It’s no surprise that AI has had a huge impact on both development and, in turn, QA. QA teams across the world have seen their industry change in a big way. Earlier this year, we put out our 2026 predictions, and AI came up in every single one of them. Halfway through the year, that’s proving true, with AI making big moves across the industry.
The Bottleneck Has Shifted
The bottleneck of testing isn’t what it used to be. For years, enterprises relied on automation, but that automation still required QA teams to manually write the test scripts themselves. The execution was automatic, but the creation was not. The shift from manual testing to automation was a big change on its own. It was better than manual testing, but it still couldn’t validate everything, and it came with its own maintenance problems. That model held for almost twenty years, and it had issues of its own.
AI has changed that dramatically. It can generate code, create tests, analyze defects, and help with automation, meaning AI is now doing the writing QA teams used to do by hand. The QA engineer’s role has shifted from writing to guiding, validating, and governing AI-generated work. And because AI is producing more code, faster, the speed needed to validate it has grown too. AI has shifted the burden from writing test code to validating AI-created development code, but the burden of time and speed still falls on QA teams. With so much coming from AI, the question now is: how do we validate all of it? Writing code used to be the bottleneck for QA teams. Now it’s evaluating what AI produces.
AI is changing QA in a lot of ways, but a few stand out as the ones really reshaping how QA teams work and how they impact the business.
What AI Is Solving
Beyond the obvious, like drafting test code, a few shifts stand out:
- Testing ahead of code – QA can validate tests before code is even finished, speeding up release cycles and improving business retention.
- Higher quality output – AI-generated code tends to be lower quality, so QA teams testing it more closely are catching issues earlier and shipping better product.
- AI checking AI – a second AI validates the first AI’s output against what was expected, essentially double-checking the payload.
- Performance tuning – AI can slow applications down, so teams tune prompts to keep performance in check.
- Service virtualization – not every test needs to hit the AI directly. Virtualizing it cuts cost and lets teams test more often, calling the real AI only when needed.
- Autonomous testing – AI agents that understand context well enough to create, maintain, and optimize tests on their own, sometimes straight from a spec, then hand feedback back to developers.
Traditional testing always had its weaknesses, things we couldn’t fully validate, like whether a chart’s data actually matched what it displayed. AI closes a lot of those gaps, which is part of why it’s making some older testing approaches feel obsolete. While gaps are being closed with AI, there are a few things in QA that will never change.
What AI Isn’t Solving Yet
No matter what, QA teams are still doing manual testing and scripting, for rarely run tests, ad hoc scenarios, and complex UIs. Many are trying to move to scriptless testing, but the majority are still writing scripts, held back by AI’s current limits in handling scriptless testing well. Natural language script creation is still getting stuck. There’s been some improvement, but AI hasn’t fully solved the maintenance problem. Tests still break when the application changes, and someone still has to go fix them. AI is helping, but it hasn’t gotten testing teams out of that upkeep work yet. Adding another AI tool won’t necessarily fix this either, AI still needs human governance and orchestration to actually work. So, do QA teams still have to do manual testing? Yes.
Even so, AI has pushed QA teams above and beyond how they used to be perceived. They used to be seen as a bottleneck holding up release cycles. Now they’re the validators development teams need.
Why QA Still Matters
QA teams are now on the front line of “trust but verify.” They let AI do the groundwork and spend their time validating. That makes QA more of a value-add to the business than a bottleneck. It’s a real shift, QA used to be seen as something to wait on before release. Now, because AI is producing so much code and trust in it is still earned, QA’s role in catching errors matters more than ever. So, is traditional testing obsolete? In some ways, yes, but AI has changed how and what QA teams are doing, not eliminated the need for them.
AI might feel intimidating to some QA leaders and teams, but there’s real reason for excitement here, given how much it’s elevating the role going forward.
The Future of QA
The future of QA isn’t about testers being replaced by AI. It’s about the role becoming more strategic. AI can handle execution, while testers and QA professionals can now spend their time on what really matters: validating and working firsthand with AI results, shaping smarter testing strategies, and proving business outcomes like faster delivery.
2026 is proving out what many predicted: AI isn’t just a testing tool, it’s reshaping how QA teams work day to day. In our own AI-accelerated testing engagements, we’ve seen this shift firsthand, helping teams cut manual regression effort by 80%, improve regression coverage by 65%, and reduce manual testing costs by more than 50%.
At VIP, we help clients build the governance, testing frameworks, and validation practices needed to trust what AI is producing, without losing the speed AI is supposed to deliver in the first place. As QA’s role shifts from bottleneck to safeguard, we’re helping teams make that transition with real, measurable results.
If you’re rethinking your testing strategy for the AI era, we’d love to talk. Reach out to us at commercial@trustvip.com.