As AI models become increasingly intricate, explainable AI is gaining importance for enhancing transparency and understandability, addressing the so-called black-box problem where even experts struggle to comprehend AI decision-making processes. This transparency is crucial across various sectors, including finance and healthcare, where regulatory compliance demands clarity in AI tool functioning, and in autonomous vehicle development, where safety is paramount. Explainable AI not only facilitates greater trust and operational efficiency by identifying and mitigating biases, it also aids in troubleshooting and refining AI tools. Despite challenges in defining explainable AI and its varying effectiveness depending on the model type, it is essential for promoting interpretability, which involves understanding AI predictions and decisions through visualization tools like decision trees and dashboards. The development of explainable AI is in its early stages, but it is anticipated to evolve significantly, driven by the need for safer and more reliable AI solutions, with examples such as Dynatrace Davis AI already being utilized in major organizations to enhance operational capabilities through increased observability and transparency.