Collective Intelligence in Practice: A Case Study of Saf-E Implementation in Large-Scale Automotive Software Development
Maria Alessandra Montenegro, Sebastian Boenisch, CARIAD SE, Germany · 2025
This paper presents a case study of the application of the Scaled Agile Framework (SAF-e) in a software development project within the German automotive industry. Conducted between 2021 and 2024, the project involved over 100 team members and multiple organizations across the automotive supply chain. The project focused on integrating artificial intelligence (AI) into in-vehicle systems, requiring high levels of coordination, adaptability, and compliance with safety standards. SAF-e was selected to enable structured collaboration, cross-team synchronization, and continuous value delivery across a distributed environment. The study adopts a qualitative approach based on the author’s role as a Scrum Master, supported by internal documentation and informal interviews. Key aspects of SAF-e implementation—team structure, agile roles, backlog management, and synchronization events—are analyzed. Particular attention is given to how the framework fostered decentralized decision-making and distributed knowledge sharing, exemplifying principles of collective intelligence within large-scale project environments. The findings contribute to the understanding of agile at scale, especially in innovation-driven and safety-critical sectors like automotive. The paper concludes with practical implications and recommendations for future research on scaling agile practices in complex, regulated, and technologically evolving domains.