Red-Green-AI: How BDD and Test Automation Became 10x Faster with AI Copilots

Writing Gherkin scenarios is tedious. Writing step definitions is boilerplate hell. Maintaining Playwright selectors when the UI changes makes you want to quit testing altogether. But what if your AI copilot could generate comprehensive BDD scenarios from requirements, scaffold step definitions that follow your team's patterns, and even suggest test cases you forgot? Not "magic AI testing"—real behavior-driven development that's faster, more consistent, and actually maintainable.

This session reveals how to supercharge your BDD workflow using AI assistance at every stage while maintaining test quality. You'll learn how to transform EARS-formatted requirements into Gherkin scenarios automatically, generate Reqnroll step definitions that follow your conventions, and leverage AI to identify edge cases and negative test paths you might have missed. We'll explore the complete pipeline from user story through executable tests: using steering files to enforce your BDD style guide, prompting patterns that generate maintainable Playwright locators, and techniques for keeping tests synchronized as requirements evolve. You'll see real examples from healthcare software where comprehensive test coverage isn't optional—it's a compliance requirement.

But it's not all roses. We'll tackle the hard problems: when AI-generated tests give false confidence, how to review AI test code effectively, and maintaining the "test-first" discipline when AI makes it tempting to test-last. Walk away with prompt templates for scenario generation, patterns for AI-assisted test maintenance, and a decision framework for when AI helps versus when it hurts your testing culture. Whether you're using Reqnroll, Cucumber, or SpecFlow with Playwright or Selenium, you'll learn how to leverage AI to write better tests faster—without sacrificing the thoughtfulness that makes BDD valuable.