This paper shows how to make AI explain itself across the whole pipeline, start to finish. This work extends explainability to cover data choices, training decisions, checks on quality, and how results are presented. Introduces Holistic Explainable Artificial Intelligence (HXAI), user-centric framework that embeds explanation into every stage of the data-analysis workflow. Each part answers plain questions, where data came from, why features and splits were chosen, what the model did, and how much to trust it. It matches explanations to 3 audiences, domain experts, data analysts, and data scientists. The same system can talk in business outcomes, quick diagnostics, or developer traces, depending on who listens. It compiles a 112 question bank and uses it to benchmark current tools. The pattern is clear, strong support for model outputs, weak support for data decisions and reliability checks. It also sketches an LLM agent that pulls together different explanation methods and rewrites them for each role. The result is not more charts, it is explanations that are accurate, actionable, and easy to use. ---- Paper – arxiv. org/abs/2508.11529 Paper Title: "A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow"