The digital age offers new opportunities for qualitative research, particularly in the transcription of interviews, focus groups, and field notes. Transcription is a critical process in qualitative data analysis, but it can be time-consuming, costly, and labor-intensive. The development of intelligent speech recognition technology has the potential to transform how qualitative data is transcribed. This paper explores the challenges and benefits of using intelligent speech recognition technology for transcription, with an application from a study on supportive self-management in Postural Orthostatic Tachycardia Syndrome (POTS). Transcription involves converting audio, video, or notes into written format for analysis. It requires strategic decisions about the necessity, extent, and method of transcription. Verbatim transcription is a dynamic process that involves more than just recording words; it requires understanding context and meaning. Researchers must check transcripts against audio recordings to ensure accuracy. Intelligent speech recognition systems use algorithms to process human voice signals and can improve communication with people with speech impairments. However, these systems raise concerns about data protection, algorithmic biases, and accuracy. Data protection is a critical issue, as researchers must comply with international and national regulations. Algorithmic biases can affect the accuracy of transcriptions, and researchers must be vigilant in checking transcription accuracy against audio or video recordings. The accuracy of intelligent speech recognition technology is also a concern. Poor audio quality, background noise, and dialects can affect transcription accuracy. Researchers must ensure high-quality audio recordings to minimize errors. The use of intelligent speech recognition technology can reduce the time and cost of transcription, but it also requires careful checking and cleaning of transcripts. In the POTS study, intelligent speech recognition technology was used to transcribe interviews. This approach provided benefits such as immediate access to transcripts and cost savings, but it also presented challenges, including inaccuracies in dialogue and the need for document formatting. Researchers must be aware of these challenges and ensure that transcriptions are accurate and representative of the data. In conclusion, the use of intelligent speech recognition technology in qualitative research offers new possibilities but also raises important questions about data protection, algorithmic biases, and accuracy. Researchers must be engaged in discussions and transparent about the implications of these technologies for their research.The digital age offers new opportunities for qualitative research, particularly in the transcription of interviews, focus groups, and field notes. Transcription is a critical process in qualitative data analysis, but it can be time-consuming, costly, and labor-intensive. The development of intelligent speech recognition technology has the potential to transform how qualitative data is transcribed. This paper explores the challenges and benefits of using intelligent speech recognition technology for transcription, with an application from a study on supportive self-management in Postural Orthostatic Tachycardia Syndrome (POTS). Transcription involves converting audio, video, or notes into written format for analysis. It requires strategic decisions about the necessity, extent, and method of transcription. Verbatim transcription is a dynamic process that involves more than just recording words; it requires understanding context and meaning. Researchers must check transcripts against audio recordings to ensure accuracy. Intelligent speech recognition systems use algorithms to process human voice signals and can improve communication with people with speech impairments. However, these systems raise concerns about data protection, algorithmic biases, and accuracy. Data protection is a critical issue, as researchers must comply with international and national regulations. Algorithmic biases can affect the accuracy of transcriptions, and researchers must be vigilant in checking transcription accuracy against audio or video recordings. The accuracy of intelligent speech recognition technology is also a concern. Poor audio quality, background noise, and dialects can affect transcription accuracy. Researchers must ensure high-quality audio recordings to minimize errors. The use of intelligent speech recognition technology can reduce the time and cost of transcription, but it also requires careful checking and cleaning of transcripts. In the POTS study, intelligent speech recognition technology was used to transcribe interviews. This approach provided benefits such as immediate access to transcripts and cost savings, but it also presented challenges, including inaccuracies in dialogue and the need for document formatting. Researchers must be aware of these challenges and ensure that transcriptions are accurate and representative of the data. In conclusion, the use of intelligent speech recognition technology in qualitative research offers new possibilities but also raises important questions about data protection, algorithmic biases, and accuracy. Researchers must be engaged in discussions and transparent about the implications of these technologies for their research.