AI tools creating more noise than signal in your testing?

Download the free guide: 5 small experiments you can run this week to figure out where AI actually helps your workflow.

When I surveyed testers about their biggest AI frustrations, 65% told me the same thing: the output is unreliable. One person called it "slop." Another described the correction loop as exhausting.

The problem is rarely the tool. The problem is knowing which testing problems AI can actually solve for you, and which ones it makes worse.

This guide gives you five focused experiments to find out. Each one takes less than a day. None of them require you to change your process or learn a new tool.

Here is what the five experiments cover:

• How to test whether AI-generated test cases catch bugs yours miss (or just add noise)

• A quick way to measure if AI saves you time or costs you time on a real task

• How to evaluate a new AI tool in under an hour, without committing to a pilot

• A method for turning your tacit testing knowledge into context AI can actually use

• How to spot where AI amplifies bad strategy before it wastes your week

This guide is for you if:

• You are using AI tools in your testing work but the results feel inconsistent

• Your manager expects AI to make you twice as productive, and you need a way to figure out what is realistic

• You are tired of AI generating tests that tick boxes but miss the bugs that matter

• You want a practical starting point that does not require overhauling your entire process

If you have ever caught yourself thinking "no wait, not what I meant, let me explain" while working with an AI tool, this guide was written for your exact situation.

The guide is free. It takes about 15 minutes to read. You could run your first experiment before the end of the day.

I will send you the guide by email. No spam, no 47-part sequence. Just the guide and occasional updates about quality and AI in testing.