At another session in the TTMS Tech Talks series, we explored how artificial intelligence is changing the work of software testers. On October 20, 2026, Małgorzata Struzik, QA Engineer at TTMS, presented “Will AI Take Testers’ Jobs? Facts, Myths, and the Future of QA.”
Małgorzata explored the tasks where AI can support testers and shared her experience of trying out one of the tools herself. Her presentation opened a discussion about the future of the profession, collaboration between testers and developers, and the skills needed to use AI in IT projects.
1. How does AI support testers’ everyday work?
What does a software tester actually do? If you picture someone clicking buttons and checking whether an application works, you are seeing only part of the job. Before a quality assurance (QA) team begins testing, it needs to understand how the system should work, who will use it, and what consequences a defect could have. A tester decides what to check and how, looks for ambiguities in the requirements, and anticipates situations that could cause difficulties for users. When testers find a problem, they describe it so that a developer can reproduce it and identify its cause.
Where can AI help with this work? It can suggest test cases: descriptions of what to check, which actions to perform, and what the expected outcome should be. It can also help write an automated test, analyze logs that record system events, or prepare test data. This support helps testers prepare the materials they need more quickly.
Let’s take a closer look at suggested test cases. AI can quickly produce an organized set, with steps and an expected outcome for each case. It is easy to assume that the set is ready to use. However, a tester familiar with the application may notice that the company follows a different process or that a check for invalid input is missing. The tester then returns to the requirements, resolves the uncertainties, and revises AI’s suggestions.
Our guide to AI in test automation explores how AI can support test creation, execution, and maintenance in greater detail. Tasks that take up a significant amount of the team’s time and produce results that can be reliably checked are a good starting point.
2. From requirements to test cases – AI in practice
For the session, Małgorzata tried the process herself. She asked ChatGPT to prepare a detailed requirement describing how someone would use a website, then entered that description into the AI generator within a test management tool. She selected the tool from our list of the 10 best AI tools for testers.
The tool quickly prepared the test cases. Małgorzata could read the suggestions as they were generated, and once saved, they formed an organized set. Each case was labeled as AI-generated. How did she assess the result? During the discussion, she explained that each suggested test still needs to be checked against the requirements: “A person will have to review each test case individually and check whether it matches the requirements, and that takes time too.”
Małgorzata noted that this was a brief trial and that the requirements had not been developed in detail. Their description was created with ChatGPT’s help. In a real project, the first step would therefore be to check whether it reflects what the business and its users need from the application. If an important rule is missing, the generated tests may omit it too. They would then match the description while leaving some necessary checks uncovered.
Małgorzata’s point about reviewing every suggestion raises a question: how much time can this approach save? To assess that, the team needs to account for preparing the description, generating the test cases, reading them, and making corrections. The total time can then be compared with the time needed to prepare a similar set without AI assistance. Time savings occur when the entire process takes less time and the resulting tests verify the requirements just as effectively.
3. Why can a passing test create a false sense of security?
Automated tests created with AI assistance also need to be reviewed. Can an application pass all of them and still behave incorrectly? Małgorzata highlighted that possibility. If a test treats incorrect application behavior as correct, it will pass even though the feature is not working properly. A test suite may also leave out an important feature or business rule.
The bread in the illustration presents a similar situation: it has a golden crust and a properly baked interior, so those checks would pass. However, it was supposed to be gluten-free, and wheat flour was used to make it. Checking its appearance and how well it was baked was not enough to confirm that it met the basic requirement.
In software, a similar problem could involve a financial report that should be available only to authorized employees. AI prepares a test: the user clicks a button, and the report opens. The test passes. Everything looks fine until someone asks: what happens if an employee without permission clicks the same button? If nobody has checked that, it remains unclear whether the application properly protects access to the report.
That is why a tester reviewing AI’s suggestions returns to the requirements and checks:
- whether each requirement has a corresponding test;
- whether the tests correctly describe how the application should behave;
- whether invalid data, missing permissions, and exceptional situations have been considered;
- whether testing covers the features whose failure would have the greatest impact on users and the business.
Understanding how users work helps identify these gaps. A tester who knows who uses the report and what its data is used for can ask further questions and expand the test suite. This helps ensure that tests also cover what matters in everyday use of the application.
4. How might AI change the division of work between testers and developers?
How might using AI affect the way a team divides its work? One participant suggested imagining two people, each able to both write code and test it. For one task, the first person implements a change while the second checks how it works. For the next, they switch roles, using AI for support. This approach would require learning on both sides. Testers would develop programming skills, while developers would gain a deeper understanding of testing techniques. A second person’s perspective would remain important for spotting defects or questioning the author’s assumptions. Whether this model would work would depend on the project, the team’s experience, and its software verification practices.
