AI is transforming test automation from brittle script maintenance to intelligent quality assurance. LLM test generation: Copilot, GitHub Copilot, and specialized tools like CodiumAI analyze code and generate unit tests with edge cases. Prompt: "Generate comprehensive pytest tests for this function, including normal cases, edge cases, error conditions, and boundary values." Review generated tests and add missing cases. Playwright AI: emerging pattern using LLM to convert natural language test specs to Playwright code. "Click the submit button and verify the success message appears" -> generated test code. Self-healing selectors: tools like Healenium detect when selectors break after UI changes and automatically find new matching elements. Reduces maintenance by 50-70% for changing UIs. Visual testing with AI comparison: Applitools uses ML to compare screenshots, distinguishing intentional changes from unintended regressions. Better than pixel-perfect comparison. Test case prioritization: ML models trained on failure history predict which tests are most likely to catch regressions given recent code changes. Run high-priority subset first for faster feedback. Autonomous testing agents: experimental tools (Mabl, Testim) record user flows and automatically update when UI changes. Best for stable, high-value user flows. Investment: AI testing tools cost $50-500/month but save hours of test maintenance weekly.