Hemraj Bedassee

Over the last year, at Testlio, we have upskilled more than 600 testers in our global community to test AI-powered applications.

Who would have imagined, five years ago, that staying relevant in QA in 2026 might involve… poetry? Not writing it for fun, but kind of weaponizing it.

Over the past few weeks, an unusual experiment has been unfolding quietly and then very publicly.

We keep hearing the phrase AI testing used in very different ways, often in the same meeting.

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.

Over the past few years, model providers have invested heavily in “guardrails”: safety layers around large language models that detect risky content, block some harmful queries, and make systems harder to jailbreak.

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 systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.