Human-centered complex systems are integral to various sectors. Despite AI’s potential to significantly optimize these systems through data analysis, behavior prediction, and process automation, several challenges remain, such as technical limitations, ethical concerns, and integration barriers. This research aims to address these gaps by exploring AI’s role in optimizing human-centered systems for effective AI integration. This research is novel in its comprehensive analysis across multiple domains, combining theoretical content analysis with practical insights from a case study in the healthcare sector. The findings highlight AI’s contributions to efficiency improvements, decision support, personalization, automation, predictive analytics, and resource management. The practical case study revealed that while AI integration improved diagnostic accuracy, administrative efficiency, and resource allocation in healthcare, it also encountered significant challenges related to IT infrastructure, data quality, and professional concerns. These insights underscore the need for upgrading technical infrastructure, ensuring data privacy, fostering human-AI collaboration, and navigating regulatory landscapes to maximize AI’s benefits.