AI is about to fundamentally reshape the Quality Assurance profession. Not because quality suddenly becomes less important, but because the way quality is achieved in software is changing.
For many years, QA existed as a separate function for a very good reason. As IT grew, software became more complex, and engineering work was split into specialized roles to improve efficiency. Validation — both manual and automated — moved away from developers and became a dedicated responsibility. This allowed engineers to focus on building features, while QA focused on verifying correctness, stability, and edge cases.
That model worked well in a world where testing was slow, manual, and expensive.
AI changes this balance.
AI is aggressively entering both automated and manual testing. Writing tests, generating test data, mocking systems, running scenarios, checking regressions — all of this becomes much faster and cheaper. Developers can now verify their own work much more effectively, and the gap between “writing code” and “validating code” continues to shrink.
As a result, quality starts moving back into engineering.
Developers will increasingly be expected to think not only about making a feature work, but about its reliability, failure modes, regressions, and long-term behavior. With AI assistance, this becomes part of normal engineering work, not a separate phase handled by someone else.
At the same time, manual QA does not simply disappear — it changes direction.
A significant part of acceptance testing naturally moves closer to managers. Mostly product managers, but not only – to all managers responsible for outcomes. With AI support, these roles can validate user flows, behavior, and visual correctness much more independently, without relying on a separate execution-heavy QA function.
Many routine QA activities — running checklists, preparing releases, writing tickets, coordinating test passes — combine extremely well with AI and modern CI/CD pipelines. As legacy code is gradually refactored and systems move to more standardized architectures, releases increasingly become fully automated. This transition will take time, but the direction is clear.
So what happens to QA as a profession?
QA does not disappear, but it becomes much smaller and much more senior.
The execution-heavy parts of the role — manual testing and large amounts of repetitive automation — are the first to go. These tasks are the easiest to automate and the hardest to justify as a separate function.
What remains is a different role: fewer QA engineers, often one per team or even per several teams, acting as quality controllers rather than test executors.
Their focus shifts to:
- defining test and quality strategy,
- identifying systemic and cross-team risks,
- owning E2E and integration testing,
- watching for degradation over time,
- acting as an independent signal of system health.
In other words, QA moves closer to true Quality Assurance rather than testing.
For some QA professionals, this means moving deeper into engineering — toward broader development and system-focused roles. For others, it means moving closer to product and user experience. And for some, it may mean leaving IT altogether.
In recent years, QA had a very low barrier to entry and attracted many people into tech, often because of salaries and working conditions rather than a deep interest in engineering or product work. AI will likely trigger a correction. Some people will return to other professions — medicine, law, economics — but with new skills, new tools, and a very different perspective.
Quality does not disappear. The QA function changes. Ownership of quality spreads across engineers and managers. And a smaller number of strong QA engineers remains — not as executors, but as guardians of system quality. This is not a loss. It is a shift toward deeper responsibility and more mature software development.