In both manufacturing and software development, unplanned downtime is a critical issue that can disrupt operations, cause significant financial losses, and reduce productivity. While in manufacturing, downtime often results from equipment failures, in the software world, it can be caused by system crashes, defects, or unexpected technical issues that halt production lines or customer-facing applications.

In a rapidly changing digital world, it's more important than ever that quality assurance (QA) not only keeps up with development but also leads the way. As new technologies and innovations reshape the landscape for software development and testing, we ensure that we use the best tools and methods to deliver the highest quality to our customers. Gartner’s latest report on the 2025 strategic technology trends highlights several areas that will directly impact the future of QA, and here’s how we address these challenges and opportunities.

In the growing landscape of the financial industry, quality assurance (QA) professionals face many challenges in ensuring financial software's robustness, security, and compliance. This blog post dives into the challenges faced by QA teams within the financial sector and provides insights into their perspectives and strategies to address them effectively. With strict regulations, rapid technological advancements, and the need for seamless user experiences, QA teams must employ innovative and comprehensive approaches to uphold the integrity of financial systems.

As someone who transitioned from software development and data science into the world of quality assurance (QA), my first weeks at System Verification were a whirlwind of discovery. While my expertise in machine learning (ML) and MLOps was warmly welcomed, I quickly realized I had to learn more about QA and testing. It was a steep learning curve, but the eagerness of my colleagues for technical insights gave me a clear mission: to bridge the gap between our strong QA foundation and the rapidly evolving AI landscape.

In the ideal world of software development, releases should be moments of celebration. It’s the time when our hard work finally becomes a reality, delivered to the users. Yet, instead of joyous milestones, releases often become our greatest source of stress. We plan ceremonies around these releases, but the anxiety often overshadows any sense of accomplishment.  

In the thrilling race to utilize the power of generative AI, companies around the world are jumping on the bandwagon faster than you can say “artificial intelligence.” But, as the saying goes, "Not all that glitters is gold"—and not all AI projects are destined for glory. In fact, according to a recent article from Computer Sweden, nearly a third of these shiny new AI projects might end up in the digital dumpster. Ouch.

In our last article, we discussed how important it is to follow the most beneficial and crucial recommendations while developing or maintaining test automation solutions. The benefits are large and well worth the time and effort required to acquire them.

In this article, we will continue with recommendations. 

As previously said, there are numerous suggestions and practices that should be followed when developing AT solutions.

Most QA engineers today are already following the standard of implementing test automation solutions on their projects. But are we assessing our AT solution to ensure that we are getting the most out of it? Do we use AT recommendations and best practices, and how crucial is that? Is it even possible to apply best practices to every project? 

Let's find out together. 

Discover the journey to the ISTQB AI certification, from motivation to preparation tips. Gain insights on artificial intelligence (AI) testing, skills acquired, and advice for aspiring candidates.

In the rapidly developing world of technology, few terms have gained as much visibility as those related to Artificial Intelligence (AI). From machine learning to neural networks, these buzzwords dominate tech conversations, often leaving many feeling overwhelmed and confused. However, understanding these concepts is crucial, especially in domains like Software Quality Assurance (QA), where AI holds massive potential to revolutionize processes and outcomes.