AI for test automation. A step-by-step guide for modern QA teams

Let us top challenge you. Let’s say you have an educational app. You covered 95% of possible cases with automated scripts… but still got negative reviews release after release. 

Nonsense? Not quite. Here is another example.

The Atlassian team ran approximately 35,000 automated tests daily, but just one huge screw-up in 2022 put their reputation at stake. Just imagine hundreds of companies simply stopped. 

We all know how mature Atlassian’s automation is, but the suite hasn’t adapted to recent changes. Now, please pay attention: Automation alone isn’t enough.

For this reason, AI for test automation serves as a next-level add-on and a way to augment QA Engineers. Not racking the brain over brittle logic, not endless double-checking after every minor change. AI QA tools allow testers to shift their focus to strategic oversight.

Below is a step-by-step AI testing framework for your current workflow:

• Where traditional automation struggles and why
• What AI does differently and measurably better
• How to get started without breaking your current state

Let’s start with the illusion of “automated = complete.”

Why QA automation could do with an upgrade

Release cycles are getting faster, apps are more complex, and traditional automation can’t keep up. What worked for stable monoliths breaks under microservices, async UIs, and daily deployments.

Software releases are faster and more complex

Dev teams are forced to deliver faster and faster and faster. GitLab’s 2023 DevSecOps report revealed that the number of teams that now deploy code multiple times a day has doubled, and 59% of devs said they now release software at least 2x faster than a year ago. 

At the same time, the testing stage hits the top-4 reasons why some teams encounter delays, with 30% making an accent on monitoring. 

Microservices, asynchronous frontends (React, Vue, Svelte), and multiple environments — all these mean tests must account not only for UI or API behavior, but also for:

• Version drift between services
• Conditional rendering logic
• Real-time data updates
• Third-party integrations and dynamic DOMs

Traditional frameworks won’t scale

We are not about to blame conventional automation. Such tools have value, but rely on things that won’t change automatically: static selectors, scripted assertions, predefined user flows. Eventually, we get automation without true automation. 

Even elite QA teams spend 20-30% of their time maintaining tests at the expense of coverage: they skip many edge cases to avoid slowing down regression suites.

Hardcoded locators (e.g., id="submit") are a common culprit. SDETs can use relative XPaths, but these still break when layouts shift.

A touch of more pain:

• UI tweaks make tests flaky 
• Manual test data preparation → execution time increase
• Duplicate test logic across teams and services
• Lack of accountability and effectiveness due to little insight into impact and risk 

How QA automation with AI impacts the entire cycle

Speaking about AI, many authors hackneyed the term “next level.” And it’s true that artificial intelligence brings some “extra” to the usual routine. But it’s not in automation itself. We believe that the next level begins with a clear understanding of your internal workflows, goals, and resources available.

Modern intelligent test automation tools apply machine learning, can parse visual elements, and analyze history to deliver:

• Self-healing: When a button moves or changes ID, the tool updates the path automatically.
• Prioritization: AI identifies which areas are most risky and runs specific test suites instead of the full run.
• Test generation: New feature released — new test generated. Including untested paths and edge cases.
• Failure clustering and triage: AI distinguishes real bugs from environment-related flakiness.

Step-by-step: Building an AI test automation framework

AI won’t fix everything by itself. To get real value, you need a clear starting point, measurable goals, and a tool that actually fits your pipeline, not just another shiny dashboard.

Step 1: Spot weak links in your QA stack

Analyze where you lose time within the pipeline, what you spend money on that is unnecessary, and where the quality is off the mark. 

Most teams find the biggest gains from AI in regression testing (as probably the most common and time-consuming task), test flakiness (high maintenance needs and unstable environments), and test creation (because the “standard” process is too slow for modern software development).

Next-gen tools visualize failure patterns, cycle times, and areas of repeated manual intervention. Finally, you get a clear-cut picture of what’s wrong with your current state.