Another idea emerged during the discussion: the team first prepares tests describing how the application should behave, and AI then generates code based on those tests. This puts the focus on the precision of the tests. If an important rule is missing, the generated software could pass every check and still fail to meet users’ needs.
Both scenarios show why it helps for IT team members to understand one another’s responsibilities. Understanding code helps testers identify the cause of a defect, while knowledge of testing helps developers anticipate problems as they write a feature. We explore these changes further in our article on how AI is changing the work of developers, testers, and analysts in 2026.
5. How can you prepare a QA team to work with AI?
Where should learning to work with AI begin? Toward the end of the session, participants expressed a need for practical training. They wanted to see how to use the tools, write prompts, and check the responses. They also suggested separate sessions on working with AI chat tools and using AI for programming, so that everyone could explore examples relevant to their responsibilities.
Such training could focus on one specific task, such as preparing test cases together for a selected application feature. The team starts with an agreed requirement, provides it to AI, and then discusses the suggestions. This makes it possible to see what the tool included, what it missed, and how much work is needed to bring the tests to an acceptable standard.
A task like this also provides an opportunity to establish a few ground rules:
- Which tools can we use? Employees should know which solutions have been approved for use in the project.
- What data can we share with them? Requirements, code, and documentation may contain confidential information. How that information is used must comply with company and client policies.
- Who reviews the output? Someone needs to assess the generated materials and decide whether they are suitable for further use.
- What do we check before approval? The team should agree on criteria such as alignment with requirements, correct expected outcomes, and coverage of important exceptional situations.
The discussion also raised the question of responsibility for code created with AI assistance. Participants explained that, in their projects, the person approving a change is responsible for checking it, regardless of how it was created. Learning to use AI should therefore also include evaluating and correcting its suggestions.
What does this mean for the future of testers? The session explored several scenarios, each prompting a discussion about the need to develop skills. AI can help prepare tests or analyze code. Using those outputs requires people who understand the application, can identify gaps, and know what further questions to ask. Małgorzata had already addressed this need to keep learning during the discussion about the profession’s future: “So we really need to be open to learning.”
To learn more about using AI in software testing, explore QATANA – TTMS’s AI-supported software test management tool. Our team can discuss how this solution could be used in your project.
FAQ
What is the difference between quality assurance and software testing?
Quality assurance (QA) covers the practices a team uses to prevent quality problems throughout software development. Software testing focuses on evaluating the product and identifying defects. QA can include reviewing requirements, agreeing on development standards, and improving how changes are checked before release. Testing provides evidence about how the software behaves under specific conditions. For a business, both matter: preventing misunderstandings early can reduce rework, while testing helps identify problems in the implemented solution.
What is the difference between a test scenario and a test case?
A test scenario describes a situation or activity that needs to be checked, such as a customer completing an online purchase. A test case gives more detailed instructions, including the starting conditions, input data, actions, and expected results. One scenario can require several test cases to cover different outcomes. For example, purchasing a product may involve a successful payment, a declined transaction, or an interruption during checkout. Teams should agree on these terms because naming conventions can differ between projects and tools.
Can AI help with testing older software that has incomplete documentation?
AI can help organize available information and suggest questions or initial test cases based on existing materials. These might include user guides, historical defect reports, support tickets, and existing tests. The main difficulty is establishing which behavior is actually correct. Older software may contain workarounds or defects that users have come to accept, and describing its current behavior can accidentally turn those problems into requirements. Business users and product owners should therefore help confirm the expected behavior. A useful starting point is one well-understood process whose rules can be checked before expanding the scope.
What are flaky tests, and why do they matter when evaluating AI-generated tests?
Flaky tests produce inconsistent results even when the software being tested has not changed. They can be affected by timing, shared test data, unstable environments, or dependencies on external services. Such failures create extra investigation work and make it harder to distinguish a product defect from a problem in the test itself. AI-generated test code can contain these weaknesses, so a single successful execution provides limited evidence of reliability. Teams should check whether tests behave consistently across repeated runs and investigate recurring failures. Otherwise, time saved during test creation can be consumed by maintaining unreliable tests.
What should a company check before committing to an AI testing tool?
A company should examine how easily it can retrieve and reuse its testing assets if it changes tools. Important questions include whether test cases, results, requirement links, and change history can be exported in a usable format. For generated automation, the team should establish whether the code can run and be maintained outside the vendor’s service. It should also identify features that depend on proprietary components or continued subscription access. A practical evaluation can include exporting a small sample and checking what another tool can actually read. This helps estimate the future effort of migration and protects the value of the team’s work.
