In the early days, record and playback tools like QTP felt like magic, just hit record, click around, and an automated test script was ready. These tools worked for simple cases, but as test suites grew, they became hard to maintain. Problems like flaky tests, broken scripts, messy Excel files, and endless maintenance were common.
Teams who understood coding and good design patterns could use these tools much more effectively. The real issue wasn’t the tools themselves, but how people used them. If you just recorded tests without planning or structure, things would fall apart.
I saw a project with hundreds of QTP tests succeed only because the team built a strong framework and knew what they were doing.
Now, we’re seeing the same thing with AI. Some teams complain that AI can’t do basic math or that the output isn’t satisfactory, without realizing they aren’t providing proper prompts and context. The best teams use AI as a tool within a solid framework, for things like fixing broken locators, picking smarter tests, and speeding up triage.
In the end, tools don’t make testing easy, good engineering does. Teams who learn to use AI the right way will move faster and more reliably. Those who don’t will repeat the same mistakes as before.
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