Step 2: Set specific and measurable goals

Don’t let things get vague. Define outcomes that will prove success:

• Regression cycle time reduced by X%
• Flaky tests cut by Y% over 3 months
• Coverage increased on core flows by Z%
• Escaped defect rate down quarter-over-quarter

Tie these to business impacts: faster releases, higher stability, lower QA overhead.

Step 3: Select the right AI QA tool

78% of respondents said they already use AI in software development or plan to in the next two years. And the most common pitfall is consolidation (64% of the surveyed mentioned this problem).

Prioritize tools with native CI/CD integration (GitHub Actions, Jenkins, CircleCI, etc.) alongside the following:

• Self-healing capabilities
• Thoughtful prioritization
• No-code/low-code interfaces so that even non-techies can use the platform

Take a look at OwlityAI. Test generation, prioritization, flaky detection, detailed reporting with actionable steps. By the way, we have a calculator to clarify the profits for your specific needs.

Step 4: Start with one layer of testing

No rush. Focus initial AI adoption on two things:

1. Smoke tests for critical flows
2. Regression suites tied to high-risk features

The results will serve as proof and as a guide to gather metrics, refine processes, and build team confidence.

Step 5: Iterate, expand, and feed AI better data

The better data you get to your model, the smarter it becomes. Track flaky test rates, coverage gaps, or slow runs, and feed insights back to the tool. Other good data points are prioritization accuracy over time and reduction in manual interventions.

Iterations make AI for test automation cut time and ensure compliance.

Set up alerts for high flakiness (depends on the project, but let’s say, >10% failure rate). Review weekly reports and cross-check with QA KPIs like Defect Density.

Refine your test scope quarterly and expand AI-driven coverage incrementally.

Avoid these mistakes when adopting AI in QA

AI can save huge amounts of time in QA but only if you use it wisely. Skip the basics, and you’ll end up with brittle coverage, messy data, and more headaches than before.

#1 Blind trust in AI

AI is powerful, but it isn’t magic.

• It doesn’t know your product context the way you do
• It can generate incorrect test priorities or incomplete flows
• It still needs human review, especially in the early stages

So don’t let it act as a black box, validate everything until you see consistent value.

#2 Underestimating human oversight needs

Once again, we are not talking about complete replacement. Rather, about complementation. Self-healing handles patterns, but edge cases and business logic still demand QA expertise. Modern tools can consider some edge cases, though.

#3 Insufficient training

Wait a minute. In previous paragraphs, we stated that modern tools don’t require extensive education. And that’s true. But your teams still need to know how AI works, principally. 

What this technology can do and what it can’t. What’s under the hood of artificial intelligence? How does it learn from data? The most successful teams begin with ground knowledge, gradually scaling their ability to apply AI to new areas. 

#4 Ignoring data hygiene

AI decisions are only as good as the data it learns from.

1. Messy logs → misleading test paths
2. Outdated bug reports → wrong priorities
3. Static, fake test data → unrealistic results

Always feed the system clean, production-like inputs.

#5 Rush

Replacing entire suites at once creates brittle coverage. Start small:

1. Validate AI in controlled, high-impact areas
2. Scale step by step
3. Focus on stability before expansion

#6 Overlooking integration points

Trying to replace everything overnight leads to brittle coverage.

1. Start with high-impact, repetitive areas like regression
2. Validate AI results in small batches
3. Gradually scale coverage based on proven stability

Step-by-step beats “big bang” every time.

#7 Failing to retrain AI models over time

With user base growth, with new features and software abilities, you’ll need a new level of testing. And this level starts from adding new data. Regularly refresh test data inputs and validation criteria to avoid drift.

Bottom line

We see AI test automation as the next big step in the organic development of software testing. AI tools are able to do many cool things, but at the moment, they can’t totally replace humans. 

This is why you need a solid foundation in your team to determine the entry point, choose a relevant tool, and fine-tune the testing process properly.

Start your QA automation with AI with a free 30-min consultation with OwlityAI’s team, or hit the button to directly start a free trial.