AI in QA is mandatory. Are you using it?

Most QA teams are experimenting with AI. Very few have changed how testing actually executes.

AI has crossed the “early adopter” stage. Not because it’s a “trend,” but because the way software ships has fundamentally changed. Releases are faster. Systems are more complex. Frontends change weekly and customers have zero patience for bugs that slip into production. If your testing approach is still built around manual effort or brittle automation scripts, long feedback loops, you’re not behind, you’re at risk.

What’s changing on the ground

AI adoption is rising but mostly in small, safe ways: generating test cases, speeding up scripts, and summarizing bugs. But testing execution hasn’t really changed yet:

• Humans still decide what to test
• Engineers still write & maintain scripts
• Failures still need manual investigation
• Coverage still grows slowly

In 2026, AI won’t be optional. The gap will be obvious: teams using AI as a side tool vs teams running testing as an autonomous system. What’s emerging now is the real shift: moving from AI-assisted testing to AI-driven executionwhere testing becomes continuous, adaptive, and intent-led.

What this session is about

This webinar breaks down the gap between “we’re using AI” and “AI is actually driving QA outcomes.” We’ll explore what changes when execution becomes autonomous and where humans still need to stay in control. You’ll also see a live demo of Atto, Testsigma’s AI coworker, in action.

Who should attend

• QA leaders and managers navigating AI expectations
• Test automation engineers and SDETs scaling coverage
• Engineering leaders responsible for release confidence
• Teams tired of flaky tests, slow pipelines, or QA bottlenecks

What you’ll gain

• A clear model of what “real AI usage in QA” looks like
• Practical shifts to make execution continuous and scalable
• How to maintain accountability without slowing delivery
• A real workflow demo not just AI promises