Artificial intelligence is increasingly finding its way into quality assurance. It generates test cases, prioritizes defects, analyzes logs, and supports teams in automating repetitive tests. This promises shorter development cycles, greater test coverage, and less manual effort. This development makes sense in principle. AI can make software testing faster and more scalable. However, problems arise when companies conclude from this efficiency that human testing will no longer be needed in the future. This is because AI primarily tests what can be described, modeled, and deduced from existing information. Real users, on the other hand, do not act according to predetermined patterns. They interpret content differently than expected, use unusual devices, skip steps, or use digital products in situations that were never anticipated internally.

The key question, therefore, is not: AI or humans?

The question is: What tasks can AI reliably handle—and where is an independent human perspective still needed?

Why AI Is So Attractive in Software Testing

AI is particularly well-suited for tasks that are frequently repeated, follow clear rules, or involve large amounts of data. For example, it can analyze source code, suggest test cases, identify technical anomalies, or accelerate existing test automation. This provides a significant advantage, especially with short release cycles and limited QA resources. Teams can test more variants, respond more quickly to changes, and reduce routine manual tasks. However, high testing speed does not necessarily equate to high software quality. Thousands of automated tests are only helpful if the right questions are asked, relevant scenarios are considered, and results are evaluated correctly. This is precisely where the limitations of AI-based quality assurance begin.

1. AI Can Produce Plausible but Incorrect Results

AI systems generate output based on probabilities. An answer may sound technically convincing yet still be incorrect. This risk applies not only to generated text but also to test cases, error analyses, and expected results. In the absence of clear requirements, an AI may make assumptions that do not apply in the actual product. It may assume certain business rules, misinterpret system states, or classify a technically plausible result as correct. Particularly critical is the so-called “test oracle” problem: A test can be executed technically without it being clearly established which result would be correct from a business perspective. Whether a process functions correctly from the perspective of the company, the customer, or regulatory requirements cannot always be deduced from code and data alone. AI can therefore create and successfully execute a test even though the underlying expected value is incorrect.

2. When AI Monitors Its Own Results

An additional risk arises when AI is used not only in development but also in subsequent quality assurance.

The scenario is straightforward: An AI generates code. Another AI creates the corresponding test cases. An AI model then evaluates whether the results are acceptable. At first glance, this process seems efficient. In reality, however, all the systems involved may share similar assumptions, training patterns, and blind spots. Errors that arose during development may then be carried over into the tests and confirmed by the subsequent evaluation.

This creates a closed-loop control system:

AI defines the expected behavior, AI checks the behavior, and AI evaluates the results of the check.

A detailed or confidently worded justification is not in itself proof that the assessment is actually correct. Without an independent oversight body, there is a risk that errors will not only be overlooked but also systematically confirmed.

3. AI Experiences No Real-World Use

Digital products are used not by models, but by people. Their behavior is often contradictory, situational, and difficult to predict.

Real users:

• interpret terms differently than the development team,
• click the same button multiple times,
• switch between devices and browsers,
• leave a process and return later,
• overlook clues,
• make typos,
• use the Back button,
• stop when they're unsure,
• use a product in a way other than intended.

AI can simulate such behaviors. However, it cannot reliably predict which problems will actually arise under real-world conditions and which of these are particularly relevant to the target audience. Emotional reactions are another factor to consider. A process can work technically and still trigger mistrust. An error message can be correct but still cause uncertainty. A navigation system can be logically structured but feel incomprehensible to outsiders. AI does not experience frustration, uncertainty, or skepticism. It can therefore analyze how an interface is likely to be perceived—but it cannot experience for itself how it actually affects people.

4. Internal Logic is no Substitute for a Human Perspective

Development, product, and QA teams know their digital products inside and out. While this is necessary for their work, it inevitably leads to a certain degree of tunnel vision. Terminology seems straightforward because it’s used internally every day. Workflows appear logical because their origins are well-known. Features are sought where the team itself has categorized them. Even AI systems operate based on the context provided to them. If they are trained or controlled using internal requirements, documentation, and existing test cases, they often adopt the same perspective. The result is quality assurance that evaluates the system from the perspective of its creators. Real users, on the other hand, do not bring any prior internal knowledge to the table. They reveal whether information is actually understandable, whether the next step can be found, and whether an application inspires trust under real-world conditions.

UX Testing with real users brings these differences to light. Qualitative and quantitative usability studies, moderated tests, and think-aloud videos not only show that users fail or abandon tasks, but also explain why this happens.

