The highest-value applications of AI in QA automation tend to be: CI/CD pipeline configuration and optimization, framework refactoring and code standardization across large test suites, automated coverage analysis built from API specifications, notification setup, data provider generation, and post-writing code quality reviews. These tasks share a common property: the target state is well-defined and the output is verifiable. Complex test logic for regulated workflows is better handled by engineers with domain expertise, with AI supporting specific, bounded tasks.