This study investigates the accuracy of using Open AI's Whisper model as an automated transcription tool for Swedish language interviews in a specialized IT domain. The research compares the performance of Whisper-generated transcripts against manual transcriptions, focusing on Word Error Rate (WER) as the primary metric of accuracy. Five interviews conducted with IT professionals at Trafikverket, the Swedish Transport Administration, serve as the basis for this analysis. The study reveals variations in transcription accuracy, with Word Error Rate (WER) ranging from levels close to human performance to being 5-6 times worse. A qualitative examination of the types of transcription errors, including substitutions, insertions, and deletions, provides deeper insights into the model's strengths and limitations. The findings suggest that while Whisper shows potential as a time-saving tool, its performance varies considerably. This variability highlights the importance of ongoing research to better understand and improve its reliability, particularly in smaller languages like Swedish. While these tools can be integrated into qualitative research, it’s crucial to be mindful of their current limitations and areas where they may fall short.