Discuss UX Tests Conducted with Real Users

5. Real Devices and Usage Scenarios Remain Difficult to Simulate

Test environments only provide a limited representation of reality. Even extensive device labs and automated browser tests cannot account for every combination of hardware, operating system, browser, network quality, and individual settings.

For example, problems occur only when:

• on certain operating system versions,
• on older or unusual devices,
• when cell phone connections are unstable,
• in conjunction with VPNs or firewalls,
• if you have specific screen or language settings,
• during a switch between Wi-Fi and cellular data,
• under real-world load and usage conditions.

AI can identify possible combinations and support automated testing. However, it still relies on defined environments and known parameters. Real people use their own devices under real-world conditions. This leads to situations that were not anticipated in a lab, in a simulation, or within an automated testing environment.

6. Autonomous Test Agents Create New Risks

The more autonomously AI systems operate, the more access they require. For example, test agents can be connected to source code, test databases, ticket systems, development environments, or external interfaces. This gives rise to new security and data protection risks. An agent may misinterpret instructions, perform inappropriate actions, or be influenced by manipulated content. Excessive permissions further increase the risk that data will be altered, confidential information disclosed, or chargeable processes triggered. Sensitive content must also be taken into account. AI-powered testing often involves processing source code, production logs, personal data, internal business rules, or information about known security vulnerabilities. Companies must therefore not only secure the product being tested; they must also verify how the AI-based testing system itself handles data, permissions, and external content.

7. Automation Can Create a False Sense of Security

AI-generated reviews often appear well-structured, comprehensive, and objective. It is precisely this professional presentation that can lead people to scrutinize the results less critically.

However, a green dashboard only indicates that the tests that were run were passed. It does not automatically indicate whether:

• the relevant risks were tested,
• the expected results were correct,
• important usage scenarios were missing,
• real target groups were taken into account,
• the test environment was representative.

The more extensive the automation, the greater the trust in metrics and status indicators can become. The real risk then lies not in a single faulty test, but in the assumption that the product has been thoroughly tested. AI accelerates quality assurance. However, without an independent reality check, it can simultaneously increase the speed at which a false sense of security develops.

AI vs. Humans: Different Strengths Rather Than Complete Replacement

AI and human testing serve different purposes.

AI is therefore particularly effective for scalable, repeatable, and formally describable tests. Humans are especially important when behavior, perception, context, and unpredictable situations determine the actual quality. The most viable QA strategy combines both approaches.

Crowdtesting as an Independent Reality Check

Crowdtesting adds an independent layer to internal and AI-based quality assurance. Qualified testers evaluate websites, apps, portals, digital journeys, or AI systems using real devices under defined yet realistic conditions. The goal is not to replace automated testing. Automation remains indispensable for recurring tests, regression testing, and technically well-defined requirements.

Crowdtesting with msg.passbrains picks up where these methods fall short. Real users test critical workflows, conduct exploratory testing to find bugs, and document issues with specific information on how to reproduce them. The findings are validated, prioritized, and prepared for QA, UX, Product, and Engineering.

Crowdtesting doesn't just create an additional list of bugs. It provides evidence of which problems occur under real-world conditions, what effects they have, and which improvements should be implemented first.

Request a Crowdtesting Pilot for Your Digital Product

AI Will Not Make Crowdtesting Obsolete—It Will Make It More Relevant

The more software is developed using AI, the greater the need for independent quality assurance. AI-generated code can be produced more quickly. AI-generated test cases can be executed more quickly. AI-based evaluations can classify results more quickly. The speed of the entire development process increases. However, this also means that errors can spread more quickly. Incorrect assumptions are carried over into code, test cases, and evaluations. Similar models may share similar weaknesses. At the same time, it remains unclear how real people will actually use the finished product. Crowdtesting therefore serves as the human oversight mechanism within an increasingly AI-driven development landscape.

It doesn't just check whether a system works according to its specifications. It shows whether the product works for real people, on real devices, and in real-world situations.

Conclusion: AI Tests the Expected—People Find the Unexpected

The future of quality assurance lies neither in exclusively manual testing nor in fully autonomous AI systems. AI can lighten the load on QA teams, speed up testing processes, and efficiently check for known risks. However, it cannot replace an independent perspective on human behavior, real-world usage contexts, and unexpected situations. Crowdtesting fills precisely this gap. It complements automation with real-world experiences, diverse perspectives, and reliable insights from actual usage. The crucial question regarding quality is therefore not whether AI or humans test better.

It reads:

How do companies combine the scalability of AI with the reality of real people?

After all, AI tests what can be predicted. Real people discover what no one expected.