Autonomous Testing Explained: The Future of QA with AI and Human Collaboration

With AI-augmented testing tools becoming more advanced, it's getting easier to imagine a future where automation takes over almost every aspect of business operations—including quality assurance (QA). As AI continues to revolutionize industries, organizations are embracing its ability to streamline testing processes, speed up release cycles, and tackle the complexities of modern software ecosystems.

However, even as these tools grow more sophisticated, there's one thing they can't replace (at least, not yet!)—the human touch. According to Leapwork's AI and Software Quality: Trends and Executive Insights report, 68% of C-suite executives believe that human validation will remain essential for ensuring quality across complex systems.

Despite the strides made in AI-driven QA, human oversight is still a critical piece of the puzzle.

"AI isn’t magic—it’s a tool. While it can automate repetitive tasks and handle large-scale testing, it still needs human oversight to ensure everything is working as intended," explains Robert Salesas, CTO of Leapwork. This highlights a key reality: while AI has immense potential, it can't yet operate without human guidance, especially in complex, high-stake environments.

How does this tie in with the concept of autonomous testing? What is autonomous testing even, and is it something companies should be working towards? All this is something we’ll cover in this blog. 

What is autonomous testing?

Autonomous testing refers to the use of AI to automatically design, execute, and evaluate software tests without requiring human intervention. Unlike traditional test automation, where tests are predefined and need to be manually maintained, the idea of autonomous testing is that it can learn from application behavior, adapt to changes, and generate new test cases on their own.

Key characteristics of autonomous testing include:

• Self-learning: The system can learn from previous test runs, adjust, and improve over time.
• Adaptability: It adapts to changes in the software or environment, reducing the need for manual updates and maintenance.
• Efficiency: Autonomous testing can dramatically speed up the testing process by running tests continuously and autonomously, freeing up testers to focus on more complex, value-adding activities.
• Error detection: The AI can identify patterns and anomalies that humans or traditional test scripts might miss, improving test accuracy.

The goal of autonomous testing is to streamline the testing process, reduce manual intervention, and ensure higher software quality in less time.

The stages of testing

The journey from no testing to fully autonomous testing can be seen as a maturity scale with several distinct stages. Each stage represents an evolution in how testing is conducted, from minimal or no testing all the way to self-sustaining, AI-driven testing. 

Here’s an outline of these stages:

No Testing → Ad Hoc Testing → Manual Testing → Automated Testing → AI-Augmented Testing → Autonomous Testing

This progression reflects an increasing reliance on technology and AI, with each step improving efficiency, accuracy, and scalability. Organizations typically move through these stages as their software and testing needs grow, aiming to reduce manual effort and improve test coverage and software quality over time.

1. No testing

• Description: In this stage, no formal testing is performed. Bugs and issues are typically discovered by users or developers during development or in production.
• Challenges: High risk of bugs going unnoticed, poor software quality, reactive approach to issues.

2. Ad hoc testing

• Description: Testing happens sporadically and informally. Individual developers or QA teams manually test the software without a structured process or predefined test cases.
• Challenges: Lack of consistency and documentation, prone to human error, difficult to reproduce bugs, limited coverage.

3. Manual testing

• Description: Structured test cases are created, and testers manually execute these tests. Testing becomes a formalized part of the development process, often conducted at specific stages (e.g. after development sprints or pre-release).
• Benefits: Higher coverage, more consistent results, formalized defect tracking.
• Challenges: Time-consuming, labor-intensive, prone to human error, difficult to scale.

4. Automated testing

• Description: Test scripts are created to automate repetitive tests (e.g. regression tests). These scripts run without human intervention, often triggered by events like code commits (CI/CD). 
• Benefits: Saves time on repetitive tasks, faster feedback loops, scalable, enables continuous integration and delivery.
• Challenges: Depending on the complexity and sophistication of the tool itself, initial setup can be time-intensive and tests may require maintenance when the application under test changes.

