From faster rituals to deeper thinking: how AI is changing software testing

With agentic AI augmenting testers, I see testers starting to move up the value chain: I see them spending more time thinking about what should be tested and why, rather than just how. I see them reasoning more about systems, users, and risks, instead of pouring time into automating repetitive checks.

In short, with agentic AI, the effort is shifting away from the how toward the what and the why—the higher-value thinking work. Mapped onto a simple model, that shift looks something like this:

Below are a few of my observations on the biggest "what's" and "whys" I'm seeing in this shift—and why I'm encouraged and genuinely optimistic about it.

AI is reducing the cost of automation in testing

It's getting increasingly easier to automate the mechanical parts of testing—whether that means automating manual repetitive checks, generating test data, or finding patterns in test results. And that's the aspect I like most about AI in testing. Not because the machinery of automation gets cheaper, but because something more important happens: testers finally get the time and space to think.

They can step back, look at the whole system, and spend their cognitive energy where it truly counts: finding software problems that matter.

Scalable automation reveals the value of skilled testing

AI doesn't just reduce the cost of generating automation. It exposes the real value of professional human testing: testing that consistently finds rare, hidden, and subtle problems that matter. And as AI automates more of the repetitive and dreary work, testers are shifting upward from "How do we automate this?" to "What risks matter most to which stakeholders?".

The bottleneck isn't automation, it's thinking

And this shift—this reallocation of attention—is healthy, because the bottleneck has never been automation, it’s thinking. What limits how fast we can ship high-quality software consistently is the time required for deep exploration, experimentation, and investigation. That’s what surfaces the risks that matter.

Teams rarely fail because they can't write enough automation. They fail because they don't (or can't) ask the right questions soon enough.

• Do we understand the system?

• Do we understand the users?

• Do we understand which risks matter most to which stakeholders?

If we don't ask those questions, no amount of automation will save us.

Testing is an investigation, not a production line

Testing is not a factory process. It's an investigation into quality, risk, and information we don't yet have. The challenge in testing is framing risks, designing experiments, questioning assumptions, and telling a useful story about the quality of the software we are testing.

And we do all of that with one goal in mind: to enable other people (like developers and managers) to make better-informed decisions faster (whether that's fixing something or shipping it). That's the heart of testing. AI supports it, but it doesn't replace it.

AI is an amplifier, not a replacement

This is why I see AI as a capability amplifier, not a replacement for software testers. It can suggest relevant tests, draft automation, generate data, and summarize logs. AI produces useful artifacts and insights, but people produce judgment.

AI raises the ceiling of what testers can do; it doesn't eliminate the need for human sense-making. Again, AI is an amplifier of everything; it increases ingenuity...but also risks. When AI becomes more autonomous, it becomes easier to mistake activity for insight. If we want to get the most out of this technology, we also need to understand the conditions under which it can mislead us. Not to be cautious, but to help us steer AI in the right direction.

When quantity masquerades as confidence

One of the first ways AI can misguide us is by creating an illusion of test coverage. When test ideas become easy to generate, it's tempting to equate test volume with test quality. But professionals know that a mountain of AI-generated checks can still miss the real risk: coverage numbers can look impressive, dashboards can glow green, yet critical questions may remain unanswered.

Sure, we can ground AI in our context. But most of our knowledge is implicit—the kind that sits in people's heads, not in documents. AI can't magically access that. So, don't let AI dictate your testing. Treat every AI-generated test idea as a prompt for inquiry: "What does this miss? What does this assume? What does this not know?". Guidance, not obedience, is the tester's job.

Faster automation can still be the wrong automation

Scalable automation encourages the automation of everything, including things that don't matter. Professional testers know this well. As Dorothy Graham says: "If you automate chaos, all you get is faster chaos." Professional testers don’t create automated checks for their own sake. They don’t measure value by volume. They know that a small set of risk-revealing checks is far more valuable than a large set of automated checks that tells you little or nothing about real risk. They ask: "What information does this check give us?" or "Does this tell us something meaningful about risk?". They focus on automating what matters, not just what's easy.

The hidden risk: AI can deskill software testers

Another subtle trap is the quiet erosion of skill. Professional testers understand that AI can de-skill them if they stop practicing the hard part. They know if they mostly prompt AI to spit out test ideas, they may not build the muscles for modeling unfamiliar systems, recognizing ambiguity and bias in requirements, or designing experiments under uncertainty.

And when those muscles fade, so does the ability to judge AI's suggestions. The twist is that AI frees professional testers for higher-value work, while it can also mislead amateur testers—less experienced testers or those testing from other roles (myself included)—into trusting AI's output too literally. Without solid testing skills, it's easy to mistake fluent suggestions for sound ones.

Overall, AI amplifies what testers bring to the table. It multiplies strengths and gaps. Mixed teams of professional and amateur testers that acknowledge these asymmetries will thrive.

The choice: better thinking or empty rituals?

This brings us to the decision testing teams must make. AI creates a new choice point: do we use the time it frees up to invest in better thinking (stronger modeling, sharper questions, better risk analysis) or do we spend it on more rituals?

By ritual I mean busywork disguised as testing: generating piles of automated checks because it's easy, following AI-generated test plans rigidly even when new risks emerge, or filling dashboards with metrics that don't reflect real quality. The future of testing depends on this choice.

Closing the loop: what makes me optimistic

So let me return to where I started: AI is changing testing, not by replacing testers but by revealing what they do. And when I look at the teams I work with, I see something encouraging: a few years back, in our backlog refinement meetings, I'd see testers' first question was always "How do we automate this?" instead of "What should we test and why?".

But now, I'm noticing something different. As it's gotten easier to automate, the conversation has shifted. More testers are asking those what and why questions because they have the breathing room to do it.

More conversations are about risk rather than scripts. More energy is going into making sense of the work than making checks. More testers are choosing deeper thinking over empty rituals.

Yes, AI has its risks (illusion of coverage, wrong automation, deskilling). But I see software testers responding thoughtfully. They are using AI to deepen their judgment, not to avoid it.

The net effect? Testing is getting better, not because AI is magical, but because it frees testers to do more of the work that matters: work that doesn't just produce more automation but surfaces more truth about the quality of our software; work that doesn't just go faster but goes deeper.

And that makes me optimistic about the future of our craft.