Human-in-the-loop plays a vital role at every stage of GenAI adoption.
In the Crawl phase, humans provide direction and oversight, setting the foundation, defining goals, and ensuring the AI is learning the right things.
As we move to the Walk phase, human experts validate outputs, fine-tune results, and make sure the system is improving in the right way.
By the Run phase, humans focus on continuous monitoring and governance, keeping an eye on performance, catching drifts, and ensuring everything stays aligned with business objectives.
In short, even as AI becomes more capable, human judgment remains the guiding force that keeps it effective, ethical, and relevant.
I’d prefer AI to first suggest the fixes and let humans review them before applying. It’s important to keep a layer of human validation early on, because even small automated changes, if unchecked, can introduce new issues. Once the system proves reliable over time, I’d feel more confident letting it handle some fixes automatically.
One of the biggest challenges in scaling AI-driven testing across multiple teams or products is keeping everything consistent especially the data being used. When different teams work with different datasets or slightly different setups, it can lead to inconsistent results.
Another common issue is model drift, where models start to lose accuracy over time as your applications or data evolve. Keeping them updated and monitored becomes really important.
You’ll also run into environment differences, what works perfectly in one setup might behave differently in another.
The best way to handle all this is to set up a centralized testing pipeline, use version-controlled models, and maintain reproducible environments so every team is aligned and results remain consistent as you scale.
Great question, I’ve seen this happen a lot.
The single biggest (and most costly) mistake teams make when moving from Crawl → Walk is scaling up too fast before the foundations are stable. In practice that looks like: adding lots more tests, flows, or automation, but without clear metrics, flaky tests fixed, or stable test environments. The result? Test suites that fail unpredictably, noisy alerts, lost developer trust, and slower delivery, which completely defeats the point of scaling.
Here’s a simple, practical way to avoid that pitfall:
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Set concrete success metrics first
Decide what “stable” and “good” mean — e.g., <2% flakiness, >95% reliable CI runs, or mean-time-to-detect < 1 hour. Measure those before expanding.
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Fix flakiness before scaling
Treat flaky tests like tech debt. If a test fails intermittently, quarantine and fix it (or remove it) before adding more tests.
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Scale in small slices (pilot → expand)
Roll out new automation to a small team or feature area, measure, iterate, then widen the scope. Don’t flip a switch for everything at once.
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Automate environment parity and test data hygiene
Make sure test environments, data setup, and teardown are reliable and repeatable — many failures come from environment drift, not product bugs.
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Create ownership and feedback loops
Assign clear owners for test suites, build fast feedback into PRs, and add actionable alerts instead of noisy emails.
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Continuously monitor and stop when needed
Keep dashboards for flakiness, runtime, and false-positive rates. If metrics regress, pause expansion and fix the root causes.
Quick example: a team added hundreds of UI tests overnight and their CI began failing randomly. Developers started ignoring failures. When they slowed down, fixed flaky selectors and environment setup, and then reintroduced tests in small batches, reliability and confidence returned.
Bottom line: growing coverage is good but grow when your metrics and stability let you. Incremental, measurable scaling prevents wasted effort and keeps the team confident in the results. If you want, I can share a short checklist you can drop into your sprint process to enforce these steps.
I believe AI can be really helpful in suggesting test strategies and speeding up the process, but when it comes to critical production releases, a human review is still essential. AI can guide us in the right direction, but the final judgment should come from experienced testers who understand the business context and potential risks. It’s best when both work together AI for insights, and humans for validation.
Start small by automating the simple, repetitive tasks that take up your team’s time every day. These quick wins not only show visible results fast but also help build confidence across the team. Once people see the real impact, like time saved or fewer manual errors, you can slowly move toward more complex areas. The key is to grow step by step while keeping quality and trust at the center of every change.
I’d say finance and healthcare are the two industries that’ll see the biggest shift in the next few years. Both rely heavily on data and strict regulations, which makes them perfect candidates for AI-driven improvements. In finance, we’re already seeing smarter fraud detection, faster risk assessments, and more accurate decision-making. In healthcare, AI is helping analyze medical data, speed up diagnoses, and even improve patient care. The impact won’t just be about efficiency, it’ll change how these industries operate at their core.
To avoid getting caught up in the AI hype, organizations should focus on solving real testing challenges and measuring the impact. Instead of adopting AI just because it’s trending, link every initiative to clear business outcomes, like faster release cycles, fewer defects in production, or reduced test maintenance effort. When your AI efforts are tied to measurable improvements, it’s easier to see what’s truly adding value and what’s just noise.
AI tools can definitely help spot potential usability and accessibility issues, things like missing alt text, poor color contrast, or unclear navigation patterns. But when it comes to understanding how real users feel when using a product whether something is intuitive, engaging, or frustrating, that’s where human insight is irreplaceable. AI can guide us to problem areas, but it’s the human perspective that ensures the experience truly works for everyone.
