Intelligent testing doesn’t replace traditional QA. It keeps the established discipline of test processes, documentation and test design, then changes which tests you run and who reviews the evidence, because AI systems behave differently from conventional software. Their outputs vary, they depend on data, and their behavior can change after release.
“AI performance testing” can mean two things here: testing how well an AI system performs (output quality, model behavior), and using AI to help with testing, including load and performance testing. This article covers both, because the cultural shift is the same. Teams need clearer ownership, stronger test-design skills, and people who still interpret the results.
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What carries over from traditional QA
ISO/IEC TS 42119-2:2025, the technical specification on testing AI systems, starts from continuity. It explains how the ISO/IEC/IEEE 29119 software-testing series and the ISO/IEC 20246 guidance on reviewing work products apply to AI systems. It says conventional software-testing concepts can be applied to AI. The established series already covers:
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- manual and automated testing;
- scripted and unscripted testing;
- test documentation;
- test design techniques, with equivalence partitioning named among those that remain useful.
A team with solid fundamentals therefore isn’t starting over. A team without them gains little from adding an AI tool. (Only the informative parts of the ISO page are public. The full specification may need to be purchased.)
What changes when the system under test is AI
The same specification says it “follows a risk-based approach and uses risks associated with AI systems, and their development and maintenance, to identify suitable test practices, approaches and techniques applicable to AI systems and their components.” In practice, the risk profile shifts in four ways.
Outputs are variable, not fixed
A conventional test asserts one expected result. Many AI outputs are probabilistic, so a single pass or fail says less. Evaluation has to judge quality across many cases, not match an exact string.
The model becomes a test target
ISO lists model testing for cases where model performance is a risk. This sits alongside functional testing of the surrounding system and static reviews and analysis.
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Data representativeness testing is one of ISO’s named examples. If the data doesn’t reflect real users and conditions, a system can pass its tests and still fail in use.
Behavior can change in production
For systems whose behavior may change after release, ISO lists continuous testing. A single release gate is not enough for them.
ISO also puts stakeholders at the center: “Not meeting stakeholder requirements is a major risk for most projects, and as such, is a key consideration in the selection of test approaches.” Risk and stakeholder requirements together decide the test approach. The tooling doesn’t.
Traditional QA and intelligent testing side by side
| Axis | Traditional QA emphasis | Intelligent testing emphasis |
|---|---|---|
| System behavior | Deterministic expected outcomes | Probabilistic or variable outputs judged for quality |
| Test levels | Unit, integration, system, acceptance | Those, plus model, data and production-behavior testing, chosen by risk |
| Test data | Adequate coverage and availability | Representativeness, security, scalability, and whether synthetic data is appropriate |
| Evidence | Repeatable automated and manual checks | Those checks plus human evaluation, domain expertise and documented review |
| Lifecycle | Often concentrated before release | Integrated across development and, where behavior can change, production |
| Ownership | A QA function or phase | Identified stakeholders with defined review points and traceable decisions |
| Team skills | Test design, automation, exploratory testing | Those, plus AI evaluation, performance/load testing, and reviewing AI-generated cases and reports |
The right-hand column adds to the left. Nothing in the evidence supports dropping exploratory testing or established QA practice.
The cultural shift: ownership, review and craft
Ownership has to be explicit
ISO calls for stakeholder identification and AI test documentation in line with the test-documentation standard. Read organizationally, that means someone is named for each risk, review points are defined, and decisions can be traced. Adding an AI assistant to an unchanged process does none of this.
Testers become reviewers of machine-generated artifacts
AI is now commonly used to draft test cases, generate test data and write test reports. A tester’s job then includes checking whether generated cases trace to requirements, whether the data is realistic and safe to use, and whether a report’s conclusions follow from its evidence.
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Test-design skill matters more, not less
The German Testing Board’s 2024 survey analysis found that systematic test-design procedures aren’t consistently used by respondents. It asks whether explicit knowledge of those procedures will decline as AI use grows. That is an open question the survey raises. It is not a measured effect. The implication is editorial but sensible: faster test creation only helps if someone can judge what coverage the generated tests actually provide.
