# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 1 Introduction  
Since today's digital landscape is becoming more fast-paced and evolving by the minute, organizations face the challenge of adapting to change while still preserving their agility and growing size. Implementing well-known and verified frameworks to support large-scale development, such as the scaled agile framework (SAFe) (reportedly holding the largest usage share, at 53%, according to ref. [1]) and Large-Scale Scrum (LeSS) (reporting a 6% usage share as per ref. [1]), can help these organizations navigate complex challenges and unknown terrains. Frameworks like these serve as foundational building blocks for achieving the agility, cooperation, and innovation required for organizational success. Their implementation to achieve the organization's transformative goals is often followed by an increasing number of challenges, ranging from resistance and process complexities to overall confusion and organization-wide difficulties. It is estimated that a significant majority of enterprises encounter notable difficulties when attempting to apply conventional agile methodologies in such expansive and diverse settings. In this context, the potential ofAI emerges as a breakthrough technology for change and enhancement, with the possibility of revolutionizing various industries.  
The challenge that is the subject of this paper lies in the limitations of traditional scaled agile development methods (SADMs) to solve the challenges associated with the management of large-scale projects effectively. Although agile methods have proven successful in small-team environments [1], they often run into issues when dealing with complex systems involving many stakeholders, large code bases, and distributed teams. As a result of this, organizations need innovative solutions to overcome these obstacles and optimize their development processes. We will try to address this problem with the help of AI, which has tremendous potential to address the challenges that SADMs (implementations) are facing. By using AI techniques, organizations can improve their efficiency, decision-making processes, and overall productivity, as well as automate repetitive tasks, optimize resource allocation, improve communication and collaboration, and gain insights from large amounts of data. In addition, machine learning (ML) algorithms can enable predictive analytics, allowing teams to anticipate and mitigate potential risks.  
During the course of our research, we formulated the following research questions which will serve as our guidelines:  
**RQ1:**: How can AI-driven assistants be used effectively to address the most common challenges faced by SADMs in managing large-scale projects?
**RQ2:**: What are the potential benefits and challenges of incorporating AI into SADMs?
**RQ3:**: Which aspects of SADMs do AI-driven assistants improve?  
The rest of the paper is structured as follows. We begin with an introduction to SAFe, which serves as our chosen representative of SADMs, upon which we focus exclusively. In Section 2, we provide an overview of related works in the field of large-scale agile development. In Section 3, we present an extensive overview of our research area, within which we conducted a systematic literature review (SLR) to narrow down the literature relevant to our specific area of interest. In Section 4, we present the findings of our SLR, and in Sections 4.1 and 4.2, we explore the challenges of SADMs as well as advantages and disadvantages of implementing AI within them. Section 5 is dedicated to classifying the challenges encountered during our research for the different configurations of SAFe, providing comprehensive descriptions of their scope, and shedding light on the different AI-driven assistants which can, to some extent, address their resolution. In Section 6, we try to respond to the research questions, and, finally, in Section 8, we conclude this paper, summarizing the key findings derived from our research.