The Future of QA Using AI

In an ever-shifting landscape of quality assurance (QA) tools and processes, a new contender is moving to the front: artificial intelligence (AI). When I hear people talk about QA and AI, the first question that comes to mind is, “Will AI replace my job?” My answer is no. AI can enhance the job and serve as one more tool in your repertoire. Karim Lakhani, a Harvard Business School professor, makes the same point in Harvard Business Review: AI won’t replace humans, but humans with AI will replace humans without AI.

So how do you stay ahead of the curve? Start with the challenges QA faces now, and look at where AI can help.

What challenges do QA engineers face?

How do time constraints hold QA back?

Most QA engineers have managed tasks across several projects at once. Writing test cases, building test suites, testing features, filing bug reports, and sending weekly reports all take time. The more time we spend on them, the less time we have for higher-level testing. That creates a bottleneck, and AI can help ease it.

Why is test maintenance so demanding?

As a project takes shape, the tests have to keep up. We write test cases for new features, change existing ones, and remove the ones nobody needs anymore. All of that adds to the time pressure above. Reusable components can also give QA a steadier target, as we describe in how a reusable component system built the St Andrews Links app.

What happens when requirements change?

Have you ever had to go back and update your test cases because the requirements changed? It goes hand in hand with test maintenance and time constraints. Whether the change comes after the first meetings or in the middle of a sprint, the QA engineer has to keep the test cases current so any team member knows what to expect.

How can AI help with QA?

The table below does not cover everything, but it shows where AI can fit, and what stays with a person. It reflects our view, not a rule.

Area	What AI can do	What a QA engineer still does
Test creation	Suggest test steps from a feature’s requirements	Check each step relates to the test case and can be tested
Changing requirements	Suggest updates when requirements shift	Keep the test cases current so any teammate knows what to expect
Monitoring	Flag likely problems from previous builds and logs	Decide which patterns matter and test them

Can AI speed up test creation?

Normally, the QA engineer writes every test case by hand from the requirements of the feature in the sprint. AI can help by analyzing those requirements and suggesting additional test steps, including cases for issues seen in previous builds. The QA engineer still has to check that each suggested step relates to the test case and can actually be tested.

This is where much of the industry is starting. In the World Quality Report 2025, from OpenText, Capgemini, and Sogeti (companies that sell quality engineering tools and services), test case design and requirements refinement now lead where organizations use generative AI in quality engineering. The survey covered more than 2,000 senior executives across 22 countries and 10 sectors.

Does AI save time and money?

When AI drafts test cases, the QA engineer has more time for thorough testing and less time on repetitive writing. The same report gives a sense of the size of the effect: organizations reported an average productivity boost of 19%, though one third saw minimal gains. These are self-reported figures, and only 15% of respondents had reached enterprise-wide implementation.

Can AI monitor an app in real time?

A QA engineer tests an application thoroughly, but no matter how extensive the tests are, we do not catch everything. AI can help flag likely problems by learning from data in previous builds, and it can surface patterns a person might miss on the surface. For example, it can help detect performance degradation in an app from server logs and user interactions. Catching these issues early helps the user have a good experience.

What should QA teams watch for?

AI output is only useful if someone checks it. In the World Quality Report 2025, respondents named these as their top challenges:

• Data privacy risks (67%)
• Integration complexity (64%)
• Hallucination and reliability concerns (60%)

In our view, that is the case for keeping a QA engineer in the loop: the tool proposes, a person decides.

Will AI take QA jobs?

I don’t think so. We will always need human QA engineers, because some issues cannot be handled by AI or automation. QA engineers do need to learn to use AI tools so we are not left behind. The report points the same way: generative AI was the top-ranked skill for quality engineers, at 63% of respondents. Learning the tools is good for your own growth, and it helps your company keep growing too. Our services page describes how we use AI in our own delivery work, and we wrote about practical AI for hospitality as well.

Want to know how AI can elevate your QA process? Let’s start the conversation.