Say these two phrases out loud: “using AI to test software,” and “testing software that uses AI.” They sound like the same sentence with the words shuffled. In practice, they describe two completely different jobs, and a surprising number of teams are doing the second while thinking they signed up for the first. The gap…
Imagine a car that can drive ten times faster than any other vehicle. That’d be amazing, right? But there’s a catch: when stopped at an intersection, the car has to wait ten times longer for the light to change. Would you drive that car? This is a similar predicament as the one software development and testing…
AI can write a test before you finish your coffee or tea. It can inspect a user story, suggest scenarios, generate Playwright code, and explain why a test failed far quicker than humans can. That’s impressive, especially if your morning routine already includes arguing with a flaky login test. The uncomfortable part is that an…
When most enterprise teams evaluate a do-it-yourself (DIY) Playwright build, they tend to focus on the foundational, table stakes costs that are easy to see. These are items like engineering hours, cloud infrastructure, and open-source tooling; they’re the costs that end up in a spreadsheet, to be reviewed and approved. The numbers that rarely make it into…
Enterprise QA has always run on two major assumptions: tests can be trusted, and tested software is what ultimately gets shipped. For the past few years, AI has stress-tested those assumptions by generating software faster than many organizations can validate with their existing QA infrastructure. Faced with becoming a bottleneck against releases, many testing teams…
