How QA leaders are tackling testing challenges with AI

2024 has shown significant breaches that have shaken our trust in the current state of quality assurance. 

One: A cyberattack on Change Healthcare compromised the information of about 190 million people (yeah, insurance data and medical diagnostics as well). This disrupted insurance payments for weeks, and the company had to pay approx USD 3B in one or another way. 

Two: National Public Data is an online background check service. Last year the company exposed 2.9 billion records (about 170 million people in the US, UK, and Canada). Suppose, it was costly both in brand and financial senses.

Three: Wrapping up 2024, Verizon’s Data Breach Report found that 68% of all breaches involved a human factor. So what does it mean to software testing? Again, the human factor. 

And the thing is not to blame irresponsible testers. The thing is that traditional manual testing is off the mark nowadays due to time constraints, limited resources, and the complexity of modern applications. Also, manual testing is prone to human error in software testing — and that’s a major reason why automation has become non-negotiable. 

But there is an app for that, and many QA managers are turning to AI in QA that automates 99.999% of the testing process and enhances efficiency, accuracy and ensures scalability.

Below is a comprehensive dive-in about AI testing as one of the software testing trends in 2025 and the reasons why wise QA managers are prioritizing autonomous testing solutions in the following year and beyond.

The growing challenges of modern QA in 2025

As of 2025, the lion’s share of companies use Agile and DevOps for their businesses’ tech part. That’s why the demand for QA teams is gaining ground. 

Yet, let’s face it: ensuring software quality is not a walk in the park, QA professionals fight with several daunting challenges. Namely:

• Lack of time: The adoption of Continuous Integration/Continuous Deployment (CI/CD) practices increased the percentage of QA teams that state they lack time for comprehensive testing (by 9%, by the way).
• Rapid feature-building: Apps often use microservices, numerous APIs, and even Internet of Things (IoT) integrations. To test this is definitely no picnic. 
• Need for faster feedback: Want to minimize costs and maintain quality? Focus on feedback then. It may not seem obvious, but it is. You need prompt and actionable feedback for developers to fix bugs earlier in the development cycle.
• Skilled QA pros shortage: Testlify found that 87% of surveyed US companies have skills gaps or expect them in just two years. And only 39% expect better talent availability over the next five years.

PoC: OwlityAI’s impact in the e-commerce niche

AI testing is the solution to almost all the challenges mentioned above. Let’s break it down by an example with an e-commerce company.  

Given: e-com startup got unexpectedly rapid growth of its user base and was experiencing frequent delays in product releases due to it and the used microservices architecture.

Map to the solution

• Check point: The company opted for OwlityAI and began the integration with a thorough assessment — they measured current metrics (for future comparison) and checked the existing testing framework to find potentially high-impact areas within it.
• Autonomous test creation: OwlityAI’s ML algorithms scanned the app’s codebase, analyzed user behavior and automatically generated relevant test cases. This resulted in a 95% drop in the time spent on manual test creation — a real-world example of how to reduce manual QA workload with autonomous tools.
• Ongoing testing: An e-commerce project integrated OwlityAI into its CI/CD pipeline and, this way, handed over test execution to the machine — Owlity started testing with every code update/commit. This is a clear example of how to solve testing bottlenecks with AI — developers receive feedback immediately and fix issues in the same sprint.
• Improvement with analytics: Reporting isn’t just numbers. OwlityAI provided a comprehensive overview of the health of the company’s testing process. Thanks to clear patterns, already prioritized areas for detailed assessment, and the report’s actionable form, the QA team translated these into test results. As a result, more informed decisions and resource allocation.

Why QA managers are prioritizing AI testing solutions

Faster releases, growing complexity, and limited resources push QA teams to their limits. Traditional testing can’t keep up. That’s why more managers turn to AI — to boost speed, cut manual work, and scale testing without scaling headcount.

Need for speed: faster testing cycles

The challenge: QA teams have always been under pressure. They must be like Jacks of all trades, shorten testing timelines and maintain software quality at the same time. This is how traditional automation appeared. Yet, it is still insufficient since it requires human intervention.

How AI solves it: AI in QA ensures some sort of independence, continuously executing test suites without manual triggers — significant acceleration to the entire development process.

Example: OwlityAI self-heals test scripts in case they don’t come off or are off your expectations. Also, the tool prioritizes tests, reducing regression testing time from days to hours. To improve testing speed with AI, OwlityAI automates execution, healing, and prioritization — reducing testing delays to almost zero.

