This study explores how leadership influences the adoption and application of Generative AI (GenAI) within the private sector in Sweden, with a specific focus on Research and Development (R&D) departments in manufacturing and technology firms. As GenAI emerges as a transformative force with significant potential for innovation and efficiency, understanding the leadership practices that guide its responsible and effective use becomes essential. Drawing on the theoretical lens of Transformational Leadership, the study investigates how leaders support, enable, and challenge the adoption process of GenAI within organizational structures.

The study is based on qualitative interviews involving seven leaders and experts across five organizations actively engaged in GenAI initiatives. Thematic analysis revealed six key themes: 1) leadership influence on AI adoption, 2) organizational strategy and processes, 3) training and knowledge-sharing efforts, 4) evaluation and integration challenges, 5) contextual and ethical concerns, and 6) collaboration and future potential. The findings indicate that transformational leadership behaviors such as vision-setting, individual support, and intellectual stimulation are involved in guiding GenAI efforts, but they also reveal inconsistencies in awareness, strategy alignment, and value measurement across firms.

The findings suggest that leaders who actively engage with GenAI, promote trainings, and articulate a compelling vision tend to foster greater confidence and initiative among teams adopting the technology. However, leadership influence varied significantly across and within organizations, highlighting inconsistencies in support, understanding, and strategic clarity. Interview Participants also reported challenges in measuring the value of GenAI, with many unsure how to evaluate outcomes or define success.

A particularly striking observation was that not all leaders were fully aware of GenAI’s broader potential, and many organizations struggled to define appropriate metrics for assessing its value. The study also faced challenges in data collection, as several potential participants were hesitant to share internal information due to the sensitivity of ongoing AI initiatives. These insights underscore the need for more structured knowledge-sharing platforms and frameworks to assess the impact of GenAI in complex organizational environments.

This study contributes to understanding of leadership’s role in technological innovation and opens opportunities for further inquiry into ethical, strategic, and organizational dimensions of AI integration. It also contributes to existing research by contextualizing transformational leadership within the emerging field of GenAI adoption, offering practice-based insights from the Swedish R&D landscape.

Looking ahead, future research could explore how different leadership styles (e.g., transactional, distributed, or servant leadership) influence GenAI adoption in various organizational contexts. A comparative or longitudinal design might also reveal how leadership impact evolves as GenAI matures within firms. Another promising direction could involve developing or testing evaluation frameworks specifically tailored to GenAI initiatives in R&D, a gap that emerged clearly in this study.