# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 2 Related Work  
Throughout our investigation of challenges within SADMs, we encountered diverse publications that addressed the challenges we have consolidated in various contexts (see Section 4.1). Certain publications [6; 7; 8] conducted SLRs to identify challenges in SADMs systematically, while others [4; 8; 9; 10; 11; 12] gleaned insights from practical experiences by conducting structured interviews and reporting their empirical findings. Additionally, specific studies focused on categorizing these challenges into distinct facets, including aspects such as stakeholder management and reoccurring concerns and patterns [6; 11].  
In our exploration of the advantages of implementing AI in SADMs, we encountered a variety of publications. Notably, we observed some publications [13; 14], that either introduced or documented experiences related to pair-programming. Furthermore, we came across a paper that conducted an SLR [15], to provide an overarching view of the research domain, and to identify potential areas where AI-driven assistants could be deployed to offer assistance.  
[PERSON] et al. [6] comprehensively identified 79 challenges arising from large-scale agile development, with 41 new challenges emerging and 38 existing ones being strengthened by large-scale agile development. Their proposed future work [4] involves leveraging their large-scale agile pattern language to address recurrent challenges, without specifying particular tools. [PERSON] et al. [7] identified 11 challenges categorized as internal and external factors. Their research, as stated in their paper as a point of future work, was used as a basis to investigate the practices to overcome the identified barriers and utilize the success factors identified. [PERSON] et al. [8] conducted an in-depth multi-vocal literature review supplemented by empirical investigation, cautioning about the limited generalizability of their findings due to a small sample size and observational nature. Despite not suggesting specific solutions, they recommended suggestions for further investigation. [PERSON] et al. [9] outlined nine challenges across 13 cases and offered recommendations for resolution, acknowledging the complexity of complete elimination due to multifaceted factors, without mentioning AI-driven assistant solutions. [PERSON] et al. [10] derived their findings from qualitative interviews, presenting 24 challenges, and proposed solutions drawing from SAFe, LeSS, and their case companies, highlighting literature gaps and suggesting relevant solution candidates. [PERSON] et al. [11] surveyed 204 practitionersto identify reasons, benefits, and challenges in adopting SADMs, refraining from offering specific solutions while emphasizing the need for future focus on quality assessment.  
These sources significantly influenced our research, culminating in the synthesis of knowledge, particularly pertaining to challenges in large-scale development. Our contribution manifests in the proposition of a set of AI-driven assistants aimed at tackling these challenges to a certain extent. Interestingly, within the literature defined during the SLR process, no papers were encountered that **specifically** tackle challenges in large-scale development utilizing AI-driven assistants. This absence might be attributed, in part, to the limitations outlined in our research, as detailed in Section 7.