Readiness is uneven between roles
The same survey reports that operational respondents feel less prepared for AI than managers do. It also notes that security and performance outcomes lagged functional satisfaction, with respondents naming IT security, load/performance testing and automation as training needs. The cultural work is therefore capability building and shared quality ownership. Buying a tool doesn’t deliver either.
Where human judgment stays necessary
In Applause’s 2026 Testing AI report, 61% of surveyed organizations relied on human input to evaluate AI performance, and 33% used LLM-as-judge methods. Applause is a testing vendor, so read this as a snapshot of its respondents, not a universal benchmark. It does show that automated and model-assisted evaluation are used alongside people, not instead of them.
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Chris Munroe, Applause’s VP of AI Programs, put the risk this way: “Without human oversight, you risk reinforcing the same blind spots you’re trying to detect.” That is a vendor executive’s view, not a standards requirement. The logic is still sound. A model grading a model shares some of its weaknesses, so someone with domain knowledge should periodically check the grader.
The same report found that 40% of surveyed users reported hallucinations, up from 32% in Applause’s 2025 survey. This is a self-reported user experience measure, not an independent model benchmark, but it shows that output quality is a live problem for users.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How widely is AI used in testing?
The surveys below have different populations, dates and definitions. They can’t be added together or compared directly.
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|---|---|---|
| German Testing Board, Software Testing in Practice and Research (discussed by ASQF/SQ Magazine, 2025) | German-speaking world, conducted September 2024 | Around a third of operational-area respondents reported current use or near-term plans for AI in software testing tasks |
| Capgemini, World Quality Report 2025–26 | Organizations surveyed for the report | 43% experimenting with generative AI in QA; 15% had scaled it enterprise-wide |
| Applause, 2025 State of Digital Quality in AI | Applause survey respondents | Most-reported QA uses of AI: test case generation (66%), test-data text generation (59%), test reporting (58%) |
The Capgemini gap between experimenting (43%) and scaling (15%) is the most useful signal here. Trying AI in QA is common. Making it a dependable, organization-wide practice is much rarer. A 2025 secondary mapping study on arXiv, Expectations vs Reality: A Secondary Study on AI Adoption in Software Testing, points the same way. In the industry-context studies it reviewed, actual implementations and observed benefits remained limited compared with the many proposed use cases. As a secondary study, it is bounded by its own search and study-selection choices.
What gets in the way
- Test data. In the World Quality Report 2025–26, 60% of organizations struggled with secure, scalable test data.
- Tool adoption. The same report says 58% cited challenges adopting AI-powered tools.
- Training. In the German survey, as reported by ASQF/SQ Magazine, 72% of operational employees wanted further training on testing with AI, and 56% saw a need for training on testing AI.
- Performance and load testing skills. In the same survey, 35% of operational staff named load/performance testing as a further-training need. So the “AI performance testing” topic is also a plain skills gap in a traditional discipline.
What the evidence doesn’t show
Adoption percentages say nothing about outcomes. None of the sources establishes that AI adoption improves software performance, reduces defects or eliminates QA roles. Applause and Katalon publish company-sponsored surveys, so treat their results as attributed snapshots, not audited market facts. The German survey covers its own respondents. Capgemini’s figures describe its surveyed group in a given year. No neutral, industry-wide census of AI-enabled QA exists in these sources.
A practical sequence for making the shift
- List the risks of the specific system. Include model performance, data representativeness, behavior drift in production, security and unmet stakeholder requirements.
- Name the stakeholders and owners. Record who decides what “good enough” means and who signs off.
- Keep the fundamentals. Maintain test plans, traceability and design techniques such as equivalence partitioning for the conventional parts of the system.
- Add AI-specific levels only where the risk calls for them. That could mean model testing, data representativeness checks, or continuous testing in production.
- Use AI to draft, and people to approve. Require review of generated test cases, test data and reports against requirements before they count as evidence.
- Combine evaluation methods. Use automated checks and model-assisted grading for scale, with human evaluation and domain expertise sampling the results.
- Invest in the skills the surveys flag. Test design, testing AI systems, load/performance testing, security and secure test data.
The principle behind the sequence is the one ISO states. Start from risk and stakeholder requirements, choose test levels and evidence to fit, and keep accountable people responsible for interpreting what the tests show.
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