The way companies develop and deliver software has changed significantly. With the adoption of DevOps, cloud, and Artificial Intelligence, development cycles are now faster, and teams can release new features at a pace that would have been difficult to imagine a few years ago.
This acceleration, however, brings a new challenge: ensuring software quality keeps pace with development. When testing cannot keep up with the speed of delivery, pressure on QA teams increases, automation requires more maintenance, and the risk of defects reaching production grows.
For large organizations, this gap can become a significant obstacle to delivering software quickly, consistently, and securely. Identifying the main friction points is therefore essential to understanding how Quality Engineering can keep pace with this new reality.
1. Testing Can't Keep Pace With CI/CD Velocity
Modern CI/CD pipelines can move code from development to deployment in minutes. Testing, however, often remains constrained by manual activities, lengthy regression cycles, or automation that is not designed to run continuously. This creates a difficult trade-off. Teams either delay releases to complete necessary testing or accept risk by moving forward with incomplete coverage. The pressure is particularly evident among QA teams.
According to Katalon's State of Software Quality Report 2025, up to 82% of testers still rely on manual testing in their day-to-day activities, while only around 45% automate regression testing. At the same time, insufficient time and workload remain major barriers to achieving quality objectives. The challenge is therefore not simply about adding more people to the process. It is about creating a testing approach that can operate at the same speed as the development pipeline.
2. Automation Scripts Break With Every UI Change
Test automation can significantly improve testing efficiency and is already replacing a substantial share of manual testing across many teams. According to Testlio’s Test Automation Statistics 2025, 26% of teams have already replaced up to half of their manual testing with automation, while 20% have replaced 75% or more.
However, automation can only keep pace with this evolution if it can adapt to changes in the applications themselves. Even a small interface change can break an existing script. Renaming an element, redesigning a screen, or changing a user flow can make previously reliable tests fail, even when the application still works as expected.
As applications evolve, maintaining these scripts can become a significant part of QA teams’ workload. Instead of expanding test coverage, teams spend time identifying tests that no longer work and adapting them to new versions of the application.
For organizations with large, constantly evolving applications, having automation that can keep up with these changes is therefore essential. Tests should adapt to the application, rather than becoming an additional maintenance burden.
3. Test Creation and Maintenance Depends on Scarce Technical Talent
The ability to scale test automation is also constrained by the skills required to create and maintain automated tests. Traditionally, this work has relied on professionals with programming and scripting expertise. As test suites grow, this dependency can concentrate the workload among a relatively small group of specialists, limiting how quickly organizations can expand test coverage.
The shortage of specialized skills is another barrier to scaling automation: 55% of QA teams report difficulties finding professionals with test automation experience, while 70% identify the complexity of integrating different tools as one of their main challenge.
A more scalable approach is to make test creation and management accessible to a broader range of professionals. QA teams, business analysts, and product owners should be able to contribute without every change requiring development-level technical expertise.
4. Traceability and Audit-Ready Evidence Are Missing
For organizations operating in regulated environments, testing is not just about achieving a positive result. It is also essential to clearly demonstrate what was tested, when it was tested, which version of the application was involved, and what the results were.
When this information is spread across spreadsheets, scripts, and different tools, gathering the evidence required for an audit becomes a manual, time-consuming process. Besides consuming valuable resources, this lack of centralization makes it harder to maintain traceability throughout the development lifecycle.
Traceability is therefore becoming increasingly important in Quality Engineering. A robust testing process should generate and maintain reliable evidence throughout the process, enabling teams to track results and respond quickly to audit requirements without reconstructing that information afterward.
5. AI Is Widening the Gap Between Code Volume and Testing Capacity
AI coding assistants are not just making developers faster. They are also changing how much software teams can build. Tools like GitHub Copilot and Cursor allow developers to write significantly more code than they could a few years ago. But more code does not always mean better software.
Katalon’s analysis of industry data from 2025 and 2026 cites research showing that AI-generated pull requests contain 1.7 times more issues than those written by humans, including 1.75 times more logic and correctness errors.
At the same time, testing capacity has not grown at the same pace. The same analysis, based on the World Quality Report 2025–26 by Capgemini and Sogeti, found that half of QA leaders using AI for test automation consider test script maintenance and unreliable tests to be two of their biggest challenges.
AI does not, by itself, solve the problems that already exist in the development process. It makes them more visible. If testing is already a bottleneck, AI can simply cause defects to be introduced at a faster rate. A strong Quality Engineering approach enables teams to keep pace with this acceleration and continue delivering software with confidence.
The companies getting the most value from AI are not simply writing more code. They are also making sure they can test it just as effectively and at the same pace.
Moving From Testing Faster to Engineering Quality
Although these challenges are different, they point to the same underlying issue. Testing can no longer be treated as a final checkpoint that happens after development. As software changes continuously, quality must be built into the delivery process itself.
This is the approach behind testingON 360, Noesis' AI-powered Quality Engineering platform. Evolving from the NTX platform Noesis launched in 2016, testingON 360 is designed to automate testing throughout the software development lifecycle. The platform enables teams to execute tests with every build, adapt testing as applications change, involve both technical and non-technical users in test creation and management, and maintain complete, audit-ready traceability in one place.
As DevOps and AI continue to accelerate software development, the ability to validate software needs to evolve accordingly. The goal is not simply to test more or to test faster. It is to build a quality engineering approach that can keep up with how software is now developed, changed, and delivered.
