As Artificial Intelligence (AI) becomes central to organizational decision-making, concerns about transparency, accountability, and trust intensify—especially in high-risk enterprise environments such as finance, logistics, healthcare, public administration, and cybersecurity. Explainable AI (XAI) has emerged as a critical requirement for ensuring that algorithmic decisions remain interpretable, auditable, and compliant with regulatory norms. This research synthesizes current literature and proposes a unified Explainable AI Framework (EAIF) for high-risk enterprise decisions. The framework integrates model transparency, interpretability tools, risk categorization layers, human–AI collaboration mechanisms, and continuous monitoring loops. The study further introduces the Explainability Assurance Cycle (EAC), providing a step-by-step decision workflow for enterprises. Figures, conceptual models, and implementation guidelines are provided to help organizations deploy XAI in mission-critical settings. The paper concludes with implications for enterprise governance, future trends, and recommendations for XAI research.