How Artificial Intelligence is transforming testing?
ISTQB Platinum Partnership, GenAI, and the future of QA
Interview with Piotr Wierski,
Head of Quality Assurance, Test & Embedded Practice at ALTEN Polska.
ISTQB Certification and the significance of partnership for ALTEN Polska
To begin with — what does achieving ISTQB Platinum Partnership status mean for ALTEN Polska, and why is this such an important distinction?
For ALTEN Polska, attaining ISTQB Platinum Partnership is a significant milestone in our journey toward becoming a trusted provider of solutions. It also marks our entry into an elite group alongside our colleagues in Italy, France, and Spain. This partnership is a source of pride for us, as it validates the meaningful impact of our work. Over the years, while leading a team of testers, I was often asked whether investing time and effort into obtaining ISTQB certification was worthwhile. My answer was always the same: if a tester understands test case design techniques, can create test documentation, and uses standardized terminology, they integrate more quickly into teams, better understand client needs, and ultimately contribute to higher project quality.
What was the path to achieving this partnership, and what criteria had to be met to join the select group of companies worldwide with this status?
Typically, obtaining Platinum Partnership status involves a long journey—many individuals must earn both foundational and advanced level certifications. Fortunately, for ALTEN Polska, the certification process went exceptionally smoothly. This is because we treat testing seriously—as a key component of delivering high-quality products, not merely an add-on to the IT process. It wasn’t just about numbers; what truly matters is the daily work, the exchange of experiences, training sessions, conversations with clients, and the ability to navigate unforeseen challenges.
Developing software testing competencies at ALTEN Polska
Training and ISTQB certification are key elements in the professional development of testers. How does ALTEN Polska support employees in acquiring and updating their knowledge in this area?
The foundation of our approach lies in recognizing the value of each individual employee. We invest in practical training, workshops, and ongoing conversations about how to smoothly transition into new roles or projects. Companies that prioritize knowledge-sharing and practical experience exchange are highly valued by clients. ISTQB certification opens the door to more complex and demanding projects, including the testing of safety-critical products.
Do you see tangible business benefits from investing in the certification of testing teams? If so, what are they?
As mentioned earlier, one of the most evident benefits is the standardization of language through the use of clear and consistent definitions. For instance, when we discuss the need to create a test plan, both parties understand exactly what information it should contain. Another—perhaps the most important—advantage is the economic aspect: delivering insights into product quality at the lowest possible cost. While it may be feasible to intuitively create test conditions for simple products, doing so for complex systems with numerous logical conditions becomes extremely challenging. In such cases, the difference is clearly noticeable.
New ISTQB Syllabus: testing systems with GenAI and the role of collaboration with ALTEN Group
The latest ISTQB syllabus focuses on testing with generative artificial intelligence. What prompted the creation of such a document?
Generative AI is currently a widely discussed topic, with its role in IT growing rapidly. From my experience, today’s testers can no longer rely solely on traditional, proven methods, as the technology landscape is evolving at an unprecedented pace. As is often the case with emerging trends, there is a lot of noise, making it difficult to distinguish between marketing hype and actual capabilities and benefits. That’s precisely why ISTQB took on the task of preparing materials and training programs that objectively present the potential of GenAI and the ways it can support the testing process. Naturally, the industry is changing dynamically, so this document serves as a foundation for further exploration and understanding of the technology.
ALTEN Group was involved in the development of the syllabus. What did that process look like, and how were we able to contribute our expertise?
As an expert partner, ALTEN Group actively participated in the development of the new syllabus, sharing insights gained from AI implementations across various industries. A key contribution was our practical knowledge of tools—their applications as well as their limitations. Within ALTEN Group’s 60,000 engineers, we have specialists recognized by the global testing community. Our colleagues from Italy dedicated their time to share their expertise and experience with the testing community. Together with a select group of experts, they created a syllabus that became the basis for accredited training materials and their delivery. The document underwent ISTQB’s standard review and approval process, and upon publication, it became publicly available.
Applying GenAI in Software Testing: Benefits and Challenges
What key areas and challenges are covered in the syllabus related to testing GenAI-based systems?
The ISTQB syllabus on testing GenAI-based systems addresses a real need for standardizing approaches that help manage the unpredictability of such technologies. It focuses on areas such as evaluating the quality of generated content, validating input and output data, assessing risks related to model hallucinations, and ethical considerations—including algorithmic bias and regulatory compliance. This comprehensive approach is designed to support testers working with technologies that do not always behave in a deterministic manner.
Does the use of generative AI in testing already provide tangible support to testing teams, or is it more of a future-oriented topic?
Yes—it already supports teams, particularly in the most repetitive and foundational tasks, such as summarizing texts, generating test cases, or analyzing large datasets. Nevertheless, human judgment remains crucial in identifying when AI hallucinates, distorts results, or provides only partial solutions—which happens quite frequently.
What are the biggest technological and ethical challenges in testing GenAI-based solutions?
This is a topic worthy of a dedicated discussion, but I’ll outline the key issues. One of the main challenges is obtaining reliable test results for system behavior. It may sound simple, but when we cannot understand the “black box” of an AI model—how it arrives at its decisions—it becomes a fundamental problem. Often, this requires a new, statistical approach and the ability to interpret results that may change dynamically.
Ethical concerns are equally critical: how to avoid errors stemming from data bias, how to protect user privacy, and who should be held accountable for decisions made by the system. Confidentiality and intellectual property security also play a major role, as they often represent a company’s most valuable assets. These are the challenges the industry must address in the near future.
The future of Software Testing: AI, ISTQB, and the evolving role of testers
Finally — in your view, what direction will software testing take over the next 3–5 years, considering both artificial intelligence and ISTQB standards?
The development of AI-based systems is currently progressing at an unprecedented pace. Beyond the sheer amount of time required to stay up to date, new breakthrough approaches and capabilities are emerging almost weekly.
If I were to outline a general vision, I’d say that in the coming years, testing will become increasingly automated and autonomous, driven by AI and predictive tools. ISTQB standards will continue to evolve—not only to keep up with technological advancements but also to provide ethical and quality frameworks.
Testers will need to combine technical expertise with a strong understanding of business context, often participating in multiple projects simultaneously.
Eventually, they may become supervisors of autonomous AI systems—“artificial workers”—and it will be their responsibility to validate and endorse the outcomes of these systems. That is, of course, assuming we’ll have enough electricity to power it all!
