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The right AI tools make all the difference in QA testing AI has officially entered every corner of software testifng. The hard part now is figuring out which tools and features actually save time, speed releases, and (most importantly) improve quality outcomes. According to a recent survey from Test Guild, 72.8% of testers are prioritizing AI-powered QA for 2026. This includes tools that handle test planning, test management, test writing, and even analyzing test results. Contents Toggle.

AI-assisted software development makes building new features to help our customers an exercise in speed. Rainforest has a deep culture of experimentation and iteration, and we’re actively exploring how AI can help us work smarter. At our core, we aren’t just building an AI test generation tool; we are constantly experimenting with how AI can make our own development cycles leaner, faster, and more intuitive. That includes experimenting with AI-assisted software development.

Building a web application crawler came with plenty of challenges—here’s what we learned. Recently, we built a web application crawler from scratch—which had some scratching their heads, asking why we’d undertake such a thing. Here’s our answer to that, plus some interesting technical challenges we ran into and how we tackled them.

AI fosters speed. Transparency fosters confidence. AI for QA testing is suddenly everywhere. Every tool claims it’s “AI-powered.” Every demo promises smarter test generation, faster maintenance, and fewer bugs. Plus, with AI accelerating the pace at which developers write and ship code, QA leaders are under growing pressure to keep up. It makes sense that teams are looking for AI for QA testing. But here’s the uncomfortable truth: AI in QA only works if you can trust it.