The study explores the evolving role of human and artificial intelligence (AI) in qualitative data analysis. In
international business (IB) research, qualitative inquiry has traditionally positioned the researcher as the central driver of interpretation. This view, however, has often underestimated how technological tools influence the way data is organized, processed, and analyzed - a gap that has become increasingly important to address.

The study critically evaluates the use of generative AI as an analytical instrument. We assess its performance in handling qualitative data, focusing on its limitations. This involves dispelling common myths about AI's analytical capabilities and the template-thinking it promotes, and examining how researchers can separate genuine insight
from misleading output.

We then turn to the foundational principles of qualitative data analysis in IB and discuss how IB researchers can more explicitly recognize and articulate the human elements - such as interpretation, imagination, comparison, and analytical sense-making. We explore how these elements can be preserved and even strengthened when it comes to qualitative data analysis. The study introduces multiple analytical approaches and strategies that enable researchers to improve the theorizing of their data. By doing so, we aim to ensure that qualitative research remains both meaningful and impactful in the age of AI.