# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 8 Conclusions and Future Work  
This paper has delved into the realm of AI-driven assistants within the context of SADMs, especially SAFe, offering a comprehensive overview of their potential, categorization based on application domains, and the challenges they aim to address. Through the course of this paper, we have uncovered and provided detailed explanations of the challenges that are encountered commonly in SADMs, with a particular emphasis on SAFe. The challenges identified encompass issues related to coordination, resource utilization, and resistance to change, which are of considerable significance in large-scale software development.  
Through the exploration of challenges articulated in the paper's introduction, we aim to propose AI-driven solutions that partially address these issues. To achieve this objective, an initial exploration involved assessing the potential advantages and disadvantages of integrating AI within SADMs. Our findings suggest a preponderance of advantages over disadvantages. Nonetheless, it is imperative for organizations and enterprises to exercise caution and informed consideration in the adoption of AI within their operational frameworks. We have also assembled a comprehensive selection of AI-driven assistants that are, to some degree, capable of solving the aforementioned challenges. By categorizing and evaluating the potential of these assistants, we are shedding light on their potential contributions to enhance different aspects of SAFe. Additionally, we have not only emphasized the technical potential of the set of AI-driven assistants, but we have also considered the organizational aspects that are essential to their successful implementation; we have curated a collection of recommendations and suggestions gleaned from extensive literature, emphasizing established best practices derived from real-world instances.  
The findings derived from our research suggest several propositions for future work. We propose that forthcoming research should focus on identifying AI-driven assistants capable of enhancing all facets across diverse SAFe levels (e.g., within the portfolio level, to achieve Agile Product Delivery, which tools and assistants can improve Design Thinking and Lean UX). Conducting empirical studies within real-world SADM environments would offer invaluable insights into the practical effectiveness and challenges of implementing AI assistants. Furthermore, an exploration into the potential integration of emerging technologies like ML models or NLP to enhance the capabilities of these assistants represents a promising avenue for advancement.  
In summary, the integration of AI-driven assistants carries the potential to reshape the way large-scale projects are designed and executed. Empirical data [13; 14; 15; 30; 36] during this research have shown that the adoption of such AI-driven solutions can result in increased levels of collaboration, enhanced decision-making processes, and optimal project outcomes, as businesses strive to remain innovative and competitive. However,according to the discoveries in this paper, achieving a lasting and effective integration of AI-driven assistants requires a well-rounded approach that considers the advantages as well as drawbacks of AI-integration.  
Conceptualization, V.S.; Methodology, V.S. and L.P.; Supervision, L.P.; Validation, V.S. and L.P.; Visualization, V.S.; Writing--original draft, V.S. and L.P.; Writing--review & editing, V.S. and L.P. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057).  
Data are contained within the article.  
The authors declare no conflict of interest.