One of the biggest hurdles teams face when bringing GenAI into their existing testing strategy is dealing with data silos, your data might be scattered across different tools or teams, making it hard for AI models to learn effectively. Another challenge is the lack of proper infrastructure to support these tools, and sometimes, there’s a bit of hesitation or resistance from teams who are used to traditional testing methods.
A good way to overcome these challenges is to start small. Begin with a simple, measurable pilot project rather than trying to revamp everything at once. Make sure your data pipelines are well-organized and accessible so the AI can actually deliver value. And most importantly, involve your QA teams early, their experience and insights can help shape a smoother, more practical adoption process.
I believe the next stage, what we can call the “Fly” stage, will be about predictive quality engineering. Instead of just reacting to issues or automating existing tests, systems will actually anticipate defects even before the code is written. Imagine your quality tools learning from past releases, understanding how requirements evolve, and then automatically creating or adjusting test cases in real time.
It’s like having a proactive testing companion that keeps improving with every project, helping teams prevent issues rather than just detect them. That’s where I see the future heading.
GenAI becomes a true part of the team when it moves beyond just helping with tasks like running tests or analyzing data and starts contributing to how decisions are made. It’s that stage where it’s not just executing what you tell it to do, but actually helping you identify patterns, suggest smarter test strategies, and guide improvements. In other words, it stops being just a support tool and starts acting like a teammate that helps shape your quality engineering process.
Hey All👋,
One of the most exciting things we’re starting to see in quality engineering is how agentic AI, self-healing tests, adaptive environment-aware testing, and predictive analytics are coming together to make testing smarter and faster. Imagine a testing setup that understands its environment, automatically fixes broken tests, and even predicts where failures might happen before they do. When all these capabilities work in sync, teams can drastically cut down on test flakiness and speed up release cycles, helping deliver quality software with a lot more confidence and less manual effort.
The fastest way to move from QA to QE is to start changing how you look at testing. Instead of focusing only on manual checks or test execution, start thinking about how your work impacts product quality and user experience.
Begin by automating repetitive tasks so you can spend more time analyzing results and finding ways to improve quality earlier in the process. Gradually move toward data- and outcome-driven quality practices, where success is measured not by how many tests you run, but by how much value your testing brings to the overall development cycle.
It’s about evolving from “just finding bugs” to ensuring every release truly delivers a better experience for the user.
One of the best ways to help teams embrace AI and GenAI is to make the journey feel exciting, not intimidating. Start by creating space for experimentation, let your QE and dev teams try new tools or ideas without fear of failure. Encourage cross-team collaboration so that testers, developers, and product folks can learn from each other’s experiences.
Most importantly, highlight real success stories. When someone uses AI to solve a problem or speed up a process, celebrate it openly. These small wins build confidence and inspire others to explore what’s possible. Over time, this approach naturally grows a culture where learning, sharing, and innovating become part of how the team works every day.
It really depends on what your goals are and how fast you want to move. If you’re looking for quick results or want to test out new ideas, buying an existing solution can save a lot of time and effort. But when you need something that fits deeply into your systems or want to protect your own unique processes and data, building it in-house makes more sense. In most cases, teams find a balance they buy tools to get started quickly and then build on top of them as their needs grow.
I’d say the two areas where teams see the quickest results are automated test case generation and defect triaging.
Automating test case creation helps save a lot of manual effort while improving coverage, and using AI for defect triaging speeds up how issues are identified and prioritized. Together, they make testing faster, more accurate, and far less repetitive for the team.
It’s definitely a tool — not a threat. When used the right way, AI can actually make testers more powerful. It’s not about replacing humans, but about helping us work smarter and faster. AI can handle the repetitive stuff, analyze patterns, and surface insights, so testers can focus on what really matters, critical thinking, creativity, and improving product quality. It’s like having an extra set of hands that helps you do your best work, not one that takes your job away.
Yes, agentic AI can automatically identify flaky or broken test scripts by analyzing patterns in test runs, like when a test keeps failing inconsistently. It can also suggest or even generate possible fixes based on what it learns from stable tests. However, it’s always best to keep a human in the loop for final approval, just to make sure no unintended changes slip through and everything stays reliable.
That’s a great question and something a lot of people are thinking about right now. The key is to focus on growing with AI, not competing against it. Teams should invest time in learning how these tools work, what they can (and can’t) do, and how to use them effectively in their daily work.
Strengthening skills like critical thinking, problem-solving, and deep domain knowledge will always set people apart because those are things machines can’t truly replicate. When used right, AI doesn’t replace roles; it makes them stronger. It helps automate the repetitive parts so that engineers can focus on strategy, creativity, and quality, the work that really drives innovation forward.
