The FAIR (Findable, Accessible, Interpretable and Reusable) principles serve as a fundamental standard for data management across diverse scientific disciplines, ensuring that data is efficiently discovered, shared, and reused. However, as technology evolves, especially with the exponential growth of advanced systems like Artificial Intelligence (AI) and Digital Twin systems, several limitations within the spectrum of FAIR principles have originated. These limitations in the areas of explainability, fairness, reproducibility, machine-actionability, and privacy, are becoming increasingly critical as data-driven decision-making systems become more autonomous and effective. In particular, sectors such as healthcare, finance, disaster management, and AI have highlighted the need for supplementary frameworks that improve transparency, address ethical issues, and ensure the reliability and robustness of AI models. This paper synthesizes findings from the literature to explore where the FAIR principles fall short and propose necessary revisions. By discussing supplementary concepts such as Explainable FAIRness, Privacy-First FAIR, and Real-Time FAIR, this paper aims to improve the framework’s implementation to modern, dynamic data management systems. These proposed updates focus on enhancing explainability, accountability, and data integrity in AI-driven environments by ensuring that the FAIR framework remains particularly effective in addressing the increasing complexity of demand of today’s data ecosystems.