Once upon a time, QA engineers seeking to advance their careers had a particularly central goal to focus on: Becoming better at writing and executing tests.
The faster you could write tests, and the more test automation frameworks or languages you knew, the more employable you tended to be. It was easy to think of your job as being mainly about delivering tests because businesses measured QA success largely in terms of how many tests the team ran.
With the advent of generative and agentic AI, however, this idea has gone out the window. Today, QA professionals can lean on AI to write tests very quickly, even if they have a smaller understanding of the framework or language they’re working with.
As a result, the number of tests that a QA engineer can create is no longer as important as it used to be. What matters more now is how effectively QA professionals can ensure overall software quality.
Retooling QA expertise for the AI era
From a QA career perspective, this shift means that employers are less likely to measure an engineer’s impact based on how many tests he or she writes. Instead, they’re likelier to focus on ultimate software quality outcomes.
The new question facing QA professionals, then, is: How can they excel at delivering software quality in a world where manually writing tests is no longer a key part of that process?
The answer involves recentering QA skill sets on areas like the following:
- Assessing business requirements to determine what needs to be tested, then overseeing the process of generating the tests using AI.
- Identifying coverage gaps in AI-generated tests, which tend to result from issues like prompts within AI-assisted test authoring tools that don’t fully capture business context.
- Troubleshooting failed or flaky tests, especially when the issues result from poorly designed AI-generated tests.
- Ensuring that testing keeps pace with the overall software delivery pipeline (which AI, like test generation, has greatly accelerated).
QA engineers who bring these skills to the table are the ones who create the most value for organizations seeking to ensure that their testing strategies accurately reflect business needs, and that AI-generated tests accelerate QA workflows without introducing new risks.
This is precisely what Leapwork Play helps QA teams do. By combining AI-driven test authoring capabilities with test orchestration and management functionality, Play provides an end-to-end platform for putting modern testing strategies into practice. Play’s AI-assisted authoring helps teams create tests, while deterministic execution keeps runtime results consistent. That lets QA professionals focus on whether the tests reflect business requirements, cover the right scenarios, and produce failures worth investigating.
Leapwork Play gives QA teams the ability to focus on leveraging QA expertise to deliver software quality. It places the power of AI within easy reach while ensuring that QA professionals retain ultimate control of the testing process.
