Develop An AI Powered Predictive Maintenance System for Machine and Equipment

Artificial Intelligence (AI) is playing an increasingly critical role in decision-making processes across sectors such as healthcare, finance, and industrial operations. This paper investigates both the limitations and the transformative potential of AI systems in such contexts, with a focus on ethical, technical, and interpretability challenges. Using a custom-designed predictive maintenance model as a central case study, we demonstrate how AI can be effectively applied to forecast equipment failures and optimize maintenance schedules in industrial environments. The model leverages real-time sensor data and machine learning algorithms to reduce downtime and enhance asset longevity. Through this case, we highlight key issues such as data bias, model transparency, and the risk of overdependence on automated systems. Our findings support the development of a human-in-the-loop framework that balances AI-driven efficiency with human judgment, offering guidelines for responsible and context-aware AI deployment in high-stakes decision-making domains.