Yes, having global standards would really help bring consistency and trust to how AI is used in quality engineering. Just like ISO or ISTQB set clear guidelines for traditional testing, similar frameworks could make sure teams around the world follow best practices when applying AI in QE. It would also make it easier for tools and processes to work well together, creating a more reliable and collaborative ecosystem.
That’s a great question and something many people in testing are thinking about today. The key is to focus on what makes you uniquely human. AI can handle repetitive or data-driven tasks, but it can’t replace your critical thinking, creativity, or understanding of how users actually behave.
Your real strength lies in areas like domain expertise, knowing your product, your customers, and the business inside out. Add to that your ability to explore, question, and strategize during testing, those are skills no tool can truly mimic.
So instead of worrying about “AI taking jobs,” think of it as a partner that takes care of the routine, freeing you up to do the high-impact work that requires human judgment and intuition.
Absolutely. AI is already becoming a copilot for testers, and that role is only going to grow. Instead of replacing testers, it’s helping them focus on what really matters, quality and strategy, while taking care of the repetitive or time-consuming tasks.
Think of it like this: AI can analyze patterns, flag potential issues early, and even suggest what areas might need more testing. It takes over the heavy lifting so testers can spend more time making smarter decisions, improving coverage, and understanding the “why” behind defects.
So yes, AI is here to work with testers, not against them making testing faster, more insightful, and a lot more efficient.
When it comes to measuring ROI and success for AI in testing, it really comes down to tracking the impact it creates on your daily testing workflow. You can start by looking at how much time your team saves for example, if test case creation, execution, or maintenance takes less effort now.
Then, check if you’re seeing better test coverage are you testing more areas of the product than before? Another clear indicator is the reduction in defects that reach production. If fewer issues are slipping through, that’s a strong sign your testing process has improved.
And finally, keep an eye on tester productivity. Are your testers spending more time on strategic, high-value tasks instead of repetitive work? If the answer is yes across these areas, time, coverage, quality, and productivity, you’re on the right track toward real ROI from AI in testing
You can start by using AI and GenAI to handle repetitive or time-consuming testing tasks, like generating test cases, running regression tests, and analyzing defects. These tools can quickly spot patterns, reduce manual effort, and speed up the overall testing cycle.
At the same time, testers should focus on areas that need human judgment, like defining test strategies, exploring edge cases, and validating critical scenarios that automation might miss. It’s all about striking the right balance: let AI handle the heavy lifting, while humans guide the process with insight and creativity.
One of the biggest challenges I’ve seen when teams move from the Crawl to the Walk stage is expanding too fast without having the right metrics in place. Many teams get excited by early wins and try to scale immediately, but without tracking clear success indicators, it’s hard to know what’s actually working.
The better approach is to take it step by step: start with a small pilot, measure outcomes, gather feedback, and only then move to a broader rollout. Setting clear success criteria upfront helps everyone stay aligned and ensures that the progress is sustainable, not just experimental.
To move from QA to QE, start by shifting your focus from just running tests to driving real outcomes for the business. Build strong automation skills and learn how to use intelligent tools that make testing smarter and faster. Understand how your work impacts product quality, speed, and customer experience. In short, think beyond finding bugs, focus on improving quality at every stage of development.
From my experience, a few tools really stand out when it comes to boosting productivity and reliability in testing. Tools like Testim, Mabl, Functionize, and Applitools have made a big difference, they help speed up regression and UI testing while keeping results consistent. I also use some custom LLM-driven scripts that make test maintenance smoother and help catch issues early. Together, these tools make the testing process faster, smarter, and a lot more reliable.
To handle bias in AI-driven risk-based testing, it’s important to stay hands-on at every stage. Start by auditing the data that’s being fed into your models, biased or incomplete data often leads to biased results. Regularly review the AI’s decisions and compare them against real project outcomes to catch any inconsistencies early. And most importantly, keep humans in the loop. Having QA experts validate and challenge the AI’s recommendations ensures that critical decisions aren’t made blindly. It’s all about maintaining balance , letting AI speed up analysis while humans keep the judgment fair and grounded.
The biggest shift teams need to make is changing how they think about working with AI. Instead of seeing automation as something that might replace them, teams should see it as a partner that helps them do more. It’s about building a culture of continuous learning, being open to experimenting, trying new tools, and understanding how AI can make testing smarter and faster. The real value comes when humans and AI work side by side, learning from each other and improving the overall quality process together.
I see AI as a co-pilot for testers, not an autopilot, at least for now. It’s here to support and guide us, helping make our work faster and smarter, but the final call still lies with humans. Testers bring the context, judgment, and critical thinking that machines can’t fully replicate. So, while AI can handle repetitive tasks or suggest optimizations, it’s still the human in charge of ensuring true quality.