AI improves defect detection

The challenge: Even though manual testing is often considered the best way to spot hard-to-detect bugs, in fact human testers and even scripted automation often miss them. Especially when it comes to frequently changing apps. 

How AI solves it: Machine Learning. These algorithms learn from ongoing application behavior and historical data which allows them to identify high-risk areas with increasing precision.

Example: Modern AI-powered testing tools can predict and flag potential defects before they affect released prod. This allows QA teams to remediate bugs earlier and ensure higher quality with lower costs.

It scales testing without increasing headcount

The challenge: Once you get more market share, you increase your power by adding new features, attracting more users, and so on. This way, hiring more people seems inevitable as you need to maintain test coverage and fix new defects.

How AI solves it: Imagine a ripped bodybuilder and a gymnast tasked with a leg split. Hardly the first sportsman will do it since there are a few bodybuilders who have the required flexibility. While the gymnast will effortlessly split. AI-powered solutions are sort of flexible gymnasts that can change their efforts depending on the load. 

Example: OwlityAI executes tests simultaneously and allows the QA team to run multiple test suites across browsers, APIs, and databases.

Expands test coverage

The challenge: The usual approach involves creating specific testing scripts, which is no longer relevant since you just can’t know every case that might happen.

How AI solves it: Autonomous exploratory testing feature scans your app continuously and identifies UI/UX inconsistencies, API failures, and performance hurdles. Next-gen testing tools map out app flows and execute unscripted interactions, including edge cases and hardly anticipated paths.

Addressing the QA skills gap with no-code AI testing

The challenge: 34% of mature QA teams find the shortage of skilled workforce is the most impactful. While many teams see further technological advancement, they just don’t know what to do with underperforming candidates. This translates into adoption delays.

How AI solves it: AI for overcoming the QA skills gap is gaining traction, with no-code tools enabling PMs and business analysts to participate directly in testing.

Insight: OwlityAI doesn’t need you to be an old-hand Director of testing. It just needs the link to your web application. Everything else is on it. 

How OwlityAI empowers QA managers to embrace autonomous testing

After so many data breaches in 2024, Y25 requires new approaches. QA specialists need an AI testing solution with next-level accuracy and effortless scaling. 

OwlityAI meets these modern challenges head-on. It has advanced automation, smart and independent test generation, and seamless integration. QA teams of all sizes are embracing AI QA tools like OwlityAI to accelerate releases while maintaining exceptional test accuracy.

How could OwlityAI transform your QA? Let’s break it down.

Autonomous scanning, test generation, and prioritization

OwlityAI analyzes the application’s UI to identify objects, elements, and workflows for automated testing coverage.

After a comprehensive (yet fast) analysis, the tool automatically creates detailed, AI end-to-end testing scenarios that cover UI, API, and integration layers. So, rest assured your app will be 95+% covered. 

It also categorizes test cases as High, Medium, or Low priority based on the previous risk assessment. This level of smart prioritization is what makes AI-powered QA so effective — test cases are ranked by risk, so teams can focus where it matters most.

Continuous maintenance

Keeping scripts and documentation up to date is time-consuming — but with self-healing automation, you reduce QA costs with AI and free up senior testers. Even traditional test automation often fails to keep up. 

OwlityAI detects UI and logic changes and updates test cases and scripts on its own. Moreover, it also removes (or updates) flaky, unstable tests and re-runs them with improved stability.

Cloud test execution and bug reporting

QA automation with AI allows OwlityAI to run tests across all environments simultaneously — making testing 95% faster and 93% more cost-effective.

An additional feature you don’t want to miss is scalability. OwlityAI runs tests on two threads by default and can scale up to five parallel threads. Again, this significantly reduces test execution time. 

And then, logs the detected issues into project management tools (if you integrated them) – Jira, Azure DevOps, Trello – so that every team member involved knows what to do.

CI/CD pipeline and API integration

OwlityAI’s usual integrations include Jenkins, GitHub Actions, GitLab CI/CD, and other DevOps tools. Thus, you can fine-tune it once, and it will autonomously continuously run tests within your CI/CD pipelines.

This is thanks to OwlityAI’s connection with existing test management and monitoring platforms via API.

QA KPI tracking

Track Code Stability Index, Defect Density, and Test Execution Trends with OwlityAI’s advanced analytics to gain real-time insights into testing efficiency and quality.

With its AI-driven test automation, adaptive learning capabilities, and full CI/CD integration, OwlityAI accelerates speed and enhances test accuracy — without the need for hiring new people and all associated costs. 

The icing on the cake is defect reporting. Generate comprehensive reports and focus on strategy, risk analysis, and product quality, rather than repetitive burdens

Future trends: What's next for QA automation in 2025 and beyond

A side note: many similar articles back up their statements with previously gathered data. And the further we go, the less we see adequate predictions. 2022, 2023, and 2024 taught us to be very careful with forecasting. However, we now see a few developments that probably will shape our tomorrow.

Increased reliance on AI-driven insights for predictive testing

Although the report by Katalon mentions the lack of capable AI testing tools as one of the most significant factors affecting technology adoption, there has been a significant shift towards AI in testing, with 45% of participants aiming to integrate AI into QA processes as their main objective. 

Interesting thing: 37% of those surveyed use AI tools for autonomous test case and script generation and 36% for test data generation. A separate place goes for QA automation trends in 2025: you can’t be a market leader without risk and early adoption of all the range of tech capabilities. That’s why some companies focus on the predictive capabilities of AI/ML models.

Growing adoption in agile environments

Agile is gaining ground. And with its demand for rapid delivery is driving another adoption — autonomous testing. AI testing tools automate test creation, execution, and maintenance, keep an appropriate quality level, and ensure everything is done on time

Quality and user experience as differentiators

Unique features and unmatched product quality are a new black. These may become new differentiators in all markets. As the offers number is growing exponentially, outstanding user experience sets the product apart. 

This puts additional responsibility on QA teams, and strategies that take into account functionality, performance, and usability become a cornerstone of modern QA practices. Because it’s all about meeting and even exceeding user expectations.

Integration with IoT, AR/VR, and blockchain

Do you have a smartwatch, Ray Bans, or maybe you have an entire smart house system? Internet of Things, augmented and virtual reality, and blockchain are becoming our present, not the far future. Actually, most of the mentioned things have already become our ordinary devices in big cities. 

And QA processes must adapt. AI-powered testing is also evolving to handle the complexities of these technologies. No one wants to expose what they listened to that Valentine’s Day or what they asked Amazon’s Alexa about their kid’s slang words. 

To stay ahead of these trends (secure and robust AI testing, not slang), QA managers should test new technologies and learn from this adoption. Efforts now will enhance the quality of your software later. And maybe this will turn your brand into a lovemark, who knows? Anyway, you wouldn’t refuse to improve your app quality, would you?

Bottom line

Without any double-talks, modern QA trends show us that the technology world is changing lightning-fast. So should software testing. 

And the main change is not even AI adoption or any of the QA automation trends 2025. It is the mind shift. Have a vision for your company, back it up with relevant and proven technologies, and focus on the most important part of your business — your customers.

OwlityAI can’t help you with goal setting or vision pitching, but it will ensure your app won’t disappoint your users.

FAQ

1. Can AI testing be trusted in high-risk industries like healthcare or fintech?

Yes, when properly validated and monitored, AI-powered QA tools can outperform traditional methods in detecting edge-case bugs and security vulnerabilities. However, they must be paired with human oversight, especially in sectors where regulatory compliance and explainability are critical.

2. What’s the difference between AI testing tools and traditional automation frameworks?

Traditional frameworks follow predefined rules and require constant script maintenance. AI QA tools, on the other hand, adapt to changes in real time using ML algorithms, enabling autonomous testing across dynamic applications.

3. Do I need data scientists on my QA team to implement AI-driven testing?

Not necessarily. Many modern AI testing platforms are low-code or no-code, allowing QA engineers, product managers, or even business analysts to initiate test generation and analysis without deep ML expertise.

4. How do I measure ROI from AI in quality assurance?

Look beyond time savings. Effective ROI metrics for AI in QA include reduced defect leakage, lower test maintenance costs, faster feedback cycles, and improved customer satisfaction tied to release quality.

5. Will AI testing replace exploratory testing done by humans?

No. AI in QA complements rather than replaces human intuition. While it handles scale and speed, humans are still essential for creative, exploratory, and usability testing — especially for unexpected user behavior.

6. How do AI QA tools handle unstable test environments or flaky tests?

Advanced AI QA platforms can detect flakiness, self-heal scripts, and adapt test logic based on real-time conditions — significantly reducing false positives and time spent troubleshooting broken tests.

7. Is AI testing suitable for legacy systems?

Partially. While AI excels in modern CI/CD pipelines and microservices architectures, its effectiveness in legacy systems depends on the testability and structure of the codebase. A hybrid QA strategy is often the best approach.