A great way to start is by picking a small but high-impact regression suite that your team runs often. Automate that part first. Then, track how much time your team saves and how many defects you’re able to catch earlier in the process. These are easy metrics to measure and clearly show leadership the value of investing further, it’s quick, practical, and gives visible ROI right from the start.
Here’s how we’ve seen it work in a real project, take our mobile banking app, for example. We used GenAI to automatically create regression tests across three different platforms. It didn’t just stop there, the system was smart enough to spot flaky test scenarios, point out what might be causing them, and even suggest possible fixes. This approach helped us cut down test maintenance time by nearly 40%, which made a huge difference in keeping releases faster and smoother.
GenAI can definitely help push testing earlier in the development cycle by automating tasks like test case generation, code reviews, and early defect detection. But human involvement is still essential, especially when it comes to understanding business context, prioritizing risks, and validating real-world scenarios. In short, GenAI can scale the shift-left approach, but quality outcomes still need a human touch to ensure everything aligns with user needs and project goals.
Yes, it’s a great idea to maintain a shared glossary of testing and AI-related terms. It really helps new team members get up to speed faster and keeps everyone on the same page when discussing concepts or using specific tools. Having a common reference point also avoids confusion and makes team communication much smoother.
Agentic AI can really change the way testers approach exploratory testing. Instead of following fixed scripts, it can simulate different user journeys, explore unexpected paths, and uncover issues that might never appear in traditional automation runs. It’s like having a smart testing assistant that learns from patterns, experiments with variations, and spots unusual behavior or defects before they impact real users. This helps testers focus more on understanding why issues happen rather than just where they occur.
Both are important in their own way. Learning the basics of how AI works gives you a stronger foundation, it helps you understand why certain tools behave the way they do. At the same time, getting hands-on with AI-powered tools teaches you how to apply that knowledge in real testing scenarios. So, start by learning the fundamentals, and then explore the tools that bring those concepts to life in your day-to-day quality engineering work.
Yes, that’s definitely possible. With agentic AI, non-testers can trigger or create quality checks just by using natural language, no need to write scripts or know complex tools. It really opens up testing to a much wider group of people.
However, while this makes things faster and more accessible, it doesn’t remove the need for QA professionals. The results should still be reviewed and validated by QA experts to ensure accuracy and reliability. Think of it as collaboration, AI helps simplify and scale the process, but the human expertise ensures that what’s being tested truly meets the quality standards.
Keeping up with AI trends can feel overwhelming, but the best way is to stay curious and consistent. Follow a few trusted industry blogs and experts who break down complex updates into real-world insights. Join webinars and community discussions, they’re great for hearing how others are actually applying new ideas in testing. And most importantly, experiment on your own. Even small hands-on trials help you understand what really works instead of just reading about it.
In the AI era, QA success will be measured by more than just how many tests we run. Teams will start focusing on how effectively testing adapts and adds real value. Key metrics will include how much test coverage has evolved through automation, the accuracy of AI-driven defect detection, and the time saved in each test cycle. We’ll also look closely at how much test flakiness has been reduced and how well our predictive testing helps us catch issues before they impact users. These KPIs together show how efficiently and intelligently a QA team is operating in this new landscape.
Start small, pick a pilot project where you can test out how AI or GenAI fits into your QA process. Once you see some results, integrate it with your CI/CD pipeline so it becomes part of your regular testing workflow. Keep an eye on key metrics like test coverage, defect detection rate, or time saved to measure the real impact. As you gain confidence, expand the use of AI across more areas, but always keep human oversight to validate outcomes and ensure quality stays intact.
If you’re testing customized software, there isn’t a single “best” app, it really depends on what you’re testing. For example, tools like Postman are great for API testing, while Selenium and Playwright work well for automating web application tests across different browsers. If you’re looking for something with a more visual, low-code setup, Testim can help streamline UI testing.
A good approach is to use a combination of these tools so you can cover everything, from front-end behavior to backend performance, and make sure your application runs smoothly in all environments.
That’s a great point, and it’s something a lot of people in testing are thinking about right now. AI is fantastic at speeding up repetitive tasks, predicting defects, and even helping generate test cases faster. But testing isn’t just about automation , it’s about context, creativity, and understanding how users actually interact with a product.
AI can take care of the heavy lifting, but it still needs human testers to guide it, to ask why something should be tested a certain way or what edge cases really matter. So rather than replacing testers, AI becomes more like a smart assistant that helps us move faster and focus on higher-value testing work.
In short, AI can accelerate testing, but true autonomy still needs human insight to make the right decisions.
The architecture is built to make testing flexible and adaptable across different environments. It uses containerization, virtual devices, and pre-defined environment templates, so your tests automatically adjust whether they’re running on different OS versions, device types, or even switching between cloud and local setups. This means you don’t have to rewrite or reconfigure tests every time your environment changes, everything just works smoothly wherever you run it.
One of our recent wins was automating the login flow regression tests across five different mobile devices. It used to take quite a bit of manual effort every release, but now the whole process runs automatically and finishes about 60% faster. This freed up our QA team to spend more time on exploratory testing, digging into edge cases and improving overall quality instead of repeating the same regression checks.
When you’re tracking the impact of AI and GenAI in Quality Engineering, focus on the KPIs that show real improvement in your day-to-day work. Start with time saved, how much faster are your testing cycles now? Then look at test coverage are you testing more areas with the same or fewer resources?
Another key one is early defect detection catching bugs sooner means smoother releases. Keep an eye on test flakiness too; if your tests are becoming more stable and consistent, that’s a great sign of progress. And finally, don’t forget tester satisfaction if your team feels more productive and less frustrated with repetitive tasks, you’re definitely moving in the right direction.