The next release is scheduled, and testing capacity is tight. Since AI started writing code alongside developers, software gets built faster, and one question is getting louder: How do quality assurance (QA) and evidence keep up? In regulated environments, every release must also be documented and justified. That turns software testing into a sourcing question: build testing expertise in-house, bring in external specialists, or combine both deliberately?
Before that question can be answered, it is worth taking a closer look at the phrase “AI in software testing”. Behind it usually sit three different tasks.
When someone asks for “more testing capacity for AI”, they mean different things depending on the team:
The first task shows most clearly why the separation matters. In a survey of more than 1,100 developers, 96 percent do not fully trust AI-generated code, yet only 48 percent always verify it before merging it into the codebase (Sonar, 2025). 38 to 40 percent even find reviewing it more effortful than reviewing human-written code.
The perceived speed is deceptive as well. In a controlled study, experienced open-source developers using AI assistance took 19 percent longer, even though they felt faster (Becker et al., 2025). More code is produced more quickly, but verification does not automatically keep pace.
Anyone who does not separate the three tasks compares apples with oranges when deciding between building and buying. The request “We need more testing” can mean additional verification capacity, building up tooling and automation know-how, or a specialist topic for which nobody in the company has experience. Each of these tasks has different requirements for expertise, utilization and evidence.
A simple cost comparison, such as the salary of one employee against the day rate of an external specialist, does not provide a consistent basis for the calculation. The result is a decision on unclear ground: staff are tied up long-term for the wrong task, and releases become harder to justify convincingly. The article “Building Software Test In-House: The Hidden Bill” describes the costs of an in-house build in general.
Not automatically. AI can speed up routine tasks such as test case generation, but it requires skills, tools and ongoing maintenance. At the same time, additional AI-generated code increases the need for verification.
Often yes. For learning or probabilistic components, test cases with a fixed expected result are frequently not enough. For high-risk AI systems, requirements from the EU AI Act (Regulation (EU) 2024/1689) apply on top.
It compares the salary of one employee with the day rate of an external specialist. Maintenance effort, utilization, onboarding and knowledge transfer are left out.
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