In short, it’s about balance, letting AI handle the heavy lifting while we focus on the thinking and decision-making that only humans can do.
The Test Center of Excellence (CoE) is evolving from just managing testing activities to becoming a true driver of quality across the organization. Instead of focusing mainly on manual oversight, it will now play a more strategic role, guiding how AI and automation are embedded into every stage of software development. The CoE will act as the hub that connects people, processes, and technology, ensuring that testing is not just about finding bugs but about building a culture of continuous quality and smarter decision-making.
Both are important, and they actually complement each other really well. Traditional frameworks like Selenium or Rest Assured help you understand how automation really works, the logic, structure, and flow behind testing. These are core skills that make you a stronger QA engineer.
AI-driven tools, on the other hand, help you move faster by handling repetitive or time-consuming parts of testing. But to use them effectively, you still need that solid foundation.
So, it’s not about choosing one over the other. Learn and strengthen your base with tools like Selenium or Rest Assured, and then layer AI-driven testing on top of it. That way, you’ll stay adaptable as the industry evolves and get the best of both worlds.
Hey 
To measure the ROI of AI or GenAI initiatives in Quality Engineering, start by tracking practical outcomes that truly reflect improvement. Look at how much testing time you’ve saved, how early you’re catching defects, and whether your overall test coverage has improved. Also, check if flaky tests have reduced, that’s a strong indicator of better stability. Finally, connect these technical gains back to business results, like faster releases, fewer production issues, or improved customer experience. That’s where the real ROI shows up.
When you’re starting out with small-scale pilots in the Crawl phase, the key is to keep things simple and focused. Start by picking a specific area or module that’s important but not too risky something where you can clearly see the impact of your experiment. Define what success looks like from the beginning with clear, measurable metrics, like reduced testing time or improved accuracy.
The biggest mistake teams make is trying to do too much too soon. Avoid turning your first pilot into a huge, complex project, it’s better to show quick, meaningful wins that build confidence and help you learn before scaling up.
Good Day Everyone👋
Here is answer to the Question:-
When you’re writing prompts to generate test cases or scenarios, the key is to be as clear and detailed as possible. Think of it like giving instructions to a new team member, the more context you share, the better the output will be.
For example, instead of saying “Generate test cases for login,” you could say, “Generate functional test cases for the login module of version 2.1, focusing on both valid and invalid credential scenarios.”
By adding specifics like the app version, the module, and the type of testing you want, you help the system understand exactly what you need, and that leads to more relevant, higher-quality test cases.
When working in an AI-driven testing setup, it’s important to treat your models just like any other piece of software. Start by using model versioning, this helps you track every change and know exactly which version is in use. Next, set up automated retraining pipelines so your models stay updated with new data without a lot of manual effort. Finally, use containerized environments (like Docker) to make sure your models run smoothly and consistently across different systems.
In short, version, automate, and containerize. This combination keeps your deployments reliable, traceable, and easy to maintain throughout the CI/CD process.
One of the biggest mistakes organizations make when adopting AI too early is trying to automate everything right from the start. It’s tempting to jump all in, but that usually leads to confusion and inefficiency. Another common issue is overlooking data quality, if your data isn’t clean, even the smartest models won’t give reliable results. And finally, removing human oversight too soon can backfire.
A better way is to start smal, pick one use case, test it, measure the impact, and then scale gradually. This “crawl, walk, run” approach helps teams learn, adapt, and build trust in the process before going all in.
At the “walk” stage, GenAI really starts to make testing smarter and faster. It can look at recent code changes and automatically suggest or generate the right test cases for those updates. Instead of manually figuring out what needs to be tested every time, it helps testers focus on what matters most. It can also point out possible exploratory testing areas or potential defect patterns based on past issues. This means less repetitive work, faster coverage, and more time for testers to focus on deeper analysis and quality improvements.
That’s a great question — moving from small AI experiments or POCs to real-world adoption in Quality Engineering takes a step-by-step approach.
Start by expanding the scope of your pilot projects once you’ve seen some success. Don’t jump into full-scale implementation right away, instead, integrate what’s working into your existing CI/CD pipeline. This helps make AI a part of your regular testing workflow rather than something that runs on the side.
The key is to connect your results to real business outcomes, faster releases, fewer defects, or improved test coverage. When your experiments start showing measurable value, that’s when true adoption begins. It’s not just about testing AI tools; it’s about proving their impact and scaling them gradually.