Still guessing on Amazon listing images? Today's guest shares a simple AI image workflow that makes decisions easy—what to fix first, what to test, and how to know it’ll win.

If your AI-generated Amazon images look “technically perfect” but still don’t convert, you’re not alone, and you’re not missing more prompts. In this AIM (AI Monthly) session, our Amazon creatives expert guest breaks down the real issue. AI and designers can execute, but they can’t decide strategy for you. That’s why sellers often spend thousands on creatives that look good, yet still fail to drive more clicks and sales.

Hannah Lyss Tampioc is the Founder and CEO of Mad Cat Creatives, and her team has worked with more than 300 brands. She walks through how shoppers actually buy on Amazon and explains why each image serves a different purpose. Your main image needs to stand out in mobile search results. Images two and three should help shoppers quickly understand what they’re getting. The rest of your images and A+ Content should build confidence by answering objections and removing hesitation. The key is figuring out whether you have a click problem or a conversion problem, then fixing the right part of your image stack instead of randomly “refreshing” everything.

The centerpiece of the episode is Hannah’s SORT framework. First, you spot the priority so you know what to fix first. Next, you gather the right context by pulling mobile search screenshots, competitor pages, reviews, and Rufus questions. Then you use that information to reason through the data, so your AI outputs are based on real buyer language instead of guesses. Finally, you test before committing by validating your image ideas with polling tools like the Helium 10 Audience tool, powered by PickFu, before you publish. You’ll also see a real example using Bradley Sutton’s Project X Coffin Shelf listing, where small changes like aspect ratio, mobile-first sizing, and packaging callouts helped the main image stand out more when it mattered most.

In episode 495 of the AM/PM Podcast, Bradley and Hannah discuss: