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.
TL;DR
- AI adoption in QA is growing, but its impact varies widely between teams and remains relatively early for many organizations.
- In TestRail’s Fourth Edition Software Testing & Quality Report, ChatGPT and GitHub Copilot were among the most commonly reported AI tools, with respondents using AI for test-case generation, debugging, code suggestions, automation snippets, and exploratory-testing support.
- Some respondents reported spending more time on higher-value work and improving efficiency with AI, while 36% said AI had not meaningfully changed their role.
- Data privacy and security, lack of expertise, difficulty finding appropriate tools, integration challenges, and implementation costs were among the most commonly reported barriers to AI adoption.
- Teams are interested in future applications such as self-healing automation, AI-generated tests and scripts, predictive defect analysis, smarter regression-test prioritization, and more AI-assisted exploratory testing.
- The report found that 86% of respondents were at least considering or implementing AI in QA, while 14% reported no plans to adopt it; this does not mean that 86% were already actively using AI.
- AI can accelerate parts of the testing process, but generated output still requires human review, product knowledge, and validation rather than being treated as automatically correct.
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.
TestRail’s AI Test Case Generation creates comprehensive test cases through intelligent collaboration with AI and an intuitive three-step workflow that maintains unprecedented human control and flexibility:
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 adoption in QA is gaining momentum.
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.
Image: Manage all of your tests in one place to gain full visibility into your testing and centralize your testing activities to ensure consistency across the testing process.
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Frequently Asked Questions
What is AI in software testing?
AI in software testing refers to the use of artificial-intelligence technologies to assist with testing activities such as generating test ideas or test cases, producing automation-code drafts, analyzing failures, identifying patterns, and supporting other QA activities.
The exact capabilities depend on the tool being used.
How are QA teams currently using AI?
According to TestRail’s Fourth Edition Software Testing & Quality Report, common reported uses include generating test cases, debugging and code suggestions, writing automation-code snippets, and assisting with exploratory testing.
The survey found ChatGPT was used by 54% of respondents and GitHub Copilot by 23%. These figures describe respondents to that particular survey and should not be interpreted as universal adoption rates across the entire software-testing industry.
Are 86% of QA teams already using AI?
No.
The report’s 86% figure combines respondents who were considering AI or implementing it at small, medium, or full scale. Specifically, 29% were considering it, 25% reported small-scale use, 23% medium-scale use, and 9% full-scale adoption. Another 14% reported no plans to adopt AI.
So it is more accurate to say that 86% were considering or implementing AI, rather than that 86% were already actively using it.
Is AI replacing software testers?
There is not enough evidence in this survey to conclude that AI is broadly replacing software testers.
In the report, 36% of respondents said AI had not meaningfully changed their role, while 34% said it had enabled them to spend more time on complex, higher-value work. Fourteen percent expressed concern that AI could eventually replace parts of their role.
AI may change particular testing tasks and skill requirements, but its impact on employment depends on the organization, role, technology, and way AI is adopted.
How is AI changing QA roles?
Among respondents to the report:
- 34% said AI allowed them to spend more time on complex, high-value work.
- 29% reported efficiency improvements through automation and test-execution support.
- 26% said they had needed to learn new tools or upskill.
- 36% said AI had not meaningfully changed their role.
This indicates that the impact is uneven rather than the same across all QA teams.
What are the biggest barriers to adopting AI in QA?
The report identified several commonly reported challenges:
- Data privacy and security concerns: 34%
- Lack of in-house expertise or training: 30%
- Difficulty finding appropriate AI testing tools: 26%
- Integration problems with existing tools and systems: 22%
- Implementation costs: 21%
Twenty-seven percent of respondents reported no major blockers.
Why are data privacy and security concerns important when using AI for testing?
AI tools may process requirements, source code, test data, defect information, logs, or other potentially sensitive material.
Teams should understand what data is being sent to an AI service, how that data is processed and retained, and what their organization’s security, privacy, contractual, and regulatory policies permit.
Sensitive or confidential information should not simply be submitted to an AI system without appropriate approval and controls.
Can AI generate test cases?
Yes. AI systems can generate draft test cases from information such as requirements, acceptance criteria, workflows, and other contextual input.
However, generated tests may contain incorrect assumptions, missing scenarios, irrelevant cases, or inaccurate expected results. Human review remains necessary before relying on them.
TestRail’s official guidance explicitly tells users to review, validate, and refine AI-generated output.
Can TestRail generate test cases with AI?
Yes. TestRail Cloud currently provides AI-assisted test-case generation.
Users can provide product requirements, review and edit suggested test cases, select the cases they want, and then generate detailed cases using supported mapped fields such as steps, expected results, descriptions, or BDD scenarios.
Is TestRail AI test-case generation available on TestRail Server?
