Explainable AI refers to methods and tools that make AI decisions transparent and understandable to humans. This involves showing the factors, logic, or data points that led to a specific recommendation or action. In regulated industries like finance, healthcare, or government, explainability is critical for trust, compliance, and accountability. Without it, even accurate models may face resistance from business users who need to justify decisions internally or to regulators. XAI bridges the gap between AI accuracy and human trust, enabling safer adoption.