Who is Rik Marselis ?
Principal Quality Consultant
Rik Marselis is principal quality consultant at Sogeti in the Netherlands. He is a highly regarded presenter, trainer, author, consultant and coach who supported many organizations and people in improving their quality engineering & testing practice.
Rik is an accredited trainer for TMAP and ISTQB certification training courses, but also he has created and delivered many bespoke workshops and training courses. He is the chairman of the TMAP special interest group.
Rik is a fellow of Sogeti’s R&D network SogetiLabs. These R&D activities result in presentations, books, white-papers, articles, podcasts and blogs. Also he is a co-author of the TMAP books “Amplified Quality Engineering” (2025) and “Quality for DevOps teams” (2020), and he is a contributor to the www.TMAP.net body of knowledge for quality engineering & testing.
In 2022 Rik received the ISTQB Software Testing Excellence Award. A year later he received the EuroSTAR 2023 Testing Excellence Award.
What will Rik Marselis be discussing?
Amplified Quality Engineering (with and for GenAI)
With the arrival of Generative AI (GenAI), the IT landscape shifted dramatically. Clients, colleagues, and partners started asking questions like: Can we use GenAI to support our quality engineering activities? Should we? And more importantly: How do we deliver the right level of quality in systems that use GenAI?
To answer this we wrote and published the TMAP book “Amplified Quality Engineering”. In this book, we explore how GenAI impacts quality engineering — not just as a tool to amplify development and testing, but also as a source of new risks and responsibilities.
We share what we’ve learned, what we’re still learning, and what we believe is needed to use GenAI both effectively and responsibly.
Precisely because of the rise of GenAI and Agentic AI, the field of Quality Engineering and Testing will soon rise to a new level of importance. The complexity and speed at which AI systems evolve require a new approach to quality engineering. The complicated nature of GenAI models makes them function as black boxes. It is becoming difficult to understand why content was created in a certain way. This makes debugging and root cause analysis much harder to accomplish without the proper tools and expertise. It is not sufficient to rely solely on the output of AI; we must thoroughly test and validate the processes and algorithms behind that output. So we need to have a broad view and take joint responsibility to achieve the right quality at the right moment.
Our main message is:
Don’t use GenAI…
Unless, you know what you’re doing…
And you are willing and able to take full responsibility for the consequences, even if the results prove to be incorrect!!
Amplified Quality Engineering!!
