The current state of AI in software testing is beginning to look a lot like a gigantic Rube Goldberg machine with vendors just bolting more onto their robotic “tester friends” in hopes something will finally evolve into “autonomous testing.”

The latest Gartner Magic Quadrant for AI-Augmented Software Testing Tools accidentally exposes the problem. Vendors focused entirely on features built on a “more is more” strategy, but not talking about how this all fits into a coherent model for testing.

Gartner states these tools aim to deliver “continuous, self-optimizing and highly autonomous testing in the SDLC through the use of AI” and promise efficiency, effectiveness, and speed in generating, maintaining, and prioritizing tests.

Reality (as usual) is a lot messier.

Unfortunately, most enterprises are approaching AI in testing the same way they approached test automation for the last 20 years: opportunistically. The endless pursuit of fixing problems they don’t fully understand that can’t be explained without invoking a tool.

Just take the implications of Gartner’s central assumption:

“By 2028, 70% of enterprises will integrate AI-augmented testing tools, up from 20% in early 2025.”

If 70% of the industry adopts AI-augmented testing on top of their current test approach, we’re not entering a golden era of testing, we’re running the risk continuing to ride the “psychotic horse toward a burning stable”. (Thank you Robin Williams!)

And they know it. Gartner’s report is sprinkled with red flags disguised as “cautions” like inconsistent user experiences, unclear vision, lack of roadmap, fragmented platforms, and irregular releases across multiple vendors.

That sounds like a Rube Goldberg machine where the bowling ball rolls down the ramp, knocks over a toaster, flicks a marble down some straws, but instead of wiping your face with a napkin, it tries to clean your mouth by slapping you 4000 times in the face.

And that’s why having a coherent set of principles to guide how you test and align to your business strategy and risk management is so crucial to success.

Before you let AI choose what to test, when to intervene, or decide if a test should be trusted, governance considerations like traceability, audit readiness, and explainability of decisions must be foundational and based on solid testing principles.

In times like these when the testing industry’s compass isn’t aligned to risk, quality objectives, or governance but to capitalizing on a trend, we have to stand guard and scrutinize claims with clarity and conviction.

I truly believe the real competitive advantage in using AI in testing won’t come from having the most AI features or tools, it will come from knowing exactly why each one exists, how it fits into the system, and most importantly, when not to use them at all.

Full credit to Pradeep Soundararajan for inspiring this post at Agile Testing Days