Will AI in Software Testing replace Human Intelligence?

Discussions
,
1

Artificial Intelligence isn’t on its way into software testing - it’s already here. It’s writing, analyzing, accelerating. It’s disrupting long-held practices and reshaping how we define quality.

And while AI is rapidly transforming how we develop software, one thing becomes clear:
The more machines generate, the more humans must evaluate.

Why?

If code is written, tested, and deployed by automated systems, then quality assurance becomes the last line of accountability. Not just for functionality but for everything AI doesn’t see: Copyright violations. Security flaws. Discrimination. Misinformation. Compliance breaches.

This isn’t an argument against AI. It’s a call to use it wisely. To pair it with structure, judgment, and real responsibility. Because true quality won’t come from automation alone. It will come from those who guide it.

Let’s begin where machines still struggle most: manual testing. The part where people think, feel, and respond to what they see - not just what they’re told to verify. AI can help. It can highlight areas of risk based on commit history, detect anomalies, and even suggest what might be worth checking next. But human-guided testing - whether exploratory or structured - is more than just execution. It involves interpretation, prioritization, and conscious decision-making. Manual testing isn’t random. It follows logic, heuristics, user expectations, business context. And it constantly adapts to change in a way no model fully anticipates. Where AI looks for patterns, people often step back to ask if those patterns even make sense.

If AI can follow patterns - who’s making sure we break them?

That kind of thinking, structured yet skeptical, systematic yet situational, is still deeply human.

AI has also changed the way we automate. What once required careful selection, choosing which tests were worth automating, where effort paid off, and where human eyes were irreplaceable, has turned into a flood of automation-at-scale. Now, we can automate almost everything. And too often, we do - without asking whether we should. AI helps generate thousands of test cases but with that power comes risk: test suites that are bloated, noisy, and disconnected from real business value.

Are we building resilient, meaningful safeguards or just chasing metrics because a dashboard says we should?

Real quality doesn’t come from executing more tests. It comes from executing the right ones — and understanding why. Modern AI models don’t just react - they reason. They analyze, infer, explain. They offer increasingly contextual recommendations but they don’t care. They don’t prioritize with empathy or challenge business goals.

Let AI scale what should be fast.
Let people own what must be right.

Because quality isn’t just about detecting defects. It’s about understanding them. It’s about building confidence, not just coverage.

So, embrace AI but let it not replace yourself.
Pair it with the right people and the right platform to manage it all.

What´s your opinion on that?

2 Likes
2

Your current testing model is a significant part of what impact it will have.

Does the model lean towards machine strength activities or more towards human strength activities.

The former will potentially lead to more of a replacement potentially with a lot less testers doing those activities going forward.

If it is the latter then AI is likely more just another tool in your tool box.

What we have seen with automation was that a lot of companies dropped a lot of the human strength activities and faster good enough is very common among mainstream companies.

Those hands on, highly technical risk focused testers often turned out to be much more efficient than those testers working to machine strengths which meant a lot less of them could do higher value testing so overall tester numbers dropped.

You can often see that in ratio’s where those doing scripted testing by hand often had ratio’s of 1-2-1 or 1-2-2 with developers but when they switched its more common for a single tester to cover say up to 10 developers work.

AI remains a bit of wait and see, in theory everyone should be more productive but will that mean more testers or more the otherway with testers covering an increasing number of developers work.

One of the bigger risks remains though, that more companies will just dropped the human strength elements and opted for that quick good enough model.

Will AI encourage that angle more, even quicker and potentially a higher acceptability of an even lower good enough bar.

This may be where we find really good testers facing an uphill battle, many companies will see that human value but will mainstream? Will it continue on a potentially lower good enough path that will consciously accept that loss of the testers human strengths?

When that bar is faster, lower and importantly accepted, that is where good testing gets impacted.

A lot of voices are required to at least make companies aware that a higher bar is possible and at pace so at least if the lower bar decision is made, it is at least made being aware of an alternative.

3

Hi @andrewkelly2555,

thank you for your thoughtful response – I really appreciate the depth of your perspective.

I fully agree that there’s a risk in organizations adopting the “fast and good enough” mindset without fully recognizing what gets lost in that trade-off. But to me, this isn’t necessarily a new battle introduced by AI. Even without AI, we’ve seen companies question the value of testing, cut corners, or underinvest in quality practices. In that sense, AI doesn’t change the nature of the challenge – it just gives it a new shape and perhaps a new speed.

Where I do see something different is in how visible and accepted the trade-offs might become. If AI lowers the threshold for what’s considered “good enough” and wraps it in automation and scale, it could lead to faster decisions that deprioritize human insight. That’s where your point hits home: we need strong voices to keep the conversation alive around what’s possible, not just what’s efficient.

Skilled testers have always had to advocate for the value they bring beyond execution – critical thinking, context awareness, user empathy. I believe those strengths will become even more important in an AI-supported world, not less. But we’ll have to be more vocal and proactive to make that case.

Thanks again for raising this — it’s exactly the kind of dialogue we need.

1 Like