AI is changing the testing profession, but not the need for testing expertise.
Many organisations are currently investing heavily in AI tools for software development and testing, including our own QI Accelerate. AI can generate requirements, test cases and identify potential defects faster than ever before.
But does that mean the need for experienced and highly skilled Testers and Test Managers is decreasing?
On the contrary.
The more AI tools we have at our disposal, the more important it becomes to have the testing expertise needed to review what AI produces and to prioritise the test effort correctly based on the specific context. Because although AI can help identify defects, it cannot independently assess which defects matter most to the business.
What is risk-based testing?
Risk-based testing is a method where the test effort is prioritised based on the risks associated with the system, process or solution.
Instead of spending equal resources on all parts of a system, the focus is placed on the areas where:
• The probability of defects is highest (technical risk)
• The consequences of defects are greatest (business risk)
The goal is to ensure the best possible quality with the resources available.
In short, risk-based testing is about testing the most important things first and most thoroughly.
Why is risk-based testing more important than ever?
Traditionally, testing has often been about finding as many defects as possible. Today, it is increasingly about finding the right defects as early as possible.
Modern system landscapes are becoming increasingly complex. Integrations, cloud solutions, AI components and faster release cycles make it practically impossible to test everything.
At the same time, we see that AI makes it easier to generate large volumes of test cases and test data. But more test cases do not necessarily equal better quality.
If testing is focused on the wrong areas, critical risks can still be overlooked. That is why the ability to identify and prioritise risks is becoming one of the most important competencies within modern quality assurance.
AI can generate tests, but AI cannot prioritise business risks
AI has become a valuable assistant in testing.
Among other things, it can:
- Generate test cases
- Analyse requirements specifications
- Suggest test scenarios
- Identify patterns in defects and historical data
But AI often lacks the context needed to make the right priorities.
An AI tool does not necessarily know:
- Which processes are most business-critical
- Which defects will affect customers the most
- Which regulatory requirements must be complied with
- Which risks the organisation is willing to accept
That requires domain knowledge, experience and testing expertise. AI should therefore be seen as a tool that supports risk-based testing, not as a replacement for it. The greatest value is created when AI is combined with strong testing competencies.
How to work with risk-based testing in practice
An effective risk-based approach typically consists of four steps.
1. Identify risks
Map the areas where defects may occur.
This could, for example, include:
- New features
- Integrations
- Complex workflows
- AI-based functionality
- Areas with many historical defects
2. Assess probability and impact
For each risk, assess:
- Probability: How likely is it that the defect will occur?
- Impact: How serious would the consequences be if the defect occurred?
3. Prioritise the test effort
The highest risks are tested first and most thoroughly. This ensures the greatest possible risk reduction for the test effort invested.
4. Report and update continuously
Risks change as new releases, technologies and business needs emerge. Risk-based testing is therefore not a one-time exercise, but an ongoing discipline. Reporting on risks creates transparency around progress and supports decision-making.
Typical challenges in risk-based testing
Although most organisations agree that the test effort should focus on the greatest risks, many experience challenges when risk-based testing has to be put into practice.
Risk analysis becomes a one-time exercise
One of the most common mistakes is that the risk analysis is carried out at the beginning of a project and then forgotten.
But risks change continuously. New features, changed business requirements, technical dependencies and AI-based solutions can significantly change the risk picture during the lifetime of a project.
That is why risk analysis should be an integrated part of the development and testing process, rather than an activity carried out only once.
The business is not involved
Many risk analyses are carried out primarily by testers or developers.
The problem is that the most technically complex areas are not necessarily the most business-critical.
The business often holds important knowledge about:
- Critical customer journeys
- Regulatory requirements
- Revenue-critical functionality
- Areas with high reputational risk
Without this insight, there is a risk of prioritising incorrectly.
Everything is assessed as high risk
When teams work with risk assessment for the first time, we often see that almost every feature is assessed as critical.
The result is that prioritisation loses its value.
If everything is important, nothing is important.
Effective risk-based testing requires the courage to make conscious priorities and accept that not all areas need to be tested equally thoroughly.
Lack of a shared method
Some organisations work with risk assessments based on gut feeling.
Others use different scales and criteria from team to team.
This makes it difficult to compare risks and create transparency.
A shared method and a common language around risk are crucial for creating consistent priorities.
AI creates a false sense of security
With the many new AI tools, we are also seeing a new challenge.
AI can generate hundreds of test cases in a matter of minutes, but this can create a false sense of quality.
The quantity of testing is not necessarily the same as the quality of testing.
If the generated test cases are not based on the most important business risks, critical defects may still slip through to production.
That is why testing expertise, risk understanding and the ability to prioritise become more important, not less important, as AI becomes a bigger part of testing.
Risk is not linked to concrete test activities
Some organisations are actually good at identifying risks, but the challenge arises when the analysis has to be translated into action.
A risk analysis only creates value when it influences:
- What is tested
- How deeply it is tested
- When it is tested
- Which test techniques are selected
- Which areas are automated
The risk analysis should therefore be an active management tool, not just a document that is archived.
The greatest benefits of risk-based testing
Organisations that work systematically with risk-based testing often experience:
- Better quality: The most critical defects are discovered earlier.
- More efficient testing: Time and resources are spent where they create the greatest value.
- Faster deliveries: Teams avoid spending unnecessary time on low-risk functionality.
- Greater transparency: Management, the business and development get a shared view of the most important risks.
A mature approach to risk-based testing is not about eliminating all risks. It is about creating transparency and making conscious decisions about where the organisation gets the most value from its testing effort.
The tester of the future must be able to do more than test
AI will undoubtedly change the way we work with testing. But the testers of the future do not necessarily need to become better at writing test cases.
They need to become better at:
- Understanding the business
- Analysing risks
- Prioritising the effort
- Facilitating dialogue between the business and IT
- Using AI as an effective tool
That is exactly why competencies within risk-based testing will become even more important in the coming years.
Do you want to learn how to work professionally with risk-based testing?
At TestHuset, we help companies create better quality through structured testing, quality assurance and competence development.
In our risk-based testing course, you will learn, among other things:
• How to identify and analyse risks
• How to prioritise the test effort effectively
• How to translate risk analyses into concrete test activities
If you prefer flexible learning, we also offer an on-demand course in risk-based testing, where you can learn at your own pace and work with concrete exercises, templates and tools.
Conclusion
Risk-based testing is no longer just a method for prioritising testing.
It is a crucial discipline for organisations that want to deliver high quality in a world characterised by increasing complexity, faster releases and new AI technologies.
AI can help us test more, but it is still people who must decide what is most important to test.
That is why professional expertise in risk-based testing is more important than ever.
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