On a Saturday afternoon, going into Target feels like something that was planned ahead of time. The shelves are full. The deals in the app don’t seem to make sense. The thing you thought you might need is right where you thought it would be. Of course, none of this happens by accident, and more and more, not much of it happens at all without AI working in the background.
Target has been putting together its AI infrastructure for years, long before AI became a phrase that every business leader used. That early investment is beginning to show in ways that are different from what the company’s competitors have made clear. Chief Information Officer at Target, Brett Craig, says that the foundation wasn’t built overnight. Over many years, people worked hard to make systems that could adapt quickly to changes in technology.
Target’s strategy is interesting and worth keeping an eye on because it’s not just about saving money. The main reason most stores use AI is to improve their operations, like getting deliveries faster and having less stock-outs. Target does all of those things and more with technology. It’s also using it to build an emotional connection with the shopper, which is harder to measure. The business seems to really believe that technology should make you feel something, not just find something.
The personalization part is what makes this real. Your shopping habits are used to make AI-powered suggestions in both Target Circle, the company’s loyalty program, and its main app. Just for fun, Craig said that if you opened his app, you’d probably see deals on vinyl records and Favorite Day snack mixes, since that’s what he buys. It’s a little thing that says a lot. The system isn’t sending general ads to a large group of people. It’s learning about you in particular.
Then there’s inventory, which might be the less exciting but more important use case. When people go holiday shopping, they depend on whether or not the item they want is in stock. Target uses AI to predict demand, catch unknown out-of-stock situations before they make customers angry, and place inventory across its supply chain so that it can act quickly when demand changes. Still not sure how well any system can predict a toy going viral or a sudden rush on a kitchen gadget, but Target’s system seems better prepared than most to handle those turns of events.
Roundel, Target’s media company for stores, adds another layer. Labels pay to be seen by shoppers who are already likely to buy them. This isn’t based on guesses, but on real data from Target’s own ecosystem about how people behave. People who shop will mostly notice that the ads seem less random. It’s easier to guess what the return on spend will be for brands. Target sees it as a way to make money that gets better as more information flows through it.
When Craig talks about exploring generative AI, he does so with a measured optimism—not breathless excitement, but genuine interest. Both internal tools for employees and customer-facing apps are being tested by the company, but details are still not clear. That lack of clarity is probably on purpose. Most of the time, stores don’t give away their plans until the results are interesting.
It is hard not to notice how quiet Target has been these days. It doesn’t have a big campaign about its AI goals. No big claims about how to change the way stores work. It’s just systems that keep getting better, a loyalty program that keeps being useful, and shelves that always seem to have what you went there for. That level of consistency, which is based on tech that most shoppers don’t even think about, could be the best use of AI in retail right now.
