Abstract: This study explores the role of artificial intelligence (AI) in the early stages of information systems (IS) development, specifically in identifying system problems for system improvement and in finding solutions to problems that arise during the system lifecycle. The current study begins by reviewing traditional methods such as interviews, questionnaires, and modeling, highlighting their limitations in today complex and data-rich environments. The research adopts a descriptive methodology and combines a review of relevant literature with structured interviews with systems analysts to investigate the benefits and challenges of AI-based tools such as machine learning, natural language processing, and anomaly detection. The study findings reveal that while AI significantly enhances the accuracy, speed, and scalability of problem identification, its adoption faces challenges such as data quality issues, tool complexity, lack of training, and ethical concerns. The study concludes that integrating AI with - rather than replacing - human expertise provides the most effective approach to identifying IS problems, paving the way for the development of a more accurate, adaptive, and user-friendly system.