From Ai-Generated Tests to Real Quality

Speaker: Tatyana Arbouzova | LinkedIn

Topic: From Co-Pilot to Colleague, at Enterprise Scale

Innovate QA community is going to Vancouver, Canada and partnering with Ministry of Testing local chapter on our October meetup. Hope to see you there.

Agenda:
🕕 6:00–6:45 PM PST — Networking
🕕 6:45–7:30 PM PST — Tatyana Arbouzova - From Co-Pilot to Colleague, at Enterprise Scale
🕕 7:30–8:00 PM PST — Networking

Summary :
AI can generate tests. But can it deliver quality?

For most teams, the honest answer is “not yet.”

AI co-pilots have made individual testing tasks dramatically faster—from generating tests based on requirements, tickets, designs, and API specifications to running tests across web, mobile, and API and investigating failures down to root cause.

But as enterprise teams adopt these tools, a gap becomes clear: every step may be faster, yet a person still has to start every one. Someone has to spot the risky PR, choose the right tests, triage failures, and follow up on the fix.
We automated the tasks. We hadn't automated the initiative.

In this session, Tatyana Arbouzova will explore the journey from AI as a co-pilot that waits for human direction to AI acting more like a colleague that can initiate and drive quality activities.
The session will look at what enterprise AI co-pilots do well, where they still fall short, and what enterprise teams have learned as they move toward more autonomous quality engineering.

You'll also see a live walkthrough of this shift in action: a PR opens, impact analysis runs automatically, tests are generated and executed from API through UI, a regression is identified, and the root cause reaches GitHub or Slack before anyone goes looking for it.

You'll leave with a simple question to take back to your own team:
Where does your AI still wait for a human to start the work?

Then come find us at the ContextQA booth to see how far that answer can go.

Takeaways:
What attendees will learn:

1. A clear model for the two stages of AI in quality engineering: the co-pilot that speeds up human work and the colleague that initiates it.
2. Real lessons from enterprise teams about where AI-assisted testing still depends on people to start, prioritize, and follow through.
3. An end-to-end view of autonomous quality in action, from PR to impact analysis to tests to root cause, running on an enterprise-grade platform.
4. A practical way to identify the next step in your own team's AI adoption without replacing what already works.

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