Artificial intelligence (AI) is showing up more and more across the software development lifecycle (SDLC), and QA is no exception. But while AI tools often promise big changes, most QA teams are seeing only small shifts so far.
In the Fourth Edition Software Testing & Quality Report, we asked thousands of QA professionals around the world how they’re using AI, what kind of impact it’s had, and what they think is coming next. The takeaway? AI is starting to influence QA, but adoption is still early, and progress looks very different from team to team.
From early use cases like test generation to ongoing challenges with tool integration and data security, here’s a look at how AI is really taking shape in QA today and where teams see it going next.
How QA teams are using AI testing tools today
Right now, AI’s role in QA is mostly playing out through familiar tools like ChatGPT and GitHub Copilot. According to the report:
- 54% of QA professionals are using ChatGPT
- 23% are using GitHub Copilot
Most teams rely on these tools for things like:
- Generating test cases
- Debugging and code suggestions
- Writing automation code snippets
- Assisting with exploratory testing
These tools are useful for basic support, but most teams still aren’t using AI solutions designed specifically for QA. Tools that manage defect triage, prioritize tests by risk, or detect flaky tests haven’t seen widespread adoption yet.
How AI is (and isn’t) changing QA roles
Even with limited implementation, AI is starting to shift how some QA teams work—but the results are mixed.
For a growing number of testers, AI is helping offload repetitive tasks and make room for more strategic focus:
- 34% say AI has allowed them to spend more time on complex, high-value work
- 29% report better efficiency through automation and test execution support
- 26% have had to learn new tools or upskill to keep pace with AI adoption
But not everyone is seeing a big impact.
- 36% say AI hasn’t meaningfully changed their role
- Many others shared that their organizations haven’t implemented AI or don’t allow it yet
There are also concerns about how this shift could affect job security.
- 14% of testers expressed worry that AI could eventually replace parts of their role
And while some advanced use cases, like predictive analytics and anomaly detection, are emerging, only 20% of respondents report using AI for those kinds of tasks so far.
In short, AI isn’t replacing testers; it’s helping some work more efficiently. But for most, the shift is still just beginning.
Top challenges of adopting AI
Bringing AI into QA workflows isn’t always straightforward. Many teams are running into real obstacles as they try to make it work.
The most common challenges include:
- Data privacy and security concerns (34%)
- Lack of in-house expertise or training (30%)
- Difficulty finding the right AI tools for testing (26%)
- Integration issues with existing tools and systems (22%)
- High implementation costs (21%)
That said, 27% of respondents say they’ve encountered no major blockers, which suggests adoption is becoming more manageable, especially for teams with mature processes and integrated testing platforms.
Future use cases for AI
Right now, most teams are using AI for basic support but looking ahead, respondents highlighted several use cases they hope to see in the next 3–5 years. Some of the most promising future use cases include:
- Self-healing test scripts that adjust automatically to app changes
- AI-generated test cases and scripts based on user stories or logs
- Predictive defect analysis to catch bugs earlier
- Smarter regression testing and test prioritization
- Automated exploratory testing for better coverage
As software release cycles accelerate, these kinds of tools could help QA teams stay ahead by not just reacting to bugs, but preventing them before they happen.
AI adoption in QA is gaining momentum
Even if all teams haven’t fully embraced AI yet, it’s definitely on their radar:
- 86% of respondents say they’re either exploring or already using AI in QA
- 29% are in the early stages, figuring out where AI fits into their workflows
- 9% are fully committed and actively scaling their AI use
- Only 14% have no plans to adopt AI at all
Compared to just a year or two ago, that’s a major shift. AI is no longer seen as a niche experiment: It’s quickly becoming a key part of how QA teams think about the future of testing.
AI in QA isn’t replacing testers—it’s reshaping the future of testing
What this year’s survey makes clear is that AI isn’t here to take over QA. It’s here to change how we approach it.
Right now, most teams are using AI to speed up small tasks or explore new ideas. But as tools mature and infrastructure catches up, we’ll likely see a shift toward predictive testing, proactive defect detection, and more intelligent automation.
QA leaders who invest now in strong processes, centralized management, and human oversight will be best positioned to take advantage of what AI has to offer in the years ahead.
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