AI helps sellers predict demand and stock smarter Most e-commerce sellers I talk to are sitting on one of two problems: too much of the wrong stuff, or stockouts on the things that are actually moving. Sometimes both, at the same time, in the same warehouse. That's a forecasting problem, and it's been around longer than the internet. What's changed is that AI is genuinely useful now for shrinking it — if you use it for the right things. Where it earns its keep is in velocity

A buyer I talked to last year told me they'd just written off $340,000 in seasonal overstock because their forecast was built on the previous two years of sales data — which included a pandemic year and a supply shock year. Two bad inputs, one very expensive mistake. That's not a technology problem, that's a data problem disguised as a process problem, and it's more common than people admit. What good AI-powered forecasting actually does is pull in signals that a spreadsheet

Building a demand forecast from historical sales data sounds like a data science project. It's not. It's mostly just cleaning up your own mess. Here's what I mean. When you pull 12 months of sales history to start a forecast model, the first thing you'll find is that three of those months are lying to you. A stockout in February made it look like demand dropped. A promotional push in Q3 inflated one SKU by 40%. A receiving error in October logged 200 units that never actual