Current surveys point to three prominent test automation trends: teams are using AI to help create test cases and scripts, seeking faster feedback, and preparing to test AI-enabled products. But adoption is not the same as enterprise-wide deployment, and more automation does not by itself demonstrate better software quality. The useful question for a team is whether its tests remain relevant, reliable, maintainable, and connected to the behavior they are meant to verify.
What the latest surveys say about AI in testing
AI-assisted authoring is a leading reported use, but the percentages below come from different surveys and populations. They are not a single, comparable measure of industry adoption.
AI-assisted test work is common in Applause’s 2026 survey
Applause’s August 2026 survey and interviews found that more than 92% of respondents used AI in the testing process, compared with 60% in its prior-year benchmark. The press release says 89% reported that AI had changed how they test digital experiences and apps, while 8% said they did not use AI for any aspect of testing. These are findings from Applause’s respondents, not a census of testing teams worldwide. Applause’s 2026 survey announcement.
In the report’s use-case results (n=186), respondents reported using AI for:
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- Creating test cases: 65.1%.
- Creating test automation scripts: 62.4%.
- Identifying and addressing coverage gaps: 48.4%.
- Analyzing outcomes and recommending improvements: 43.5%.
- Autonomous test execution and adaptation: 36.6%.
The pattern suggests that AI is being applied across test design, scripting, analysis, and execution, rather than only to replace manual test steps. It does not establish that those applications improved product quality.
Experimenting is not the same as scaling
The Capgemini and Sogeti World Quality Report 2025–26 says 43% of organizations were experimenting with Gen AI in QA, while 15% had scaled it enterprise-wide. The report also identifies secure, scalable test data (60%) and adopting AI-powered tools (58%) as challenges. Those figures describe the report’s edition and should not be read as timeless adoption rates.
The same report says synthetic data use in testing rose from 14% in 2024 to an average of 25% in 2025. It ranks Gen AI as the top skill for quality engineers at 63%, followed by core quality engineering skills at 60%; verbal and written soft skills were fifth at 51%. In practice, AI familiarity sits alongside test design, data readiness, and the ability to explain risks and findings.
A smaller practitioner poll highlights expected priorities
VALA surveyed 65 testing professionals at RoboCon in February 2026. VALA describes the poll as a small snapshot, and respondents could select multiple options. For 2026, selections included AI-driven test automation (78.5%), faster feedback (50.8%), containerized automation (35.4%), testing AI-native systems (35.4%), shift-left automation (33.8%), and security test automation (27.7%). These responses are practitioner selections, not organization-wide adoption figures. VALA’s survey and trend discussion.
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Another vendor survey offers a separate snapshot
Katalon’s State of Software Quality 2025 page reports that 76% of respondents used AI-powered tools in software testing and 56% of QA teams struggled to keep up with testing demands. Its survey differs in year and population from Applause’s and the World Quality Report’s; the percentages should not be combined into one trend line.
What these trends change in day-to-day testing
Test design and scripting become assisted tasks
Generating candidate test cases or automation scripts can help teams address repetitive authoring work and explore coverage. A generated test still needs a clear objective, relevant inputs, and a check that would fail when the intended behavior breaks. Teams should review the assertions and edge cases rather than treating a plausible-looking script as evidence of coverage.
Faster feedback depends on useful signals
More frequent or faster test runs are valuable only if they help developers distinguish real regressions from flaky tests, environment problems, and irrelevant failures. Track whether a test catches meaningful defects and whether teams can diagnose its result; raw execution volume or speed alone cannot answer that.
AI-enabled products add new targets for quality checks
VALA respondents’ interest in testing AI-native systems points to a distinct concern: a product whose behavior depends on AI may need evaluation beyond fixed expected outputs. Teams should define acceptable behavior, risks, and review criteria for the particular product. The survey records interest, not a settled universal method.
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Why human judgment and test intent still matter
In Applause’s 2026 survey, 86.1% considered human involvement extremely important to functional testing, and another 13.4% considered it somewhat important. This does not mean every test must be manual. Human judgment is especially important for domain context, exploratory testing, user experience, and checking whether generated or repaired tests still verify the intended behavior.
Applause CTO Tacita Morway warns that an AI system may respond to a failing test by changing the test so it passes without checking the behavior it was meant to verify. She argues that safe self-healing requires understanding test intent, so automation can adapt to legitimate application changes without producing false positives or gaming the result. The practical safeguard is to review changes to assertions and test logic as carefully as application changes.
Speed also needs a quality measure. Morway notes that fast generation can create noise if tests are not relevant, reliable, or maintainable; useful results depend on the testing knowledge and context provided to the system. Teams should evaluate the signal their AI-assisted workflow produces, not just how many tests it creates.
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How to assess an automation trend before adopting it
- Name the problem. Specify whether the bottleneck is test authoring, slow feedback, coverage, maintenance, data, or validation of an AI-enabled feature.
- Start with a bounded workflow. Choose a representative but limited application area and define the expected behavior and failure conditions before introducing AI assistance.
- Protect test data. Decide how test data is created, secured, refreshed, and kept representative. The World Quality Report 2025–26 identifies secure, scalable data as a major reported challenge.
- Keep accountable review. Have a tester or engineer inspect generated tests, repairs, and outcome recommendations, including whether assertions still capture the product requirement.
- Measure quality as well as speed. Monitor meaningful defect detection, false alarms, flaky failures, maintenance effort, and time to diagnose. A quicker run is not useful if its result is untrustworthy.
- Scale only after the workflow earns trust. Set ownership, access controls, review practices, and operating guidance before expanding beyond the initial team or use case.
What the defect figures do—and do not—show
Applause’s 2026 press release says 29% of respondents reported that the number or severity of functional testing defects had increased, and 15% reported increases in both number and severity. A separate question in its companion report, answered by n=197, found 26.4% said both the number and severity of issues reaching production decreased. The questions and measures differ. Neither result establishes that AI caused the reported outcome.
For that reason, teams should treat adoption and quality outcomes as separate questions: whether people use AI in testing, and whether a defined quality measure changes under a clearly described workflow. Survey percentages alone cannot establish causation.
Capture website evidence for browser-based tests
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Or skip the browser setup
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Quick Recap
How to read the trend figures responsibly
- Keep each figure attached to its publisher, survey year, and population; do not turn vendor or attendee surveys into universal rates.
- Separate current use, organizational experimentation, enterprise scaling, and expectations about the future.
- Compare what was measured: tool use, a selected trend, reported defects, or a skill priority are different questions.
- Do not infer that AI caused a quality outcome from survey responses alone.
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