What We Got Wrong (So You Don't Have To) Our visual search launch was a disaster. After three months of development, we went live with great fanfare—and watched engagement rates plateau at 0.3%. Customers uploaded images that returned zero results. Mobile performance was terrible. Our merchandising team couldn't override bad algorithmic matches. We nearly killed the entire project before diagnosing and fixing seven critical mistakes that I now see repeated across the industry. If you're reading implementation guides and vendor case studies, you're seeing the success stories. What you're not seeing are the expensive mistakes that tank visual search ROI before you ever realize the benefits. After fixing our implementation and consulting with other e-commerce teams, I've identified patterns of failure that kill AI Visual Search projects. Here's what actually goes wrong and how to avoid it. Mistake 1: Launching with Inconsistent Product Images This was our biggest error.…