How, when, and why top QA engineers apply AI in software testing

Even big fish can fail if it doesn’t sufficiently test its approaches. In October 2020, PHE’s COVID-19 test-and-trace system failed epically: nearly 16,000 positive cases were not reported due to a simple spreadsheet glitch. 

The outdated Excel file format couldn’t handle the data volume and led to truncated records and delayed contact tracing.

Why do small and medium-sized businesses neglect double-checking and the benefits of AI testing, if even Public Health England, with their resources, caused such a mess? 

Modern apps grow in complexity and their data volumes, so traditional testing falters, leading to significant operational and reputational risks.

Let’s zero in on the "how," "when," and "why" of AI in software testing. 

Expect real cases, where AI brings value, the typical motivations of early adopters, and actionable guidance for QA teams.

Why QA engineers turn to AI in testing

Testing isn’t what it used to be. Modern apps evolve too fast, have too many moving parts, and break in too many unexpected ways. Manual checks and brittle scripts simply can’t keep up, so QA teams bring AI into the mix.

To deal with the complexity of apps

Users want more from modern apps. This forces engineers to pack their products with many, many, and a bit more attributes: dynamic interfaces, integrations, and this new-you-should-try feature.

For instance, e-commerce platforms must handle real-time inventory updates, user personalization, and third-party payment gateways, and this is without hurting user experience. So, nothing groundbreaking that testers burn out when conducting all this scope manually. 

AI-driven testing tools adapt to growing complexity:

• They recognize patterns and adjust to changes in the user-application behavior. 
• They handle adaptive UI changes in different environments.
• They ensure compatibility and consistency in these environments.

To increase testing accuracy and stability

Manual approach. Let’s take 500 regression test cases; each will take approximately 5 minutes to execute manually = 40+ hours in total. Add the time needed to document and report issues.

Traditional automated approach. It reduces execution time but requires ongoing script maintenance. Fitness, Mental Health, and other frequently changing apps will bring your testers up the wall. 

Autonomous QA solutions approach. With it, the tool handles the entire cycle: from scanning the app to analyzing test performance. This approach allows QA teams to focus on exploratory testing, usability assessments, and strategic test planning.

To scale their efforts without increasing resources

Manual approach scales when your resources scale, particularly team size. You won’t hire a skilled QA specialist in one day. In addition, you may not need that already hired specialist for a long period or for a stable scope (you probably discussed during the hiring process). Inflexible.

This is a typical marker when to use AI in testing. AI-powered tools scale effortlessly: they conduct testing across various environments, devices, etc., simultaneously, and you don’t break any agreements with native changes. 

This way, you keep the development pace and maintain quality without overextending the team.

To reach the market faster

You can outperform your competitors due to the speed of implementation. The user found an inconvenience within your app, you got feedback, developed the solution/feature/fix, tested it, and rolled it out to the market.

The entire cycle takes time. But with AI-powered QA solutions, you feel like getting an extra hour in a day. Modern tools integrate seamlessly into CI/CD pipelines, and streamline the giving-receiving feedback cycle on code changes. 

You spot issues, find resolutions, and ensure development progresses without unnecessary delays.

Yet, it's also important to understand the challenges of AI in QA: the need for quality data to train AI models and the potential for AI to misinterpret unexpected behaviors. 

AI in software testing: areas of application

Even though AI for QA engineers brings a lot of value, it doesn’t mean it is a Jack of all trades; it will take over the parts that waste your team’s time or frequently break under pressure. It fits best in the following areas:

During regression testing

Highly repetitive and bug-missing stage. Every code change, every UI tweak affects stable features. Ask any Manual QA Engineer, and they will tell you that executing it or even maintaining automated scripts is a massive time drain.

This is where AI-powered testing tools come in:

• Auto-detect changes to UI elements and application flows.
• Update test scripts on their own (self-healing) and remove manual script edits.
• Re-run relevant tests selectively (they don’t execute the entire suite every time).

Example: OwlityAI notices changes in the structure of a checkout page (button label change, a reordered form, etc.) and adjusts the test script automatically based on visual and behavioral analysis.

