Manual vs. Automated Testing: Why AI Made QA a Bigger Job, Not a Smaller One

AI writes a lot of the code now. So the obvious guess is that quality assurance (QA) gets smaller, that we automate the testing too and move on. Underneath the manual-versus-automated debate sits a bigger question, your overall testing strategy and what is even worth testing.

The opposite happened.

When developers can produce code faster than ever, someone still has to prove that code actually does the right thing. That job didn’t shrink. It grew.

The bottleneck in software development moved from writing code to proving it works.

That’s the real story behind the old manual vs. automated testing debate, and almost nobody writing about it has caught up. If you’re a CTO or an engineering leader deciding how to staff and run QA in 2026, the question is no longer “manual or automated.” It’s how to use both when a single developer with the right tools can ship a week’s worth of code in an afternoon. If you are weighing where to do it, here is the case for choosing to hire software testers in the Philippines.

Manual vs. automated testing, the part that hasn’t changed

Start with the basics, because they still matter.

Manual testing is a person using the software the way a real user would, looking for what’s broken, confusing, or just wrong. Automated testing is code that checks other code: scripts that run the same checks the same way every time, fast and without a human in the seat.

Here’s the honest comparison most teams already know:

Aspect	Manual testing	Automated testing
Speed	Slow, one human at a time	Fast, thousands of checks in minutes
Best at	Exploratory testing, usability, judgment calls	Regression, repetitive checks, scale
Catches	Weird, unexpected, “this feels wrong” issues	Known things that should keep working
Cost shape	Low to start, expensive to repeat	Higher to build, cheap to run again
Fits CI/CD	Hard to fit into automated pipelines	Built for continuous integration and delivery (CI/CD)
Human judgment	High	None on its own

The classic answer is that you need both, and that’s still true. Automated testing handles the repetitive checks so your team isn’t re-running the same login flow by hand every release. Manual testing covers the things a script would never think to try. If you want the full primer on testing types and where each fits, we cover that in our guide to software testing, and our take on building a real QA function goes deeper on staffing it.

But “use both” is where every other article stops. It used to be a good enough answer. AI changed what the question even means.

AI didn’t shrink QA, it turned testing into the bottleneck

For most of my career, writing software was the slow part.

I started as a software engineer, became a chief technology officer (CTO), and co-founded VinSolutions, which sold for $147 million in 2011. Across all of that, the thing that took forever was always the building. Almost every practice we adopted, agile, DevOps, better tooling, existed to make engineers more productive so they could ship faster.

AI broke that pattern. The building got fast, but the proving didn’t.

When code volume goes up and the human attention on each line goes down, the risk doesn’t disappear. It just moves downstream to whoever is supposed to catch the problems. That’s QA. The real impact of AI on software development is that the hard part of the job moved from typing to judgment, and testing is where a lot of that judgment now lives.

I’ve lived this. I’ve worked on systems where the testing was harder than the building. Billing is the classic one. The code to charge a customer might be straightforward, but proving you bill every customer correctly, in every plan, with every proration and refund and edge case, can be a bigger job than writing the feature was. Now imagine AI helps you write that billing code in a fraction of the time. Great. You just arrived at the hard part faster.

I’m not the only one seeing this. On my Startup Hustle podcast, Nuwan Samarasekera, a former Google engineer who now runs the AI testing company TestChimp, put it plainly: “The bottleneck has transformed from the development side to testing.” Five or six years ago, he said, coding was the slow part. Now things ship so fast that making sure they actually work is what holds everyone up.

When the cost of writing code drops, the cost of being wrong about it goes up.

Automated testing got easier to build, which is exactly why it’s not enough

There’s a real upside here, and I don’t want to undersell it. Building automated tests has never been cheaper. Playwright and Cypress made end-to-end browser testing far more approachable than the old tools ever were, and AI assistants like GitHub Copilot, Cursor, and Claude can draft an automation script faster than a tester can type one from scratch.

That handles the syntax. It does not handle the strategy.

And there’s a trap hiding in how good these tools are. AI agents are built to make things work, which sounds great until you realize a test’s whole job is to notice when things don’t. Nuwan gave me a perfect example. Say a button used to take one click and now takes two because of a bug. A naive AI testing agent, told to get through the flow, will just click twice and move on, the test passes, and the bug ships. The thing you built the test to catch is the exact thing the agent quietly worked around.

This is why software testing has to be predictable. You don’t want a test suite that reinterprets your app differently every run, or “self-heals” around a real defect because adapting was easier than failing. The judgment about whether a change is a fix or a problem has to stay with a human.

