This thesis investigates how software professionals perceive the organizational impact of Artificial Intelligence (AI) integration in software development. As AI tools, including generative language models and code assistants, become increasingly embedded in technical workflows, there is a growing need to understand how they reshape productivity, decision-making, collaboration, and professional roles from the perspective of practitioners.The study employs a qualitative methodology grounded in an interpretivist paradigm. Data was collected through semi-structured interviews with six professionals working across diverse industries, including cloud computing, finance, healthcare, and embedded systems. Thematic analysis following Braun and Clarke’s (2006) framework was used to identify recurring patterns in the data. The analysis is informed by theoretical perspectives on AI integration, human–AI collaboration, and organizational innovation. Five overarching themes were identified: Productivity Acceleration, Code Confidence with Caution, Collaboration Dissonance, Adaptive Skill Shifts, and Risk-Aware Adoption. The findings suggest that while AI enhances individual productivity and learning, concerns remain about reliability, trust, and security. Skill demands are shifting toward hybrid roles and prompt engineering, while human oversight continues to play a crucial role, especially in high-risk or regulated settings. Team collaboration dynamics, however, appear largely unchanged at this stage. This research contributes to the understanding of AI’s socio-technical impact in software development, highlighting both the opportunities and organizational challenges arising from its integration. It calls for thoughtful integration strategies that promote responsible AI use while preserving developer trust and organizational coherence. The findings offer actionable insights for managers seeking to align structures, roles, and workflows with the growing presence of AI.