5 proven ways to improve your QA strategy with AI

In 2025, we don’t hold a candle to the builders of the Egyptian pyramids. While building cutting-edge software, we sometimes fail to ensure its robustness. The testing industry in general faces many challenges, from increasing complexity to a significant shortage of skilled QA professionals. 

The fourth World Economic Forum report indicates that machines will complete more tasks instead of humans (+9%). But what’s more scary is that almost half (44%) of employees already need reskilling. The shortage of skilled tech pros makes many companies shake like a leaf because of upcoming spending on training and the absence of any success guarantees. 

In this stage, autonomous software testing is a game-changing approach to tackling modern challenges and one of the most effective answers to the question of how to improve QA process with AI. With AI and ML, next-gen testing streamlines processes and enhances the accuracy of the entire process. 

Let’s cover five test autonomous tips on the immediate overall software testing strategy improvement. 

1. Prioritization matters for future automation

The first step in how to improve the QA process is clear prioritization. Not all tests bring equal value, so focusing on high-impact areas saves time and strengthens your overall strategy.

Identify high-profile areas

Two things here: the Pareto principle and the Eisenhower matrix. Zero in on the most important components of your app and utterly urgent tasks. Identify vital areas of your development/testing processes and take 20% of actions that will deliver 80% of the result. And scale later, of course.

Which tests are important at which stage:

• MVP/Beginning — Regression testing: If your app updates frequently, regression testing is the most important type for you. You don’t want new code to break existing functionality, do you? Including this to 20% prevents time-consuming manual checks.
• Operational activity — Security testing: Y24 was packed with data breaches: AT&T, Change Healthcare, and Synnovis contributed to over 1B stolen records. So, security testing must be a priority. Autonomous testing solutions scan for security weaknesses and ensure robust protection for sensitive data.
• Further development — Performance testing: As application scales and user interactions grow, performance testing becomes more significant. AI-powered testing tools alleviate bottlenecks and downs and maintain a better user experience.

How to assess important areas

Were the previous tips too vague? Let’s run through more specific considerations for determining which parts of your app could bring about issues.

• Analyze historical data: Manually review previous defect reports to identify modules that frequently failed more often. High defect density is a sign to automate those modules first.
• Conduct risk assessments: Engage key stakeholders to evaluate the risk. Figure out key business functions and integrate next-gen testing into the components related to those functions.
• User interaction metrics: Assess where users spend the most time within your application. High traffic is another sign indicating possible prioritization (and the benefits from it). 

Quantitative assessment framework:

• Defect Density Index (DDI): Reveals how often bugs popped up in the past. 
• Change Volatility Metric (CVM): How often the code was changed.
• User Impact Score (UIS): Evaluates user-facing functionality criticality.

The tip: Code analysis tools assess risk and identify priority to particular areas with 71%+ accuracy.

Implement AI testing in these areas

The next step is to configure the chosen tool. Choose a tool, set it up, and start pilot executing. 

OwlityAI has fine-tuned Artificial Intelligence under the hood. It’s built to automate testing processes, ensure thorough coverage, and serve as a practical answer to how to improve QA process with AI. How it’d be if you choose OwlityAI:

• Smart scanning, smart tests: OwlityAI analyzes the app, particularly the ways users interact with it. Smart scanning also includes regular check-ups of the app's UI and functionality, which provides the crude material to cook test scenarios.
• Testing grows with you: You won’t stay frozen (till you can handle new leads). OwlityAI monitors all changes associated with your growth and automatically updates test scripts. Minimal manual maintenance, utmost relevant tests.
• Instant execution: OwlityAI speeds up every cycle with parallel and efficient execution. A typical suit is executed in days or even weeks, while OwlityAI completes the same test suite within hours.
• Keen eye on performance: During performance testing, OwlityAI identifies and reports on 4xx and 5xx network errors (detailed insights included). Thus, you can fix issues in future runs.

2. Integrate autonomous testing into your CI/CD pipeline

When autonomous testing is embedded into CI/CD workflows, teams gain immediate feedback on code quality, detect risks earlier, and release with greater confidence. This approach transforms QA from a bottleneck into a driver of delivery speed.

How to adjust CI/CD integration

The most logical way to enhance software quality is to focus on QA process improvements, ensuring quality checks are more frequent and effective. So to ensure faster feedback to developers and their prompt response, embed autonomous testing at every stage of the CI/CD workflow.

