Explainable AI (XAI) is becoming increasingly vital across industries as organizations seek to ensure fairness, transparency, and trust in AI-driven decision-making. The need for XAI is underscored by regulatory pressures and the demand for systems that stakeholders can understand and trust, particularly in sectors like finance, healthcare, and public services. XAI techniques, such as model-agnostic methods like LIME and SHAP, intrinsic interpretability through simpler models, and post-prediction explanation methods, aim to clarify the reasoning behind AI outputs. This transparency fosters accountability, mitigates risks, and supports fairness by revealing and addressing potential biases. Different audiences require tailored explanations, from deep technical insights for data scientists to clear business implications for executives. Despite challenges like model complexity and lack of standardization, the evolution of XAI is guided by regulatory developments, bias audits, and human-in-the-loop approaches, aiming for transparent, trustworthy AI that complements human judgment and decision-making.