Here’s the part that should stick with any engineering leader betting on AI to handle quality for them.

I’ve shipped products where the test suite ran green and the customer hit the bug anyway.

A green suite only proves that the things you thought to check are still working. Whether you checked the right things is a separate question, and no amount of passing tests answers it.

The new QA job is to think like a product person

So if running the tests isn’t the hard part, what is?

It’s deciding what to test, and why.

That takes someone who understands what the product is supposed to do, who it’s for, and which outcomes actually matter to the business. A tester who’s handed a feature with no sense of the user behind it will file the wrong bugs and miss the right ones. As I put it on the podcast, AI can write a thousand tests, but it can’t tell you which thousand matter, and it won’t decide what’s worth testing in the first place.

Good QA people have always needed some of this product instinct. What’s new is that you can’t get away without it anymore. When code ships this fast, a tester who only knows how to follow a script can’t keep up, and can’t tell which failures are real. The job moved up a level, toward understanding the why behind how the whole thing fits together.

This is the same shift I wrote about in Product Driven. The skills that decide whether a team wins as the mechanical parts of the job get automated come down to communication, curiosity, and courage. For a QA engineer, communication means writing a bug report a developer can actually act on. Curiosity is what I tell our team is the thing that keeps you safe: stay curious about how AI changes your job and how you can use it, and you’ll adapt. Courage is the nerve to hold a release in a room full of people who want to ship. None of that is going away. All of it got more valuable.

Where manual testing will always win

Now to the part I want to be clear about. There is a huge place for manual testing, and there always will be, because manual testing is the real judgment of product validation.

A developer can use AI to write a feature, use AI to write the tests for it, and watch everything pass. None of that tells you the product is actually good, only that the code does what someone thought to specify. A human still has to look at the thing and decide whether it works for a real person.

Manual testing is where you catch the stuff no script anticipates. I’ve gotten bug reports my whole career that start as “this doesn’t work with Chinese characters” on software that was never built for them. Fine, I don’t care, except then someone drops an emoji in the same field and it breaks too, because an emoji is also a Unicode character. That’s the kind of weird, real-world edge case a person finds by poking at the software like a curious user, not by running a planned test. Testers call it monkey testing, and it’s exactly the messy, unpredictable exploration that automation is bad at and humans are good at.

Manual testing is the last guardrail before your software reaches real users. That guardrail matters more now, not less, because the volume of code hitting it is higher than it’s ever been. Part of that judgment shows up in something as basic as how a tester writes a bug report: a good one reflects an understanding of the product, not just a screenshot of a failure, and in how a team decides which bugs to fix first when the list is long.

How we rebuilt QA at Full Scale for the AI era

We saw this shift coming, so we changed how we run QA at Full Scale.

We upskilled all of our manual QA people to do automated testing with modern tools like Playwright and Cypress. The goal wasn’t to turn testers into script-writers and call it a day. We wanted people who already had strong product judgment to also pick up the technical range to automate the repetitive work, so they could spend their attention on the parts that need a human. Every engineer we place through our offshore software development teams, QA included, is trained on the Product Driven approach plus the modern AI toolkit, so they can take a vague requirement, direct the tools, and judge the result.

That’s the combination that’s hard to find and hard to fake: a tester who can build a Playwright suite and tell you which user journeys are worth covering in the first place. It’s the same model that worked for Testery, where we staffed a dedicated team around their automated testing platform, and for engineering teams like AMC Theatres that needed to scale quality without scaling headcount carelessly.

The model holds up because the people stay. Our developer retention is 93%, our QA engineers are senior, and a dedicated offshore QA engineer runs about $35 an hour all in. If you want testers who own quality on a real product rather than just running someone else’s scripts, that’s what hiring dedicated QA engineers through a staff augmentation model should actually get you.

So which do you actually need, manual or automated testing?

Both, but stop thinking of it as a versus.

Automate the things that should keep working: regression checks, critical user journeys, the login and checkout and billing flows you’d be embarrassed to break. AI and tools like Playwright make that cheaper than it has ever been, so there’s no good excuse to ship a real product without a real automated suite running on every release.

Then put your human testers where judgment lives: exploratory testing, usability, the weird edge cases, and the call about whether a release is actually ready. More than three-quarters of developers are now using or planning to use AI tools, per the Stack Overflow Developer Survey, which means the flood of fast code isn’t slowing down. The teams that win with AI keep a human in charge of what gets tested and why, no matter how much of the suite is automated.

That’s the decision in front of you, and it was never really manual or automated. It’s who owns the judgment of what to test and why.