The present study was conducted to investigate the challenges of adopting artificial intelligence in the recruitment and hiring process in knowledge-based companies. The study is applied in terms of purpose, descriptive in terms of data collection, and qualitative in terms of approach. The statistical population of this study includes CEOs, human resource managers, key employees of knowledge-based companies, and experts in the field of artificial intelligence. The research sample was purposively selected to include 12 CEOs, human resource managers, and artificial intelligence experts. Data were collected through semi-structured interviews and analyzed using open, axial, and selective coding stages in MaxQuda software version 2020. In addition, the data analysis method is theme analysis. To ensure validity, the double coding method was used, and reliability was evaluated by calculating the percentage agreement coefficient (83.78%) and Cohen's kappa coefficient (κ=0.72), which indicates "good to strong" reliability. Data analysis identified four main themes of challenges: technical and infrastructure challenges (including a shortage of skilled labor and high costs), human and cultural challenges (such as resistance to change and the need for training), organizational and managerial challenges (such as weak human resource structures and managers’ focus on operational tasks), and ethical and legal challenges (such as a lack of clear regulations and privacy issues). This study, in its innovation, looks at integrating AI with the critical recruitment and hiring processes at the heart of knowledge-based companies. By uncovering the challenges ahead, we aim to open up pathways that will accelerate these new technologies, innovation, and productivity in these leading-edge ecosystems.