5. AI-augmented testing

• Description: AI is used to assist testers in generating and maintaining test scripts, optimizing test coverage, and identifying high-risk areas of the application that require more attention. AI can also help by analyzing test results to pinpoint patterns and prioritize issues.
• Benefits: Reduces the burden of script maintenance, optimizes coverage, improves risk detection, and enhances test execution by predicting likely failure points.
• Challenges: Requires skilled oversight. Relying too much on AI without human judgment can lead to missed issues, as AI lacks the ability to fully understand complex scenarios.

7. Autonomous testing

• Description: In this most advanced stage, AI is responsible for the entire testing process—from generating test cases to executing them and analyzing the results. The system learns from software behavior and adapts test cases dynamically based on changes in the application, user behavior, or past test results.
• Benefits: Fully scalable, continuous testing with minimal human oversight, AI learns and adapts to changes automatically, reduced time to market, and higher quality outcomes.
• Challenges: Still evolving technology, high initial setup, and integration complexity, requires careful monitoring and validation of AI decisions.

Are we ready for autonomous testing?

Simply put, AI isn’t perfect, and probably never will be. 

Our AI survey showed that 68% of AI-adopting businesses have encountered reliability, performance and accuracy issues, underscoring the need for ongoing quality control. 

While AI can assist in automating many repetitive, time-consuming tasks, it still lacks the nuanced understanding of context, creativity, and intuition that human testers bring. In unpredictable or ambiguous scenarios, human judgment is critical in ways that AI may struggle to replicate.

As Robert Salesas explains, “AI can generate tests, but it relies on the completeness of the data provided. It lacks agency. Human oversight ensures we bridge this gap. AI can assist in generating test cases, but humans are still needed to oversee and refine what the AI produces.”

This is why the future of testing isn't about replacing human testers but transforming their roles. As AI-augmented testing tools become more prevalent, QA roles are evolving. Instead of being phased out, human testers will increasingly focus on overseeing AI, interpreting data, and applying critical thinking to ensure testing accuracy.

This shift is reflected in the fact that 53% of C-suite executives report an increase in new positions requiring AI expertise. The focus is now on blending human insight with AI's speed and efficiency to drive better outcomes.

Humans and AI: The perfect partnership

The partnership between AI and humans promises to deliver more effective and comprehensive testing. 

AI can handle large-scale tasks quickly and consistently, but it still requires human oversight to ensure software not only functions properly but also meets business goals, user needs, and compliance standards. Robert elaborates, “AI excels in understanding repetitive tasks, like regression or API testing, but human oversight is critical to ensure that the output aligns with what the business really needs. AI alone can’t make judgment calls on what's important.”

By combining AI’s efficiency with human creativity and critical judgment, businesses can achieve higher-quality outcomes and maintain trust in their systems. As Robert reminds us, “We have to remember that AI isn't magical—it's statistical. It follows patterns and prompts, but it needs to be monitored to make sure the outputs are correct. For this reason, testing AI should also be a core priority for businesses going forward.”

As AI continues to evolve, so too will the responsibilities of those working in the field of QA. In conclusion, the future of quality assurance is not about replacement but collaboration—where AI and humans work together to achieve better results than either could alone. Robert sums it up well: “You can’t fire an AI when something goes wrong. At the end of the day, you still need a human to sign off and take responsibility, especially in high-stakes scenarios where trust is crucial.”

Download our report, AI and Software Quality: Trends and Executive Insights, to gain a comprehensive understanding of how AI is reshaping software quality. This report offers key insights and actionable solutions to help your business adapt, scale, and consistently deliver exceptional user and customer experiences in today’s AI-driven landscape.

About the authors

Maria Homann has 5+ years of experience in creating helpful content for people in the software development and quality assurance space. She brings together insights from Leapwork’s in-house experts and conducts thorough research to provide comprehensive and informative articles. This AI articles is written in collaboration with Robert Salesas, CTO of Leapwork. He heads the global Product, Engineering, and CloudOps teams, driving the AI-driven test automation space. He is passionate about all things DevOps, SaaS, and AI.