Critical interactive systems operate in high-consequence environments where automated decisions have immediate and significant impact. Artificial Intelligence (AI) offers substantial potential to support human operators in these settings, yet its adoption introduces challenges related to transparency, interpretability, and trust. This paper provides a systematic analysis of the opportunities and challenges of embedding Explainable AI (XAI) in such systems, with attention to core properties including usability, dependability, safety, privacy, and confidentiality. We illustrate these considerations through a case study on an AI-driven early-warning system for atmospheric turbulence in commercial aviation, emphasizing the interactions between algorithmic explanations, human cognition, operational constraints, and regulatory requirements. Based on this analysis, we propose research directions that address methodological, human-centered, and regulatory challenges for the practical integration of XAI in safety-critical interactive systems.