The rapid shift toward Artificial Intelligence (AI) in business has created a “trust gap” where autonomous systems manage critical decisions—like hiring and lending—without enough human oversight. Current AI models often operate as “black boxes,” leading to risks like algorithmic bias, lack of transparency, and failure to meet legal regulations. While organizations want the efficiency of AI, they often lack a structured way to manage these ethical risks. This study proposes a Multi-Layer Ethical Governance Framework designed to bridge this gap. Unlike existing methods that handle ethics and technical performance separately, our framework integrates them into one system. It uses five functional layers—Data Governance, Ethical Evaluation, Governance Monitoring, Explainability, and Human Oversight—to ensure AI decisions are fair, understandable, and legally compliant. We tested this approach using a real-world Credit Risk Assessment scenario. The results show that the framework successfully flags hidden biases and provides clear “reason codes” for AI decisions, allowing humans to step in and prevent errors. This research provides a scalable and practical roadmap for businesses to adopt AI responsibly, ensuring that technology remains a tool for progress rather than a source of unfairness