AI is writing your code faster than your team can test it, and that gap is exactly what an exec or release manager exposes when they ask, "how do we know our application is ready?"

To close this divide, organizations must rethink their workflows and bring AI test management directly into the systems where teams already plan and build. This session will explore how to transition from a patchwork of fragmented tools to a single, governed flow—moving seamlessly from a requirement to a tested, release-ready outcome. By maintaining a complete, auditable record at every step, teams can ensure that development speed never comes at the cost of trust.

Following a discussion on industry best practices for AI-driven QA, the session will feature a brief demonstration of how SmartBear Zephyr, the Jira-native testing system of record puts these concepts into action. You'll see how the Zephyr Agent for Rovo generates test cases from requirements and checks coverage before a release, all while a human stays in complete control.

What you’ll take away:

• Strategies for building one governed workflow, rather than a patchwork of tools, so quality stays consistent across every team and project.


• Best practices for applying AI to the testing work itself, turning requirements into tested outcomes in a fraction of the time, without giving up oversight.


• How to establish a trusted system of record, with a full, auditable trail from requirement to result, so audits, releases, and compliance rest on measurable confidence.


• Methods for creating a standard you can scale, ensuring the same process and confidence whether one, or 20, teams run it.


• Live Q&A: Bring your questions on adopting AI in testing without adding risk.

You’ll leave knowing how to bring AI into your testing workflows without giving up the control your releases depend on.