Perspectives

Most product teams today are very good at one thing: testing what happens when a user types a prompt.

For a long time, we spoke about “AI agents” like they were a future concept, something that might eventually book flights, run workflows, or make payments on our behalf.

AI testing careers are shifting in ways that most people in QA are not fully prepared for, and the changes are creating opportunities that did not exist even a few years ago.

AI doesn’t just learn from data, it learns from us, and we are far from perfect. When it scrapes the internet for knowledge, it also absorbs our biases, blind spots, and noise, shaping how it interprets the world.

For years, QA practices were designed for predictable, rules-based software. AI has upended that reality by introducing risks that traditional methods cannot fully address.

We put two of the most talked-about models head-to-head in a real-world RAG scenario, and the results might surprise you.

AI is no longer just a technical feature, it is a business-critical system that shapes conversations, decisions, and customer experiences.

When you add AI to your product, the hardest part is not building the feature but making sure it works safely, reliably, and as intended in the real world.

Delivering analytics quality at a global scale is never easy. One broken event or missed signal can derail product launches, fuel bad decisions, and shatter customer trust overnight.