The rapid advancement of artificial intelligence (AI) has significantly transformed the design and functionality of modern information systems, enabling the development of intelligent and adaptive systems capable of real-time decision-making. Our results illustrated the distribution of AI applications across various domains, highlighting healthcare as the most prominent area with 25%, followed by smart systems, finance, and industry at 20% each, and education at 15%. This distribution reflects the growing importance of AI in critical sectors where decision support, automation, and predictive analytics are essential. The dominance of healthcare applications indicates the increasing reliance on AI for diagnosis, monitoring, and personalized treatment, while smart systems and industrial applications demonstrate the role of AI in optimizing operations and enabling adaptive responses. A comparative analysis of system performance presented and showing a clear progression from traditional systems (efficiency score of 0.65) to rule-based systems (0.72), machine learning-based systems (0.82), and AI adaptive systems (0.91). This trend highlights the significant improvement in system efficiency and decision-making capabilities achieved through AI integration. The results demonstrate that adaptive AI systems outperform conventional approaches by leveraging data-driven learning and continuous adaptation. This study underscores the importance of AI-driven approaches in designing next-generation intelligent systems and provides insights into future research directions for improving system performance and scalability.