How AI Automation is Changing QA Careers

Carsten Camon

Sponsored Post: Careerist

With the exponential explosion of artificial intelligence (AI) throughout all industries in recent years, it’s no wonder it’s only become more and more prominent in quality assurance (QA) processes as well. Every day, companies are adopting new AI-powered testing tools to use within the development lifecycle, but with so many options available, how do you determine which AI tools are right for your goals, and how can you be sure you’re using them to their best advantage?

In this article, we’ll take a look at the effect of AI automation in QA careers, including how to make sure you’re getting the most out of the process—from choosing the right tools, to learning how to build your skills.

Growth of Artificial Intelligence in QA Roles

As with industries across the board, quality assurance has seen a significant impact from AI and machine learning. So it’s no surprise that, as automation is adopted into software testing, that the number of available tools is also expected to continue increasing. As Careerist notes on their website, the AI automation market size in 2025 was $638 billion, and the future global AI market is expected to reach $3.68 trillion by 2030.¹ With such high expectations, you may be wondering if using AI technologies is right for you as well, and how a QA team can ensure it’s making the most of them.

Why Use AI-Powered Testing Tools?

Saves Time and Effort

Because machine learning models are designed to recognize patterns, they’re able to recognize issues at a faster rate than through manual testing. This can significantly reduce the time and effort required by humans, ultimately speeding up the deployment process as the model analyzes an increasing amount of data to find bugs and detect issues earlier.

Reduces Cost

As more AI-driven testing strategies are adopted, it can also save costs for the company in the long run. Now, the everyday repetitive tasks that had required time and manual effort have been optimized and automated, which creates an opportunity for reducing the expenses that had been allotted to these manual tasks in the past.

Increases Scalability

Since artificial intelligence is self-learning, the door is opened to more scalability as it’s able to grow and learn through more and more data.

Considerations About Using AI Testing Tools in Your QA Job

Monitor All Results

Just because an AI tool produces an output, that doesn’t necessarily mean it’s accurate or even relevant, so critical thinking is still necessary. If you’re using an AI, be sure to do a thorough code review and consistently double-check results to be sure it’s accurate and useful.

Choose the Right Tools for the Tasks

As AI and machine learning has increased in popularity over the past few years, the number of AI-driven tools appearing on the scene has snowballed. Therefore, it’s important to do your research and choose a method that fits in with your specific needs.

Be Cautious of Data Privacy

If using a cloud-based tool, there’s also a risk of sensitive information, such as user data or proprietary code, being exposed inadvertently. Before implementing, you’ll need to carefully consider whether using these tools aligns with your company’s security policies and QA best practices.

Where Can You Use AI in QA Processes?

According to Katalon’s 2025 State of Software Quality Report, 76% of QA professionals who responded to their survey reported using AI-powered tools in their software testing.² But how it’s used can vary widely based on the team, their products, and their aims. Below, we have just a few examples of how you can use artificial intelligence to better guarantee quality and high performance.

• Creating Test Data. By being able to create an array of test data sets that accurately simulate user behavior and possible edge cases, you won’t need to spend your time manually prepping data while ensuring comprehensive performance tests.
• Creating Test Cases and Scripts. Programs can also be used to generate test cases automatically, with the goal of eliminating the need for manual creation while increasing coverage.
• Test Optimization. The more quality data that goes through your model, the better it will be able to optimize its tests, evaluating results, changes, and patterns to increase its efficiency and precision.
• Bug Detection. When it comes to developing web and mobile applications, catching bugs early is key. And since AI tools are not only able to analyze much more data in a shorter period of time, but also to use vast amounts of historical data in its analyses, it can be used both to help detect bugs and to predict when and where future problems might appear.

How to Effectively Implement AI as a QA Engineer

Now that you know some of the ways you can use AI, there are several steps you’ll need to take along the way to fully integrating it into your testing strategies:

Step One: Scope Identification

First, you’ll need to define your objectives. Do you want to focus on monitoring certain performance issues, or maybe automating tasks? This will help you determine where AI can add value to your QA, as well as what features you should look for in a tool.

Step Two: AI Model Selection

Once you know what your needs are, you can choose models that fit those needs, from automating test scripts to data analysis and much more.

Step Three: Training

The next step (and a crucial one at that) is to ensure your model is properly trained. You’ll need to gather and label the right set of high-quality data in order to help it learn to recognize patterns, execute tests effectively, and make predictions accurately.

Step Four: Validation

As we mentioned earlier in this article, testing and evaluation are still necessary. So you’ll want to regularly use subsets of data to verify your models’ performance and reliability.

Step Five: Integration

Now, it’s time to deploy the trained models into your QA workflow in order to automate the development lifecycle from test creation to analysis and beyond.

Step Six: Continuous Improvement

However, the work doesn’t end with deployment. As the model learns and grows, and as outside factors like tech updates or shifts in user behavior occur, you’ll want to keep monitoring the processes to ensure they keep improving.

Where Can You Learn AI Automation?

Colleges or Universities

Many people may prefer to pursue a more traditional route to learning AI automation. In these cases, someone may opt for getting a bachelor’s or even master’s degree in computer science, information technology, software engineering, or other tech-related fields. These will provide you with a more holistic education across a variety of subjects over a longer period of time, as opposed to the more focused pathways we’ll look at below.

Bootcamps

On the other hand, some may want to invest less time and money in a degree. Luckily, there’s a wealth of options for non-traditional education—such as bootcamps. These are shorter-term (typically a few weeks to several months) and much less expensive than getting a four-year degree. Keep in mind, though, that as these programs usually aren’t accredited like colleges and universities, it’s important to do careful research when finding a program to be sure you’ve chosen a reputable school that matches your learning style, schedule, and career goals.

Self-Teaching

Still others who are just looking to get a foundation in AI for quality assurance—or who want to brush up on their existing skills—may opt for self-learning. This will cost less than a degree or a bootcamp, and you can choose your own pace and the subjects you want to study. However, it may be difficult to enter the job market without formal training. So if you’re looking to make a career switch from an entirely different field, you’ll also need to build up a robust portfolio that can showcase your abilities, and depending on prospective employers you may still need to round out your education with a degree or certificate.

AI Automation in QA Careers: Final Thoughts

As we’ve seen, there are myriad ways that QA professionals can use AI to optimize their work. In the ever-evolving world of tech, there’s no shortage of opportunities to build new skills and advance in a new role or in your current one. For example, Careerist offers several QA-focused programs, including their four-month AI automation bootcamp.

If you’re thinking this might be the right path for you, Career Karma is here to help! From in-depth resources and guides to help choosing the best training program for your budget, schedule, learning style, and goals, we offer support at every step of the way. Explore our Comparison Tool and start your journey today.

Sources

¹https://www.careerist.com/ai-automation

²https://katalon.com/reports/state-quality-2025

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