Explainable Artificial Intelligence (XAI) addresses one of the most critical challenges in machine learning. That is the opacity of complex models. While traditional AI models offer powerful, predictable capabilities, their lack of interpretability creates obstacles for adoption in high-stakes business applications. This paper will explore the principles, methodologies, and real-world implementation of explainable artificial intelligence in business environments. It focuses on how transparency and interpretability foster trust, better decision-making, and accountability. It draws on current literature. The study systematically examines XAI frameworks and their applications in various industries. These industries include manufacturing, finance, and healthcare. It also discusses emerging trends, challenges, and the path forward for integrating XIA in enterprise-level systems.