AI in Testing vs Testing of AI
If you’ve ever heard the terms “AI in Testing” and “Testing of AI” and felt a wave of confusion wash over you—you’re not alone! They sound similar, but their meanings (and impact on modern QA) are worlds apart. Let’s clear up the confusion and spotlight how these concepts shape the present and future of quality assurance.
AI in Testing: Smarter Testing with Artificial Intelligence
AI in Testing refers to leveraging AI and machine learning technologies to make the software testing process faster, smarter, and less error-prone. Imagine regression test suites where flaky locators heal themselves, or test cases that write themselves by reading your requirements, or analytics that tell you which tests to prioritize to catch bugs early. That’s AI working as your sidekick in the testing workflow.
Examples:
Self-healing UI automation (auto-fixing selectors)
Intelligent test case generation and risk-based prioritization
AI-powered visual and UX regression tests
Predictive analytics on defect trends and testing ROI
AI here isn’t what’s being tested—it’s the tool that empowers you to test better!
Testing of AI: Assuring Trust in AI-Driven Products
On the flip side, Testing of AI means putting AI systems themselves under the microscope. As AI is increasingly embedded in products—think chatbots, recommendation engines, fraud detection, even self-driving cars—the stakes are high. Here, the role of the tester is to ensure that these intelligent systems are accurate, robust, explainable, fair, and safe.
What’s unique about Testing AI?
Unlike traditional software, AI/ML models are non-deterministic and data-driven. Their output isn’t always the same, and “correctness” can be ambiguous. This calls for new validation approaches:
Checking data quality and label accuracy
Evaluating model bias, fairness, and explainability
Robustness checks under edge cases, adversarial and real-world scenarios
Ongoing validation post-production (to catch degradation or drift)
Using NIST’s AI Risk Management or ISTQB CT-AI frameworks for lifecycle assurance
Here, AI is the subject under test—can we trust it in the real world?
Why You Need Both
Smart QA teams embrace both. AI in Testing helps you cope with fast releases and complex UIs. Testing of AI ensures your AI-driven features don’t put customers, business, or brand at risk. In a world where every product is becoming smarter, both approaches are now QA essentials.
In summary:
Use AI to test better. But don’t forget to rigorously test your AI. Each stream addresses a unique challenge—and together, they future-proof your QA!
Share this edition with team members or leaders who still use “AI in Testing” and “Testing of AI” interchangeably—it might just clear up a major misconception!