AI Has Become a Commodity. Intelligence Hasn’t.
Over the past year, we’ve spent a great deal of time thinking about AI in the context of PractiTest.
Like many other companies in the testing space, we’ve invested heavily in understanding how this technology can genuinely help our customers. We’ve experimented, built prototypes, released features, gathered feedback, and, most importantly, spent countless hours talking with the organizations and teams that use our product every day.
One thing became very clear along the way: building AI features is relatively easy. Building AI features that consistently create real value is much harder.
As with every major technological shift, there’s always a period where companies rush to adopt the new capability simply because the market expects it.
AI is no exception.
Today, many respectable organizations require that every tool they purchase “has AI,” even when they haven’t defined what they actually expect AI to improve.
We’ve even heard of executives announcing specific productivity targets tied to AI adoption before even stopping to think and decide how AI should be used in their own processes. That’s not a technology strategy, it’s treating AI as a magic wand. And while the pressure is understandable, it often leads organizations to focus on adding AI rather than on solving the problems AI was supposed to address in the first place.
Regardless of the reason or motivation, today virtually every testing platform includes AI modules. We have systems that can generate test cases, others that write automation scripts, some that summarize executions, and those that produce bug reports. All these capabilities are useful. They save time and remove repetitive work. They absolutely deserve to exist. And they are also rapidly becoming expected commodities.
Customers no longer buy a platform because it has AI. They expect AI to be there, in much the same way they expect cloud hosting, dashboards, or integrations.
If everyone can use AI to generate artifacts, then generating testing artifacts is no longer where the real value lies.
We Solved One Problem…
For many years, one of the biggest bottlenecks in testing was creating enough assets. Teams constantly struggled to keep up with the pace of development. We needed more test cases, more automated scripts, more execution coverage, more dashboards, and more structured bug reports.
AI represents an enormous leap forward in that journey. Today, creating a hundred additional test cases is no longer a major effort. The same goes for automation scripts, test executions, or initial bug reports. Work that used to take hours or even days can often be completed in minutes.
That is a genuine achievement, and it is already changing the way testing teams work.
But like many technological advances, solving one problem exposed another, or as it is commonly referred to: The Law of Unintended Consequences.
…And Quietly Created a New One
Once generating artifacts becomes inexpensive (or at least less time consuming), we naturally generate more of them. More tests. More runs. More reports. More dashboards. More documentation. More metrics. More information.
At first, this feels like progress. More visibility should lead to better decisions… except it doesn’t always work that way.
Not every generated test case provides value. Not every report deserves to be read. Not every dashboard answers an important question. Some information is duplicated, some becomes obsolete almost immediately, and some was never particularly useful to begin with.
Large Language Models (LLMs) add another dimension to this challenge. While they produce remarkably good results, they are also capable of producing convincing but incorrect information. Somebody still needs to verify whether the output is accurate, relevant, and worth acting upon.
The bottleneck has shifted. We’re no longer struggling to create information. We’re struggling to understand which information actually matters.
Not Every Test Case Is Worth Running
One of the discussions we’ve been having internally revolves around a deceptively simple question:
If AI can generate thousands of test cases for us, should we really execute all of them?
Let’s stop for a second and think about this…
The goal was never to maximize the number of test cases or to blindly increase the number of runs. The goal has always been to maximize confidence while minimizing unnecessary effort.
Some tests provide tremendous value. They protect critical business flows, expose regressions quickly, and give us confidence that a release is ready. Others add very little, if anything. They consume execution time, increase maintenance costs, and rarely uncover meaningful issues. What’s more, some of them suffer from flakiness, failing inconsistently and eroding trust in the results.
The real and interesting challenge isn’t generating another thousand tests and runs. It’s understanding which tests are actually helping us deliver better software, and which ones have quietly become noise.
Imagine a system that continuously learns from the project’s history. It understands which tests repeatedly find defects, which never fail, which protect business-critical functionality, and which consume significant execution effort without contributing much value.
Instead of asking “Can AI generate more tests?”, we should be asking “Which tests deserve our attention?”
Looking Beyond Individual Tests
The same idea applies to the project itself.
Most dashboards tell us what happened yesterday. Testing Intelligence should help us understand what is likely to happen tomorrow.
Imagine opening your testing platform on Monday morning, not to look at another collection of charts, but to receive meaningful guidance on where to focus your team’s efforts this week.
For example: Three stories have failed testing several times over the past week. Two continue to generate high-severity defects. Another has repeatedly moved back from “Ready for QA” into development. Historically, stories that exhibit this pattern are significantly more likely to delay a release.
No one had to ask for this information. The system connected the signals, recognized the pattern, and highlighted where the team should focus.
The testing team immediately knows where additional effort should be invested. Development understands which features deserve another review. Managers can identify delivery risks while there is still time to react and remove some stories in order to still make the delivery date on time…
That, to me, is where Testing Intelligence begins.
Where I Think the Industry Is Going
I don’t believe the next generation of testing platforms will be defined by how many artifacts they can generate. I believe they will be defined by how well they help teams make decisions.
Generation saves time. Intelligence saves projects.
The future isn’t about producing more tests, more bugs, or more dashboards. It’s about helping teams understand what matters, where the real risks are, and what they should do next.
That’s the vision behind what I call Testing Intelligence, and I believe we’re only at the beginning of that journey.