CI/CD testing transformation framework:

1. Architectural integration points

Critical pipeline stages where you want to trigger AI testing:

• Pre-commit code analysis
• Build validation
• Deployment readiness checks
• Post-deployment verification

1. Intelligent test orchestration

Improving the testing strategy, make sure your testing tool can do:

• Context-aware test selection
• Predictive risk assessment
• Parallel test execution
• Intelligent test prioritization

The most powerful benefits of AI testing

There are a lot of them. However, the one that stands out is the ability to catch issues early in the development process. This significantly reduces the cost and effort spent fixing bugs, making it a cornerstone of any test process improvement strategy.

IBM System Science Institute found that fixing a defect during production is up to 100 times more costly than addressing the same defect earlier. Integrating AI testing into the CI/CD pipeline helps to avoid the risks of costly delays and rework.

The saying “a stitch in time saves nine” perfectly describes another powerful benefit of AI testing tools — cost-effectiveness. They reduce the need for extensive manual testing resources so that you need a smaller QA team to meet your goals.

If you don’t want to fall behind, you should create trends by noticing their spring-throughs. Autonomous testing is one of such. Next-gen testing tools support continuous integration and delivery, making it a forward-looking investment that keeps your QA strategy relevant.

How to prevent costly delays and rework

Ongoing software quality validation lowers the possibility of experiencing the disruptions that come from discovering critical issues late in the deployment cycle. These tools assess functionality, performance, and security to ensure every code change meets the established quality standards. 

This proactive approach minimizes the likelihood of regressions, reduces the need for extensive manual testing, and shows exactly how to improve QA process through test automation.

3. Automate test maintenance

Test maintenance is one of the most time/effort-consuming things in Quality Assurance, which makes improving QA process initiatives critical for scaling fast-moving teams. The modern software development pace is so fast that you need to assemble a dedicated manual testing team for just one app. And just think of weeding out candidates, investing in their training, etc. Yet, even those problems are not exclusive.

Complex microservice architectures force testers to tease out intricate dependencies. Considering 35-45% test script obsolescence per quarter, this requires significant manual intervention. What else?

Common challenges

• High maintenance overhead: With frequent updates, current test cases often become outdated. In large organizations, there are many and many test suites, which can consume a disproportionate amount of QA resources.
• Flaky tests: They intermittently fail without any changes to the application and lead to confusion and wasted time You just can’t be confident in the testing process. The root causes usually vary from timing issues to environmental factors. 
• Lack of version control: Consistency in test cases is another challenge. You have several devs contributing to the codebase, and it becomes daunting to “keep the style”. Manual version control of test scripts can result in discrepancies, where different versions of a test exist across various branches or environments. So nothing groundbreaking in your inconsistent test results. Moreover, one test can be tied to another, and these dependencies can break down the lion’s share of the test process.
• Documentation deficiencies: While updating test suites, you should also update documentation. This doesn’t allow new team members to effectively contribute to test maintenance.

Reduce the burden of test maintenance

Autonomous testing solutions are the way forward for test process improvement. They can analyze code changes, automatically refine test cases, and cut maintenance costs. Namely:

• AI-powered test script self-healing
• Automatic locator strategy updates
• Dynamic test case optimization
• Machine Learning test optimization

Self-healing tests: Ongoing scanning changes in the application’s UI and functionality: when an element in the application changes, the AI tool updates (or not, depending on the testing strategy and the need) the corresponding test case on its own. Example: button’s ID changes, and, let’s say, OwlityAI adjusts the test script accordingly without human intervention.

Dynamic test generation: Based on all interactions with the app, the tool creates new test scenarios. Significant time savings, innit? Especially if we’re talking about edge cases, which are hard to comprehend completely. 

Assigning the priority level: By analyzing usage patterns and defect history the AI testing tool assesses the importance of various test cases and prioritizes them. Additionally, many tools can remove redundant tests and unite similar ones.

Auto-reporting: You definitely could do with detailed reports introducing flaky tests and insights into the overall health of the test suite. For example, OwlityAI has in-built comprehensive reporting and analytics features.

Enhance test coverage with AI-driven exploratory testing

Groundbreaking experiments set new parity in the human vs. AI battle. DeepMind revealed that AI systems can demonstrate reasoning capabilities that surpass human performance in some problem-solving scenarios. 

With Sam Altman’s and other AI leaders’ claims, the buzz surrounding Artificial General Intelligence (AGI) has reached a critical point. AI scientist Ray Kurzweil predicts that AGI could appear between 2045 and 2060, while the mentioned leader of OpenAI says he expects it in the next 5 years.

