Abstract
The study aims at the latest trends for disruptive technologies. With the emergence of artificial intelligence and machine learning, the very fabric of every system has been shaken, therefore, the study highlights the essential factors through extensive thematic analysis. As it is qualitative research that employs content analysis to investigate the role of artificial intelligence (AI) and machine learning (ML) as enablers for disruptive technologies. The study is conducted in three main phases: 1) data collection, 2) theme extraction, and 3) thematic analysis. A total of 570 articles encompassing industry reports, academic databases, and news sources, published between 2013 and 2023, were analyzed using content analysis. A coding scheme was developed, and key themes were extracted and categorized. The developed categories include efficiency, innovation, accessibility, network effects, scalability, and disintermediation. To substantiate the extracted themes, we also conducted interviews of 27 experts from the field of AI and ML to gain a deeper understanding. Thematic analysis was employed to analyze the interview data, and a set of findings and conclusions were developed based on the recurring themes that emerged from the data. The study provides valuable insights regarding the role of AI and ML as enablers of disruptive technologies and their impact on different sectors.The study aims at the latest trends for disruptive technologies. With the emergence of artificial intelligence and machine learning, the very fabric of every system has been shaken, therefore, the study highlights the essential factors through extensive thematic analysis. As it is qualitative research that employs content analysis to investigate the role of artificial intelligence (AI) and machine learning (ML) as enablers for disruptive technologies. The study is conducted in three main phases: 1) data collection, 2) theme extraction, and 3) thematic analysis. A total of 570 articles encompassing industry reports, academic databases, and news sources, published between 2013 and 2023, were analyzed using content analysis. A coding scheme was developed, and key themes were extracted and categorized. The developed categories include efficiency, innovation, accessibility, network effects, scalability, and disintermediation. To substantiate the extracted themes, we also conducted interviews of 27 experts from the field of AI and ML to gain a deeper understanding. Thematic analysis was employed to analyze the interview data, and a set of findings and conclusions were developed based on the recurring themes that emerged from the data. The study provides valuable insights regarding the role of AI and ML as enablers of disruptive technologies and