No. TestRail’s current documentation states that its AI functionality is available in TestRail Cloud only.
Does AI-generated test-case creation automatically execute those tests?
No.
Test design and test execution are separate activities. TestRail’s AI test-case generation creates test cases that are saved to the repository. Those tests still need to be executed through the appropriate manual or automated testing process.
Can AI generate test automation code?
Yes, some tools can generate automation-code drafts.
TestRail Cloud currently has a separate Automate with AI capability that creates a first draft of automation code from a manual test case. TestRail’s documentation specifically states that the generated code is intended to be reviewed, tested, and refined by a human rather than treated as a finished solution.
Does AI-generated automation remove the need for automation engineers?
No.
Generating code is not the same as building and maintaining a reliable automation system. Engineers still need to validate assertions, selectors, data, environment assumptions, framework configuration, security considerations, maintainability, and whether the generated test actually verifies the intended behavior.
AI can reduce some of the work involved in producing a first draft, but it does not eliminate engineering judgment.
What is self-healing test automation?
Self-healing automation generally refers to automation systems that attempt to adapt when certain elements used by a test change—for example, by finding an alternative UI locator after the original locator stops working.
It can reduce some maintenance in appropriate situations, but it is tool-dependent and cannot guarantee that a changed test is still validating the correct behavior. Human oversight is still important.
Can AI predict software defects?
AI and machine-learning systems can be used to analyze historical information and identify patterns associated with higher-risk areas or likely failures.
That does not mean AI can reliably predict every future defect. Predictions depend heavily on the available data, the model, the system being tested, and how predictions are validated.
In TestRail’s survey, only 20% of respondents reported using AI for advanced activities such as predictive analytics and anomaly detection.
Can AI prioritize regression tests?
AI-assisted systems can potentially use information such as code changes, historical failures, dependencies, and risk to help recommend which regression tests should receive priority.
This can support risk-based testing, but AI prioritization should not automatically be treated as proof that unselected tests are unnecessary.
Can AI perform exploratory testing?
AI can assist exploratory testing by suggesting scenarios, generating test ideas, analyzing session information, or identifying areas worth investigating.
However, exploratory testing relies heavily on learning, observation, questioning, interpretation, and adaptation during testing. AI assistance therefore does not necessarily replace human exploratory testing.
Can AI detect flaky tests?
Some AI- and analytics-based tools can identify patterns associated with flaky tests by analyzing repeated execution results, timing, failure patterns, or environmental information.
The ability to identify the underlying cause and correct the test still depends on the tool, available evidence, and engineering analysis.
Does AI always improve test coverage?
No.
AI can suggest additional scenarios and help testers think of cases they might otherwise overlook, but generated test quantity does not automatically equal meaningful test coverage.
Coverage depends on whether tests address the correct requirements, risks, behaviors, platforms, conditions, and failure modes.
Can AI prevent defects before they happen?
Not literally.
AI can potentially help teams identify risks, ambiguous requirements, suspicious code changes, historical defect patterns, or likely problem areas earlier in the SDLC. Earlier detection may reduce the likelihood of some defects reaching later stages or production.
It is therefore more accurate to describe AI as potentially supporting earlier defect detection and prevention activities, rather than claiming that it can simply prevent bugs before they happen.
Why is human oversight still important in AI-assisted testing?
AI systems can produce incomplete, inaccurate, irrelevant, or misleading output.
Human testers provide product context, domain knowledge, risk judgment, critical thinking, and accountability. TestRail’s current Responsible AI Use Guide explicitly advises users to review, validate, and refine AI-generated suggestions before relying on them.
What skills will QA professionals need as AI adoption grows?
The exact skills will depend on the role, but useful areas can include:
- Evaluating AI-generated output critically
- Writing clear requirements and contextual instructions
- Test design and risk analysis
- Automation and programming fundamentals
- Understanding data privacy and security
- Investigating failures rather than accepting AI explanations automatically
- Understanding the limitations of the AI tools being used
The TestRail survey already found that 26% of respondents had needed to learn new tools or upskill as AI adoption increased.
What is the future of AI in QA?
The survey respondents identified areas they expect or hope to see develop further over the next three to five years, including self-healing tests, AI-generated test cases and automation scripts, predictive defect analysis, smarter regression-test prioritization, and more AI-assisted exploratory testing.
These are anticipated or developing use cases, not guarantees about what every QA team will adopt or what every AI testing tool can currently do.
What is the best way for QA teams to start using AI?
A practical approach is to begin with clearly defined, lower-risk use cases where AI output can easily be reviewed—for example, brainstorming test scenarios or producing first drafts of test cases.
Teams should establish rules for what data can be shared with AI systems, keep humans responsible for validation and decisions, measure whether the tool is actually improving the process, and expand usage only when the results justify it.
