AI E-commerce Operations: 7 Critical Mistakes to Avoid I've seen more failed AI implementations than successful ones. Not because the technology doesn't work, but because teams make predictable mistakes that doom projects before they reach production. After reviewing dozens of e-commerce AI initiatives—from demand forecasting disasters to recommendation engines that actually decreased conversion rates—clear patterns emerge. These aren't exotic edge cases; they're common traps that even sophisticated retailers fall into. Here's what to watch for and how to avoid the most expensive pitfalls. The promise of AI E-commerce Operations is real—I've seen retailers achieve 20-30% improvements in key metrics when implementation goes well. But the path from proof-of-concept to production value is littered with abandoned projects and wasted investment. Most failures aren't technical; they're strategic and organizational. Let's examine the most common mistakes and how to sidestep them.…