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30.09.2026
AI accelerates software development. Code can be created faster, prototypes can be developed in shorter time, and new features can be delivered significantly faster. However, this creates a new challenge for software testing.
As development becomes faster, quality assurance must also keep pace with this speed. Classical testing processes often rely on stable requirements, predictable development cycles, and fixed testing phases. AI and agile development change these prerequisites. Requirements can change more quickly, new software increments emerge continuously, and test teams have less and less time to wait for finished software.
Daniel Horn has been working in quality assurance, software testing, and test automation for about 14 years. In this expert interview, he explains why AI is intensifying existing challenges in quality engineering, what the role of early testing and automation is, and how companies can adapt their testing strategy to the new development speed.
In the full expert interview speaks Daniel Horn explains the challenges of quality engineering in the AI era and explains how companies can adapt their testing strategy to the increasing development speed.
AI initially reinforces a development that has already begun using agile methods and shorter iteration cycles. Requirements are becoming more flexible, software is developed more quickly, and it is delivered more frequently.
With generative AI, this speed increases significantly further. Developers can create prototypes faster and produce larger amounts of code. For quality engineering, this means that the speed of the current testing processes is increasingly insufficient.
Test pipelines that take one or two days are no longer suited to an environment where new software increments are created in a very short time.
„AI is only a tool for now.“
Daniel Horn, Principal IT Consultant at accompio
A central approach is therefore the so-called Shift LeftQuality assurance is moved as far forward in the development process as possible.
Instead of testing the finished software first, the question should be asked during the requirements analysis: What exactly should be tested? What use cases exist? Which edge cases need to be considered? And what quality requirements are there in the first place?
Daniel Horn also sees communication between the department responsible for development and testing as crucial. Quality requirements should not be created at the end of the development process, but should be defined together as early as possible.
This leads to a fundamental change: Testing is not just used to check finished software, but is an integral part of software development from the very beginning.
If software is developed and delivered more frequently, tests must also be able to be performed more quickly.
Test automation This is not a completely new concept. It has already helped to shorten testing cycles in the past. However, due to the additional speed of AI, it is becoming even more important.
It’s not just about automating individual test cases. It’s also Deployments, test environments, and the parallel execution of tests must be made more automated.
One example illustrates the challenge: When hundreds of pull requests with different software versions are to be tested simultaneously, one or two fixed test environments are no longer sufficient. Test systems must be able to be deployed dynamically and in parallel.
AI offers various possibilities in testing. For example, it can create test cases, analyze test results, prioritize errors, or assist with pattern recognition.
This can be particularly useful for large amounts of test results. Instead of manually evaluating hundreds of results, AI can help identify anomalies and focus on relevant errors.
Nevertheless, human evaluation remains important. AI should be used as a Tools and support to be understood – not as a substitute for the professional understanding of a tester.
For companies, this raises an important question: Where does AI actually bring added value?
Daniel Horn warns against using AI just because it is available or because the current hype suggests so. For some tasks, there are already classic automation solutions that can be faster, cheaper, or more reliable.
Therefore, every process should be considered critically: Where is AI useful? Where is classic automation sufficient? And in which areas is human expertise indispensable?
AI should not be seen solely in terms of individual work steps. It can be more useful to view it in terms of the entire process to analyze and discuss with the participating teams where AI can actually be used to support them.
According to Daniel Horn, a modern testing strategy must primarily accomplish one thing: Integrate quality early and continuously into the development process.
This includes many small tests at different levels – for example unit and integration tests – instead of solely extensive end-to-end tests at the end of the development process.
At the same time, tests, deployments, and test environments must be made more automated and parallelized. Only with comprehensive Test Management Quality engineering teams can keep up with development when potentially hundreds of changes are being made simultaneously.
„We need to move in more of the same direction: What can we actually do technically already, which was previously not at all necessary? We should strengthen this now.“
Daniel Horn, Principal IT Consultant at accompio
accompio helps companies adapt their testing strategy to the increasing development speed.
AI fundamentally changes the speed of software development. This also increases the pressure on existing testing processes. However, the answer to this is not to hand over all testing tasks to AI. Companies must instead invest in their own testing capabilities. Further develop the entire testing strategy: Test earlier, automate more, parallelize processes, and consider quality requirements already during development.
AI can assist in this – in the creation of test cases, the analysis of results, or the detection of patterns. The decision, where and how AI is used sensibly, However, remaining a central task of the people in quality engineering.
Daniel Horn is a Principal IT Consultant at accompio and helps companies adapt their testing strategy to the increasing development speed.
AI accelerates software development, leading to more software increments and shorter development cycles. Testing must therefore be made faster, more automated, and integrated earlier into the development process.
AI can support testers in tasks such as creating test cases, analyzing test results, and pattern recognition. However, the professional evaluation and the decision of which tests and quality requirements are relevant remain important tasks for humans.
Shifting to the left means moving quality assurance as early as possible into the development process. Tests and quality requirements are taken into account during the requirements analysis and development, rather than only being checked on the finished software at the end.
As software is developed and delivered more frequently, testing must also be conducted more quickly. Automated tests, deployments, and testing environments enable the simultaneous review of larger numbers of changes.
