Scaling a startup brings real concerns with it – business needs speed to grow, while engineering needs process and automation to sustain that growth. In most cases, teams end up choosing one over the other: either moving fast and building up quality debt, or slowing down with heavy processes that don’t scale.

In fast-scaling, AI-driven startups, this becomes even more pronounced. Rapid releases, changing architectures, and growing teams quickly make traditional QA approaches ineffective. This talk is about what it actually takes to build and scale a quality function from scratch in that kind of environment, moving away from QA as a separate phase to quality as a shared responsibility.

Based on my experience of setting this up in a startup, I’ll talk through the challenges of getting the basics right early on, and how those practices need to evolve as the system and teams grow – without becoming a bottleneck.

AI is definitely changing how we build and test software – faster feedback, better automation, smarter tooling. But the bigger shift I’ve seen is cultural: moving from siloed QA teams to shared ownership, where quality is continuous and part of everyday engineering decisions.

The focus of the session is practical, how to balance speed with discipline, where process actually helps (and where it doesn’t), and how to use automation and AI in a way that supports scale instead of getting in the way.