Systems | Development | Analytics | API | Testing

Our goal with the Tricentis blog is to distill insights that help QA professionals navigate the massive, AI-driven transformation happening across the software delivery landscape. To that end, I reached out to experts across Tricentis, from product and services to marketing and strategy, to hear what they’re really thinking about AI in QA right now. This group brings decades of experience building testing products, guiding enterprise transformations, and shaping how organizations adopt AI.

Many organizations run data quality programs that, on the surface, serve teams well enough. They validate data, flag missing fields, remove duplicates, and reconcile reports. Most of the time, that feels secure enough. When teams collaborate and compare datasets, discrepancies often appear but are dismissed as negligible. Fixing them is built into workflows and job descriptions, even if it takes hours or days. This approach is starting to show its age.

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” That’s Amara’s Law, a principle coined by futurist Roy Amara. It explains how emerging technologies, like the early internet, are often overhyped at first, followed by a shift toward recognizing their value and integrating them over time. This thinking is a lot like what we’re seeing today with agentic AI.

AI is fundamentally reshaping software quality, and the organizations leading this shift aren’t waiting to adapt. In October 2025, we brought together over 1,000 quality engineering leaders, practitioners, and innovators for Transform, our annual conference exploring what’s next in software delivery.