AI is helping developers write code faster than ever before. But this sudden acceleration creates a dangerous bottleneck in testing, exposing a critical gap when leaders inevitably ask: "How do we actually know this application is ready for release?"

Accelerating code creation without evolving your testing strategy introduces hidden risk. To bridge this divide, engineering teams must embed AI directly into the testing workflow while keeping human judgment and governance at the center of every release decision.

This session explores how to establish a single, seamless flow—moving directly from initial requirements to tested, release-ready outcomes. We will look at how AI can be used to instantly draft test cases, verify coverage, and evaluate release readiness against live results, all while keeping your teams in full control of the final record. Speed never has to come at the cost of trust.

What you’ll take away:

• A Unified Quality Workflow: Move away from a patchwork of disconnected processes and establish a consistent quality standard across every team and project.


• Pragmatic AI Acceleration: Apply AI directly to the testing process—converting requirements into fully tested outcomes in a fraction of the time—without surrendering control.


• Complete Auditability & Traceability: Maintain a trusted system of record with a complete audit trail from initial requirement to final result, giving executives and auditors measurable confidence.


• Scalable Quality Standards: Implement an adaptable quality process that yields the exact same level of confidence whether managed by 1 team or 20.


• Live Q&A: Bring your real-world questions on adopting AI within testing workflows without introducing operational risk.

You’ll leave with a clear roadmap for bringing AI into your testing ecosystem while preserving the control, compliance, and governance your releases depend on.