Implement AI-driven exploratory testing

Traditional testing methods often flop in identifying edge cases or unexpected user behaviours, which is why companies increasingly adopt AI for improving testing accuracy and coverage. This may seem funny, considering that humans often don’t even know the reasons for their actions themselves. Machines know, though. 

On this, scripted tests are effective for validating known scenarios, but they can’t mimic the diverse ways users might interact. This means some issues may go unnoticed until after deployment. How to avoid it? 

AI-driven exploratory testing:

1. Intelligent scenario generation

• Generate thousands of unique test scenarios
• Simulate complex user interaction patterns
• Find unexpected application behaviors

1. Adaptive testing strategies

• Machine learning-powered test path creation
• Dynamic risk assessment
• Continuous learning from test execution results

Next-gen testing tools can automatically generate and execute sophisticated scenarios, offering a new approach to testing and improving QA without heavy manual scripting, like rapid clicks, unexpected input combinations, or navigating through the application non-linearly — this really beefs up test coverage.

Monitor and optimize test performance with real-time analytics

Real-time monitoring turns raw test execution data into actionable insights. It’s one of the most effective ways of driving QA process improvements, ensuring your team can react quickly to failures, bottlenecks, and coverage gaps before they escalate.

Leverage analytics for continuous improvement

Without effective analysis, you won’t spot inefficiencies or areas requiring attention, which limits your ability to know how to improve the QA process in the long run. For this reason, there is real-time analytics:

• Test efficiency: Tracks the duration and resource consumption test by test. This streamlines bottleneck identification. If any test takes longer than expected, devs and QA specialists will have data for prompt resolution.
• Failure patterns: Pinpoints recurring issues or specific test cases that frequently fail. Find reasons and hints on ways to solve the problem. 

Coverage gaps: Reveals gaps in test coverage: if certain functionalities are consistently less tested than others, it’ll alert your team.

How OwlityAI’s analytics can help

Recent years have been marked by data-driven decision-making, and software testing is no exception. Advanced analytics play a major role in QA process improvements, guiding better test execution. OwlityAI has advanced analytics that beef up this process.

• Comprehensive reporting: Reports on test execution outcomes, including pass/fail rates, execution times, and resource usage. 
• Actionable insights: All thanks to Machine Learning algorithms — they suggest modifications to test cases based on historical performance data and show how you should adjust the testing approach.
• Real-time dashboards: All dashboards present real-time data and are intuitive. The special word goes to the contrast — even people with bad sight can effortlessly recognize elements.
• Historical analysis: OwlityAI evaluates past test performance and identifies long-term trends.

Bottom line

AI-powered testing tools are the next big thing. They reduce human error and improve over time without your oversight and much effort. The new technology morphs traditional testing methods, making it easier, more thorough, and a reliable method of AI for improving testing efficiency.

With CI/CD integration, this approach ensures quality checks at every development stage, driving continuous QA process improvements and long-term scalability.

If you’re at the starting line with autonomous testing, these five tips provide a clear guide on how to improve QA process step by step. Contact our team for a free consultation, or start off with the demo.

FAQ

1. What is the best way to start implementing AI in the QA process?

The best way to start is by running a pilot project. Choose a small but critical module, integrate AI tools, and measure results. This approach reduces risk and helps you see how QA process improvements scale before full adoption.

2. How to improve QA process with AI if my team has no prior experience?

You don’t need in-house AI expertise from day one. Many AI for improving testing tools come with low-code or no-code options that let QA engineers and developers build automated test cases without deep ML knowledge.

3. What skills are needed to use AI in software testing?

Implementing test process improvement with AI requires QA fundamentals, scripting knowledge, and a basic understanding of data quality. Teams benefit from training in interpreting AI-generated test cases and analytics rather than building AI models themselves.

4. What are common challenges in implementing AI testing?

The main hurdles are tool integration, test data preparation, and managing false positives. Companies that succeed usually adopt a phased approach to testing and improving QA, focusing first on high-value test areas.

5. Can AI testing replace manual QA completely?

No. AI tools accelerate automation and highlight risks, but humans are still essential for exploratory testing, usability checks, and final validation. The goal of AI is to improve QA process efficiency, not to remove QA professionals.

6. How do I measure success after implementing AI in QA?

Track metrics like defect detection rate, test coverage, execution speed, and maintenance effort. Consistent improvement in these areas shows that AI test automation benefits are materializing.