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AI workflow testing reveals why an AI tool can produce one strong draft and then fall apart when you try to repeat the result. In this video, you’ll learn how to diagnose the workflow, not the tool — so you can understand what’s actually causing inconsistency in your AI-assisted content process. You’ll see the three reasoning gaps that distort your experiments, why output quality can look good once and fail across multiple pieces, and how to separate perceived efficiency from real time savings. You’ll walk through the full three-stage testing model — time, voice, and readiness — so you can pinpoint where your workflow needs adjustment before anything scales. You’ll also learn how to define the voice patterns that let you check for consistency across AI-generated drafts, how to identify drift early, and what changes in your day-to-day work once the workflow finally holds. This gives you a repeatable way to evaluate new tools, refine your process, and run a system you can trust. READ THE FULL BLOG POST https://diannerobbinssocial.com/why-y... GET WEEKLY EMAIL INSIGHTS HERE: https://dianne-robbins-social.myflode... CHAPTER MARKERS 00:00 Why Tool Testing Isn’t Telling You the Truth 00:41 Why Your AI Experiments Keep Breaking 04:32 The Three Gaps Distorting Your AI Results 11:47 Does Your AI Workflow Actually Save Time? 15:35 Is Your Voice Showing Up Consistently? 19:42 Does the Content Hold Up Before You Publish? 23:09 What Changes When the Workflow Finally Works 25:46 Where Your Testing Should Focus Now DISCLAIMER Some links in this description are affiliate links. If you make a purchase, I may earn a small commission at no additional cost to you. I only recommend tools I personally use. Your support helps me continue exploring AI content questions. Thank you.