile Development  
The use of SADMs is becoming increasingly common in modern software development organizations [1]. The transition to scaled agile development has many potential benefits, but there are also difficulties due to coordination challenges, communication difficulties, and lack of flexibility [9], among many other things. Many turn to the use of SADMs such as SAFe to overcome these problems, since they have established workflow patterns and processes, and are supported widely by extensible tools. However, empirical research on the adoption of these methods, their use, success, and problems, is still quite immature [4; 6; 7; 9; 11; 36]. [PERSON] et al. [9] conducted a study analyzing 13 large projects of different global companies over a period of 15 years. Their results show that the success of implementing SADMs depends on many different factors, not only following the framework's regulations \"by-the-book\". Some businesses and organizations continued to use the selected method and, over time, observed bigger success; others switched to a different type of method, and consequently enhanced their performance and efficiency; and, finally, some totally abandoned the use of the SADMs for several reasons. Figure 2 displays a graphic representation of SADMs' implementation over time. A 15-year study [9], a 2021 survey [10], and a proposition for the implementation of an AI-driven solution [27] highlight common challenges in organizations adopting SADMs. \begin{table}
\begin{tabular}{l l l} \hline \hline
**ID** & **Source** & **Description** \\ \hline
16 & [20] & Examining how the integration of human and AI collaboration, known as hybrid intelligence, enhances real-world business applications. \\  & & Introducing “analytics-driven testing (ADT)” that predicts software failures in agile sprints by applying analytical and statistical techniques, while also estimating error locations with a specified statistical confidence level. \\
23 & [27] & Addressing challenges within SAFe and proposing a machine learning model as a potential solution to track team performance. \\
26 & [23] & Delving into the synergy between AI and SE, investigating AI’s contributions to SE and strategies for enhancing the development of AI systems by software engineers. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 2: The adoption of large-scale agile frameworks (based on [9]). [PERSON] et al. [6] conducted an SLR that identified stakeholders in SADMs. The review pinpointed 79 challenges, categorized into 11 groups. [PERSON] et al. [11] explored the reasons for adopting SADMs, the potential benefits, as well as the satisfaction their implementation can cause in scaled environments, through a survey of software practitioners. The gap of documenting reoccurring concerns and patterns in large-scale development is one of the key points that [PERSON] et al. [4] were trying to address in their paper. They introduced their own pattern language, which, equipped with structured interviews with 14 large-scale agile development experts from 10 organizations, gave us additional insights as to what challenges and concerns real-life organizations face. [PERSON] et al. [7] conducted an SLR and a SWOT analysis exploring the effects of the adoption of SADMs. In addition to this, [PERSON] at al. [8] undertook an SLR complemented by empirical research to gain a deeper understanding of adopting SADMs, with a focus on SAFe. The input data were gathered from 25 respondents across 17 companies in eight countries, highlighting challenges related to decision making, organizational structures, and technical and managerial competencies. Separate case studies with three different companies from different industries were the topic of [PERSON] et al. 's [12] research paper. These studies centered on identifying current issues, needs, and implementation challenges through structured interviews.