The AI coding boom is putting pressure on QA

AI-powered development tools have quickly become part of everyday software development. According to SmartBear’s 2026 research, 93% of surveyed organizations already use AI coding tools, while 40% reported that AI is involved in producing more than 40% of their code. Development is clearly getting faster. The question is whether quality assurance can keep up with the same pace.

More code means more to verify

In the same study, 70% of respondents said they were concerned that application quality was already declining, while 60% had experienced a quality issue in the past year that was partly caused by development moving faster than testing. In addition, 68% were concerned that AI-accelerated development would place further pressure on testing processes.

One of the most tangible benefits of AI is that more development work can be completed in the same amount of time. However, this does not mean that pre-release verification becomes any simpler. More changes and shorter development cycles can place additional pressure on code review, testing, and go/no-go release decisions.

In 2026, GitHub also highlighted that the growing productivity of coding agents can amplify new challenges. AI-generated output still requires thorough review, testing, security checks, and validation. As code generation becomes faster, the importance of verification increases.

This can easily lead to QA becoming one of the slowest stages of the development process. Testing capacity cannot be scaled indefinitely, and running every test after every change is not a sustainable approach either. As development speeds increase, it becomes more important to understand exactly what a change affects and which tests provide the most relevant information.

The goal is not more tests, but better decisions

The next major challenge in QA management is therefore not necessarily how to create even more automated tests. The more important question is how to connect test cases, coverage data, and code changes in a way that makes it clear where teams should focus their attention.

DORA research also suggests that AI does not solve software delivery problems on its own. Instead, it tends to amplify the processes that are already in place. If an organization has effective feedback and testing processes, AI can strengthen their impact. But if development accelerates while QA still gathers the information needed for release decisions manually, the slowdown simply shifts to testing and release decision-making.

Faster development requires faster feedback

This is also the problem TestNavigator is designed to address. The platform does not only show how many tests have passed. It also connects code changes with test coverage. Change detection makes it possible to identify which parts of the code have been modified and determine which tests should be executed to verify those changes.

TestAdvisor then prioritizes test cases based on risk, highlighting those that are most relevant to the specific change.

In the AI era, code generation may no longer be the slowest part of software development. Competitive advantage may increasingly depend on how quickly an organization can provide a reliable answer to a much more important question: is this release ready to go?