Between Policy and Practice: GenAI Adoption in Agile Software Development Teams

TL;DR

The paper investigates how GenAI is adopted in agile software development by conducting an exploratory qualitative multiple-case study across three German organizations, collecting 17 interviews and internal documents. Through thematic analysis and cross-case synthesis within the TOE framework, it shows GenAI is mainly used for creative tasks, documentation, and code assistance, delivering efficiency and creativity gains but facing barriers from data privacy, validation workloads, and governance gaps. A key finding is the policy-practice gap: organizational rules often misalign with actual usage, leading to shadow IT and fragmented governance. The work highlights the need for context-aware governance that aligns technological capabilities with organizational practices and regulatory requirements, informing policy design, training, and tool integration in agile settings.

Abstract

Context: The rapid emergence of generative AI (GenAI) tools has begun to reshape various software engineering activities. Yet, their adoption within agile environments remains underexplored. Objective: This study investigates how agile practitioners adopt GenAI tools in real-world organizational contexts, focusing on regulatory conditions, use cases, benefits, and barriers. Method: An exploratory multiple case study was conducted in three German organizations, involving 17 semi-structured interviews and document analysis. A cross-case thematic analysis was applied to identify GenAI adoption patterns. Results: Findings reveal that GenAI is primarily used for creative tasks, documentation, and code assistance. Benefits include efficiency gains and enhanced creativity, while barriers relate to data privacy, validation effort, and lack of governance. Using the Technology-Organization-Environment (TOE) framework, we find that these barriers stem from misalignments across the three dimensions. Regulatory pressures are often translated into policies without accounting for actual technological usage patterns or organizational constraints. This leads to systematic gaps between policy and practice. Conclusion: GenAI offers significant potential to augment agile roles but requires alignment across TOE dimensions, including clear policies, data protection measures, and user training to ensure responsible and effective integration.

Figures (2)

• Figure 1: Example of the coding process
• Figure 2: Visualization of the identified use cases