Can AI Testing Think Like a Human?

What’s Still Wrong With Testing?

Manual testing is tough. It eats up time and focus. You repeat the same actions, again and again, until it starts feeling like a blur. Eventually, you stop noticing the small stuff — the bugs that slip by because your eyes are tired or you’re stuck in a pattern.

Regression testing? Same story. There’s never enough time, and releases come fast. You might be lucky to get a full day — sometimes just a few hours — to run through everything. That pressure leads to missed cases, surface-level checks, and a whole lot of guessing.

Automation was supposed to fix this. And sure, it helps.
But the truth is, automation scripts break. A small change in the UI or a shift in flow means you’re suddenly maintaining broken tests. And let’s be honest — those scripts don’t think. They follow exact rules. Which brings us back to square one.

That’s why AI testing caught my attention again. Could it go beyond scripts and really start acting like a human tester?

The Big Question

A few weeks ago, I started asking:

Can AI testing go further than just scripted actions?

Could it understand why we test the way we do? Could it spot bugs not because it was told to, but because it noticed something was off? Could it act on vague input like:

“Find the profile with a fish in the photo.”

That’s not something most automation frameworks would know how to handle. But a human tester gets it. They use clues. They explore. They adjust.

If AI testing can start doing that — even a little — we’re in a different game.

What This Could Mean for QA

Now, imagine an AI agent that can:

• Understand everyday language — not just test code.
• Adapt to unexpected changes in layout or flow.
• Try out new paths, based on usage patterns or past bugs.
• Run smart exploratory scenarios without you needing to spell everything out.
• And yes — output real test scripts when needed.

That’s not just AI testing — that’s smart testing.

Instead of replacing testers, it supports them. It takes over the repetitive checks and gives testers room to focus on creative problem-solving, user empathy, and critical edge cases.

A Real-Life Test

A few weeks ago, we started playing around with a tool that caught our attention. I won’t name it here, but it’s built by a small team in Europe — and they’re doing something bold.

This tool goes beyond the usual script runners. It takes natural language instructions — things like “find the profile with a fish in the photo” or “check if the GDPR section mentions user rights” — and actually makes sense of them.

We tested it against real-world flows, including vague or incomplete steps. And the tool got it. It clicked buttons, searched fields, ran flows, and even gave us a breakdown of what it did. That’s AI testing stepping up.

What Makes It Different

• Here’s what stood out to me:
• It accepts test scenarios in natural language, like a conversation.
• It adapts to layout changes without breaking.
• It runs exploratory tests using patterns from past usage or logs.
• It can even suggest edge cases or paths you might not have thought about.
• And it outputs real code — like Selenium or Playwright scripts — that can be plugged into your CI/CD pipeline.

This isn’t about replacing testers. It’s about removing the repetitive stuff, so testers can focus on what humans do best: think, analyze, and ask “what if?”

That’s why I think AI testing has real potential here.

Where This Fits

In my mind, this tool works great for teams that:

• Don’t have huge QA budgets.
• Rely heavily on manual testing.
• Have BAs writing the test cases directly.
• Need fast feedback before a release.
• Or want help bridging the gap between business scenarios and working code.

It’s also perfect for juniors or mid-level testers — folks who know what to test, but not how to automate everything. This tool fills that space.

What’s Next for AI Testing?

We’re still early. The tool’s in alpha. But I already see the value.

AI testing won’t do everything. It won’t replace deep thinking or complex validations. But it will take care of the time-wasters. It’ll run through the basics, suggest overlooked paths, and even help generate clean test code — all without needing a full setup.

That’s a win. Not just for QA, but for the whole team.

If we can let AI handle the routine, we free ourselves up for the real stuff: product thinking, edge cases, user empathy, and building quality in from the start.

And that’s the kind of testing I want to keep doing.