Intelligent test automation is an approach that enhances the efficiency and effectiveness of the test automation process by incorporating technologies like artificial intelligence, machine learning, and more. It uses a model-based testing approach where models representing expected behavior or business logic help automate different testing tasks. Intelligent test automation improves decision-making, offers predictive testing capability, provides high speed and better efficiency, simplifies complex workflows, and boosts software quality. It is used for creating test scripts, automated test execution, self-healing tests, predictive testing, model-based testing, reports and insights, and reducing manual effort leading to cost savings. However, it poses challenges such as test maintenance challenges, limitations in the availability of high-quality data, ambiguities and measuring ROI, over-dependence on artificial intelligence, compliance and security concerns, and requires careful planning and execution. Best practices for adopting intelligent test automation include having clarity in objectives and goals, choosing the right technologies and tools, fostering collaboration and upskilling teams, starting small with gradual scaling, prioritizing risk areas and test coverage, continuous monitoring and optimization, aligning ITA with CI/CD practices and DevOps, and fostering a healthy collaboration between operations, development, and QA teams. Its future looks promising with the integration of AI and ML into test automation tools, driving innovation, boosting productivity, and transforming operations.