Flaky tests are a persistent challenge for testers and developers, causing unpredictable failures and consuming valuable resources. These tests can be caused by various factors such as inadequate test environments, finicky network conditions, or code gremlins. In enterprise testing, flaky tests can have a significant impact on project stability and timely releases. AI-based tools offer a solution to this problem by automating the detection and resolution of flaky tests, allowing teams to focus on critical issues. By leveraging machine learning algorithms, these tools provide quick data analysis, root cause analysis, and early detection of potential flakiness, leading to enhanced test accuracy and effectiveness.