Explainable Artificial Intelligence examines how Explainable Artificial Intelligence is reshaping the design, governance and practical use of AI-enabled decision support in contemporary business. As organisations increasingly depend on complex algorithmic systems for strategic, operational and customer-facing decisions, questions of transparency, interpretability, accountability and trust have become central to responsible AI adoption. Bringing together foundational concepts, technical approaches and management perspectives, the authors show how XAI can bridge the gap between sophisticated AI models and stakeholders who must understand, scrutinise and act on their outputs. Through international case studies and discussion of emerging technologies, the authors offer practical insights into overcoming technical, organisational and governance barriers to explainable decision-making. Written for researchers, business leaders, data scientists, policymakers and advanced students, this volume provides a timely roadmap for developing AI-driven business systems that are effective, interpretable, trustworthy and aligned with the demands of a responsible global economy.