It has never been easier or cheaper to generate a mountain of automation code. With AI, you can spin up hundreds of tests in minutes. But here is the catch. While creating code has become incredibly cheap, owning poorly planned code has become massively expensive.
When automation is implemented without proper thought, clear standards, or the right scope, it backfires completely. You end up with a brittle, bloated test suite that creates more noise than value. Before long, your team is spending more time fixing broken tests than shipping software.
We lose our way the moment we forget the proper order of solving engineering problems. You figure out what you want to solve first. Then you figure out why. The how comes dead last. The tooling, the frameworks, the AI models, the specific test types, these are the final parts of the thought process, not the starting point.
AI is a lot like the test automation trends that came before it. It is just a way to solve a specific problem. The QA landscape is massive and there is no shortage of challenges to tackle, but our job as QA professionals is to see how the whole machine works together.
Test automation and AI are tools in your toolbelt. They are powerful and valuable, but they are tools nonetheless. Manual testing, exploratory work, and performance testing are tools in that same kit. None of them is a silver bullet. Some are simply better suited to certain tasks than others, and our real value lies in knowing exactly what to use and when.
In my own experience, trying to understand and master different testing disciplines makes you better at all of them.
Learning how one tool works shifts the way you see problems across the board. It raises the standards you set, the patterns you reach for, and the way you approach a broken system. When you stop treating AI or automation as the entire strategy and start treating them as parts of a single, well-oiled machine, that is when you stop wasting money and start delivering real value.