The article highlights the "trust gap" in AI adoption, where business users are skeptical of AI-driven insights due to a lack of understanding and explainability. To bridge this gap, it's essential to provide context and explanations for AI outputs, ensuring that users can trust the results. A universal semantic layer plays a crucial role in achieving this by defining shared definitions and metrics, allowing AI systems to point to exactly how a metric is calculated. By designing AI outputs with transparency in mind, including annotations, drill-down options, and contextual help, business users can regain confidence in AI-driven insights, ultimately leading to increased adoption and value delivery.