AI technology is being increasingly used in business and government to make better decisions, but there's a growing concern about the lack of transparency in AI decision-making processes. This opacity can lead to mistrust and skepticism among humans who rely on these systems for critical decisions that affect their lives, such as healthcare, finance, and education. Explainable AI (XAI) aims to address this issue by providing clear explanations for how AI models reach certain conclusions, which is essential for building trust in these systems. There is a need for standardization of explainability methods, as well as financial benefits from increased transparency, including improved adoption rates and revenue growth. However, the field of XAI is still emerging, and there's a lack of consensus on concepts such as interpretable AI, which can make it challenging to address inherent biases in machine learning models. Ultimately, strong AI transparency is crucial for success in areas like search optimization and other applications where human trust is essential.