Test scripts appearing in seconds… sounds almost magical, right?
At first glance, it felt like we had stepped into the future of testing. Scripts were generated instantly, and the promise of full automation was exhilarating—for both me and my team.

But as our AI-assisted Playwright journey unfolded, reality began to show its limits. AI helpers like GitHub Copilot, Playwright Codegen, and eventually MCP were fantastic assistants, but they weren’t great testers. Without human intelligence and experience, AI—no matter how fast—remained somehow incomplete.

The Story of Speed vs. Accuracy

One of our first “aha” moments came when we asked MCP to generate a login test.

Prompt given—done.

Code appeared instantly—done.

Assertions included—done.

Impressive ? Think again after you read the second part.

The moment we clicked “Run,”  problems started piling up like dominoes—more than half of the locators were broken. Worse—when we dug deeper, we realized that AI had chosen validations that didn’t really reflect user value.

That’s when it clicked: AI is brilliant at speed—and no need for us humans to take it personally—but it struggles with accuracy and intent. Humans bring domain knowledge. We know why a login flow matters, which edge cases are critical, and what a test truly needs to validate. For AI, that kind of understanding is simply not possible (Hey, don’t take me the wrong way—I really hoped it could be perfect. I’m from the generation that still thinks Google is a futuristic miracle.)

Lessons Learned from Blending AI and Playwright

Working with AI and Playwright has been a wild ride so far—but we’ve learned a few lessons along the way:

1. AI accelerates, but humans validate.
Sure, generating tests is lightning fast—but making sure they actually do something meaningful? That’s still all of us. Think of AI as a race car: it can go 200 mph, but only you can steer it safely through the corners.

2. Prompts are everything.
A vague prompt = vague results. Clear, detailed prompts = AI output you can actually use. Treat your prompts like treasure maps: the better the map, the easier it is to find the gold.

3. AI is a booster, not a replacement.
It’s perfect for cutting boilerplate, brainstorming test scenarios, or taking on repetitive flows—but don’t expect it to replace critical thinking. AI is your helpful sidekick, not the hero of the story.

4. Business logic is human territory.
AI doesn’t know your domain—but it can help you explore forgotten edge cases, spark new ideas, and challenge assumptions you didn’t even know you had.It’s like having a curious intern who won’t stop asking questions… (except it doesn’t need coffee breaks.)

The Future of Test Automation

The question of ‘Will AI replace QA engineers?’ is no longer the one we should be wasting time on. The real question is: “How can we use AI to deliver higher-quality software more efficiently?‘’

AI won’t replace QA engineers.

But QA engineers who learn to leverage AI effectively will replace those who don’t.

This is the real shift happening in QA right now—a move from fear of replacement to embracing AI as a powerful tool for smarter, faster, and more efficient testing.

Bringing It All Together: Human + AI

At Nenya, this mindset shapes how we deliver Testing as a Service. We use AI to speed up creation, but we lean on human expertise to connect tests to pipelines, reporting, and real business needs. And tools like Pulse, our API testing application, reflect the same philosophy—AI can help, but humans decide what matters.

The future of AI-powered Playwright testing isn’t about choosing between automation and people. It’s about blending the two—letting AI handle the scaffolding so testers can focus on what they do best: asking the right questions in the right environments.

And to the developers reading this :breaking the program to give you extra bugs is definitely never the answer!

Author: Simten Efeoglu