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More than 92% of respondents to Applause’s August 2026 survey said they use AI in testing, while 29% reported an increase in the number or severity of functional testing defects. The findings describe adoption and respondents’ reported quality trends; they do not show that AI caused defects to rise. Applause released the results on September 30, 2026.
What the survey says about AI adoption and defects
Applause’s release compares the current result of more than 92% with 60% in its prior-year benchmark. The report page gives the 2025 figure more precisely as 59.6%. These are survey responses, not a census of all organizations, and adoption alone says nothing about whether tests are correct, prevent defects, or deliver a return on investment.
The release says 29% of respondents reported an increase in the number or severity of functional testing defects. That broad measure includes respondents who reported an increase in either dimension; 15% said both defect number and severity increased. On the report’s separate production-quality breakdown, 14.7% reported both rising, while 26.4% reported both decreasing. The production breakdown uses a different question and respondent base, so its figures should not be substituted for the release’s broader 29% measure.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →These results do not establish why defects changed. They do not show that AI use caused defects to increase, or that teams using AI had worse outcomes than teams that did not.
What respondents use AI for in testing
Among respondents answering the AI testing use-case question (n=186), the leading uses were:
| Reported use | Respondents |
|---|---|
| Creating test cases | 65.1% |
| Creating automation scripts | 62.4% |
| Identifying or addressing coverage gaps | 48.4% |
| Analyzing results and recommending improvements | 43.5% |
| Autonomous execution or adaptation | 36.6% |
These are use cases respondents reported, not independently verified measures of accuracy or effectiveness. The figures also do not mean every respondent uses AI for each task.
Why human judgment remains important
In a separate question, 86.1% of respondents considered human involvement extremely important to functional testing; another 13.4% considered it somewhat important, and fewer than 1% said it was not at all important. This measure concerns perceived importance, not a controlled comparison of human-only and AI-assisted teams.
Automation is well suited to repeatable checks: it can run defined steps and report whether expected conditions were met. People contribute judgment when the question is whether a test reflects real user behavior, business logic, accessibility, or a confusing interface—and when an unexpected result needs investigation rather than a simple pass or fail.
Tacita Morway, Applause’s chief technology officer, distinguishes the roles this way: “Traditional automated testing answers the question: can this task be completed? A human tester answers a harder one: could a real person work out how to do this, and get it done?”
Morway also warns that a poorly governed AI-powered test system can optimize for task completion rather than preserving the intended check. In Applause’s report, she says an automated test may stop when it fails, or an AI system may alter the test so it passes without checking the behavior it was meant to verify. This is a vendor executive’s caution, not a finding that the survey measured such incidents.
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How to interpret the findings
Applause says the survey was conducted in August 2026 among uTest community members and other professionals in software development, QA, product, AI, and data science; it also interviewed technology leaders. The public report does not provide enough sampling-frame detail to establish that respondents represent the wider population of organizations. The results are self-reported, and question denominators differ: the report lists n=186 for AI testing use cases, n=197 for production quality, and n=202 for human judgment, among other question-specific samples.
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For a QA team, the useful takeaway is not that AI adoption guarantees better or worse software. Treat AI as a way to assist with repeatable work and analysis, then keep human review focused on whether coverage matches user needs, whether results preserve the test’s intent, and what unusual failures mean. Monitor defect trends directly rather than inferring quality from AI adoption.
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