As artificial intelligence (AI) systems are increasingly deployed in critical decision-making domains such as finance, healthcare, and automated systems, the need for transparency and explainability has become a key challenge. Existing Explainable AI (XAI) techniques, including LIME and SHAP, provide technical insights into model behavior but often remain inaccessible to non-expert users. This study addresses the gap between AI explainability and user comprehension by introducing an AI Transparency Label, designed to standardize AI disclosures while maintaining adaptability across domains. Using a mixed-methods approach, we conducted a literature review to define essential AI Transparency elements, followed by two focus groups, one dedicated to content development and the other to structuring the label. The resulting AI Transparency Dashboard integrates five key elements: Model Identity, Global Explanations, Local Explanations, Bias and Risk Disclaimers, and Performance Metrics. The label adopts a modular, interactive design that balances concise overviews with expandable details, ensuring accessibility for diverse user groups. Findings suggest that dynamic transparency mechanisms, such as real-time updates and layered disclosures, enhance user trust and comprehension. Future work includes large-scale user testing and the development of a reference model for AI governance, supporting regulatory and industry-wide adoption of standardized AI transparency.