Why Starbucks Built an AI for Its Stores

To most people, it seems like a normal Starbucks crowd: the same mornings, the same beverages, and the same line inching forward. But what they can’t see is the invisible layer using AI to decide on orders and staffing numbers before anyone even opens an app.

Inside Starbucks, that layer has a specific name. Deep Brew is the company's AI platform, and some of its projects have already begun delivering a return on investment of around 30 percent on the money spent to build them.

The interesting part is not that “Starbucks uses AI.” It’s that Deep Brew seamlessly integrated itself into everyday decisions about ordering, staffing, and app content without changing the surface experience.

Deep Brew: Starbucks' Retail AI Platform

Deep Brew rests on a foundation of data stretching back more than a decade through the Starbucks app, rewards program, and registers. Every order, reload, and check-in contributes to an expanding record of what people buy, when they buy it, and how weather or seasons affect demand.

The platform integrates this app usage, sales data, and local trends with weather reports and even signals from connected machines. On the other side, it pushes out decisions: which offers appear in the app, how much inventory a store receives, and how many partners should be working at 8:00 a.m. versus 3:00 p.m.

A simple analogy is: data flows in from stores and customers, Deep Brew makes sense of it, and its outputs guide marketing and supply.

Predictive Scheduling: Aligning Labor with Demand

The first place these decisions appear is in staffing.

In the old system, many stores used fixed templates. A manager decided how many partners opened, served lunch, and closed. Local knowledge helped, but gaps remained. Some days were painfully understaffed, while others had too many people on the clock with little to do.

Deep Brew starts with a different foundation. It analyzes what a store does by the hour, compares mobile orders against in-store traffic, and considers factors like school schedules or weather that drive demand for hot or cold drinks. Using this, it can recommend different staffing levels for 8:15 a.m. on a Tuesday compared to 10:30 a.m. on a Saturday.

That changes the trade-offs. There are fewer moments where one barista is overwhelmed by mobile orders while a line waits at the counter. There are also fewer quiet stretches with a full staff standing around. For the team, it feels like the store is prepared for the day it actually gets, rather than the day someone guessed at.

Retail Inventory Automation: Reducing Stockouts and Waste

The other side of the equation is inventory. A perfect staff does no good if the fridge is empty.

In 2025, Starbucks rolled out an AI-driven inventory system to thousands of stores across North America. Associates no longer manually check shelves with clipboards. Now, they walk the stockroom and fridges with a tablet. A computer vision model identifies what products are on the shelf and how much remains, pushing that data into the same systems Deep Brew uses.

This means counts happen faster and more often, with less chance of missing an item or misreading a label. With fresher, accurate information, Deep Brew can recommend precise order levels. Stores carry less product that will never sell, and they are less likely to run out of a key ingredient during a rush.

External breakdowns link this to marked decreases in overstock, fewer stockouts, and a reduction in waste. In plain terms, less food ends up in the garbage, and there are fewer “We’re out of that” conversations at the register.

AI Assistant for Baristas: Starbucks' Green Dot

While Deep Brew operates in the background, Starbucks has started testing a more visible form of AI on the bar itself.

A pilot at select U.S. stores allows baristas to use Green Dot Assist, a generative AI assistant on in-store tablets based on the same tech stack. It answers common questions, like how to build a complex seasonal drink or what to do if a machine displays a specific error code.

Rather than digging through a binder or waiting for a supervisor, a partner can type a question and get an immediate answer. The goal is not to replace partners, but to support them by removing the friction of finding rules and recipes.

Green Dot Assist fits the same pattern as Deep Brew and the inventory system. It uses AI for small decisions that slow people down: what to do next, what to order for next week, or how to answer a tricky question.

What Starbucks’ 30% ROI Looks Like

The numbers associated with Deep Brew, around 30% return on investment, plus higher app engagement and lower waste are impressive. But the more fascinating part is how quietly they appear.

There isn’t a robot barista in the corner. Instead, these changes show up as a line that moves more steadily, a drink that is available more often, and a shift where staff is less stretched at exactly the wrong times. Deep Brew takes existing patterns and turns them into live inputs. That is where the return actually comes from.