In CI/CD pipelines

Software testing should never slow the pipeline down. In Continuous Integration and Deployment environments, timing matters.

AI fits here because:

• It triggers tests automatically as part of every commit or merge.
• It provides real-time feedback directly into your CI dashboards.
• It eliminates the need for manual test scheduling or execution.

As a part of Jenkins or GitHub Actions pipeline, autonomous QA solutions validate builds instantly and post results back to your tracking systems.

For testing large-scale applications

Enterprise-level apps usually include complex workflows, multiple environments (dev/stage/prod), and thousands of test cases. Even if you can afford an army of testers, is it worth such expenses? Seems like a resource sink.

Use AI to:

• Run tests across browsers, devices, and environments simultaneously.
• Simulate real-world conditions (high traffic or unstable network behavior).
• Detect performance regressions under varied loads.

Case to imagine: A healthcare portal handling 200K daily users. Performance testing under load with role-based access controls, backend validations, and API dependencies is ideal for AI-assisted execution.

When identifying hard-to-spot defects

Rare yet vital things are edge cases, rare usage flows, and unstable third-party services. They are difficult to detect manually, simply because humans don’t test what they don’t expect.

Let autonomous testing do it for you:

• Track test result patterns and flag outliers.
• Analyze anomalies across environments and builds.
• It learns from previous defect patterns to predict risk zones.

During maintenance

Updating scripts every time the app changes is one of the most annoying and time-wasting parts of test automation.

Artificial intelligence cuts this down:

• Recognizes UI changes and updates selectors or flows.
• Re-links broken test steps to new components.
• Automatically revalidates the test outcome after changes.

Dev team updates a component name or rearranges the UI, but your tests adapt and keep running. Ideally. 

The ways to apply AI in software testing

AI in QA isn’t about replacing people, it’s about cutting the grunt work, stabilizing fragile test suites, and spotting risks before they hit production. Here’s how teams actually use it in practice.

Test case generation

Artificial intelligence scans the app’s behavior, backend interactions, and usage data and auto-generates test cases. You may suspect random, but these cases depend on learning data, so they reflect real user flows and usage probabilities.

This way:

• You reduce manual effort.
• You ensure realistic, usage-based test cases.
• Do nothing while the tool updates suites to reflect new features or components being deployed.

Self-heal your tests

Again, frequently changing elements often break previous tests. AI identifies the issue and fixes the test logic.

This means fewer interruptions during builds and significantly lower false fail rates. Eventually, you get a stable system, even when the UI evolves.

Sweet bonus is that self-healing saves hours each sprint (previously went into script triage and rework).

Smart defect detection

AI goes beyond binary pass/fail and digs into test results for patterns. It can flag issues before they become recurring defects.

Next-gen testing tools support:

• Root cause analysis based on test behavior and logs.
• Correlation between test outcomes and recent code changes.
• Predictive alerts for modules likely to fail soon.

This way, the testing team stays ahead of recurring bugs and can handle reliability proactively.

Real-time monitoring and reporting

Instead of manually checking logs or waiting for QA to summarize results, AI gives live, actionable test insights:

• Visibility into which areas of your app are risk-heavy.
• Test coverage gaps identified and flagged.
• Reports that align with compliance requirements like SOC2 or HIPAA.

Many tools generate export-ready PDF and CSV reports with highlighted key QA KPIs (test coverage, Defect Density, and others).

Integration with existing tools

To avoid breaking everything down, ensure the tool integrates smoothly into your pipeline.

OwlityAI supports:

• Jenkins and GitLab CI pipelines, and triggers tests directly from build jobs.
• Exports bugs automatically with logs and screenshots to Jira and other tracking tools.
• Updates test status via Slack, email, etc.

Best practices for getting started with AI testing

AI testing isn’t a “plug it in and forget it” deal. To see real impact, you need to pick the right areas, roll out in controlled steps, and give your team just enough know-how to use it effectively.

Select sweet spot areas for AI testing

Obvious but still important note — target tasks that drain time and deliver diminishing returns.

