{"id":139010,"date":"2026-02-19T13:15:05","date_gmt":"2026-02-19T21:15:05","guid":{"rendered":"https:\/\/careerkarma.com\/blog\/?p=139010"},"modified":"2026-02-19T13:15:16","modified_gmt":"2026-02-19T21:15:16","slug":"how-ai-automation-is-changing-qa-careers","status":"publish","type":"post","link":"https:\/\/careerkarma.com\/blog\/how-ai-automation-is-changing-qa-careers\/","title":{"rendered":"How AI Automation is Changing QA Careers"},"content":{"rendered":"\n

Sponsored Post: Careerist<\/em><\/p>\n\n\n\n

With the exponential explosion of artificial intelligence (AI) throughout all industries in recent years, it\u2019s no wonder it\u2019s 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?<\/p>\n\n\n\n

In this article, we\u2019ll take a look at the effect of AI automation in QA careers, including how to make sure you\u2019re getting the most out of the process\u2014from choosing the right tools, to learning how to build your skills.<\/p>\n\n\n\n

Growth of Artificial Intelligence in QA Roles<\/strong><\/h2>\n\n\n\n

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<\/a> 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<\/em> by 2030.1<\/sup> 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\u2019s making the most of them.<\/p>\n\n\n\n

Why Use AI-Powered Testing Tools?<\/strong><\/h2>\n\n\n\n

Saves Time and Effort<\/strong><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Reduces Cost<\/strong><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Increases Scalability<\/strong><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Considerations About Using AI Testing Tools in Your QA Job<\/strong><\/h2>\n\n\n\n

Monitor All Results<\/strong><\/h3>\n\n\n\n

Just because an AI tool produces an output, that doesn\u2019t necessarily mean it\u2019s accurate or even relevant, so critical thinking is still necessary. If you\u2019re using an AI, be sure to do a thorough code review and consistently double-check results to be sure it’s accurate and useful.<\/p>\n\n\n\n

Choose the Right Tools for the Tasks<\/strong><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Be Cautious of Data Privacy<\/strong><\/h3>\n\n\n\n

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.<\/p>\n\n\n\n

Where Can You Use AI in QA Processes?<\/strong><\/h2>\n\n\n\n

According to Katalon’s 2025 State of Software Quality Report, 76%<\/a> of QA professionals who responded to their survey reported using AI-powered tools in their software testing.2<\/sup> 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.<\/p>\n\n\n\n