Consider these:

• Regression testing: It’s repetitive, time-consuming, and frequently breaks after UI or API changes.
• Performance testing: Put it under high load, with hundreds of concurrent users across environments.
• Cross-browser and device compatibility: Check-in across 10+ browser/OS combinations.
• Test data creation: Some may say that fictional data is another form of self-destruction. However, modern technologies can already generate realistic data sets based on schema analysis.
• Flaky test triage: Spot patterns in unstable test results and rerun them to confirm or dismiss failures.

Start small and scale

It is not an overnight story. Begin with one flow, one area (check the passage above), validate ROI, then move to broader coverage.

Slow but steady matters because:

• It reduces the risk of poor test quality due to unfamiliarity with the tool.
• It gives your team time to adapt.

Financially, rolling out AI incrementally means lower upfront cost: covering login, checkout, and search workflows may reduce regression cycles by 60-70% before you even touch secondary modules.

Tip: Clock your execution time during one sprint, take benchmarks of defect rates, and evaluate script maintenance effort to compare results pre- and post-AI integration.

Invest in training

One of the challenges of AI in QA is making heads or tails of it. Modern AI testing tools are very intuitive, they don’t even require previous QA experience. 

At the same time, you should understand how to work efficiently within the specific tool.

Consider these resources:

1/ Vendor’s onboarding guides and live sessions

For example, OwlityAI offers real-life application flows, CI/CD setup walkthroughs, and advanced configuration tips tailored to QA professionals.

2/ Specific courses from testing companies

For example, Test Automation University.

3/ Coursera

Machine Learning for Everyone (QA-focused track) or similar directions to understand the core logic of AI engines.

Collaborate with development teams

Align your testing workflows with your CI/CD processes:

• Set up shared goals (use the SMART framework).
• Trigger tests automatically on pull requests or merges.
• Share dashboards with test status, coverage trends, and blocker visibility.

Keep tracking metrics to refine processes

Gauge these metrics over time:

• Test coverage
• Defect detection rate
• False positive rate
• Execution time per cycle

They’ll help you to decide where to invest next: more environments, broader flows, or, maybe, UI health monitoring.

Challenges and misconceptions about AI in QA

AI in testing gets hyped, misunderstood, and sometimes feared for the wrong reasons. Let’s clear the air, here’s what people assume vs. what’s actually true.

POV: AI replaces QA engineers

Real:

Test case creation, flaky test detection, and UI change tracking — this is the scope of tasks that AI-powered QA solutions take on. 

We suppose that many QA Engineers will be happy to hand over these duties to someone else and dedicate themselves to exploratory testing, risk-based test design, and user-impact analysis.

In fact, AI for QA engineers becomes something like a highly motivated personal intern.

POV: AI is only for big companies

Real:

OwlityAI and other modern tools have diverse pricing to support small dev teams, mid-sized QA departments, and enterprise workflows. 

They use API-first integration and cloud execution, so you scale at your own pace.

POV: AI is too complicated to implement

Real:

Modern AI-powered testing tools target developers and QA engineers, not data scientists:

• GUI-based setup for non-coders.
• API integrations for dev-centric teams.
• Out-of-the-box support for Jenkins, GitHub Actions, Jira, and more.

It usually takes a few hours.

Challenge: Initial setup effort

Real:

Onboarding still requires planning: you need to identify key flows, prepare environments, and train your team. But that investment pays off quickly.

Four-step how-to:

1. Pick 2-3 test flows with high regression overhead.
2. Connect your CI tool (GitLab, Jenkins, another one).
3. Use auto-generated reports to refine which areas to scale next.
4. Within 2-3 sprints, you’ll already see reduced test times and more stable builds.

Bottom line

Your app gains momentum, you onboard new users, and the risk of incorrect performance soars. You feel like it’s time to scale, but are scared of a costly mistake.

This is exactly the moment when to use AI in testing. 

Among the benefits of AI testing are ongoing scanning, understanding behavioral patterns of both sides (the app-the user), generation of relevant test suites, and refining current stash if any tests go wrong. 

This helps to save time, money, and some nerves.