Starbucks

As business leaders, what do we already know about Starbucks?

Fortune 500 company with 346,000 employees. Valued at $98.23 billion based on market cap. Owns 40,729 stores globally as of Q2 2025. Probably a coffee shop you wouldn’t mind spending a day sipping coffee, working, and taking meetings.

That’s the Starbucks we’ve seen over the years. Might have even endlessly heard “Starbucks doesn’t sell coffee. It sells experience” from our marketing peers.

But…

In today’s AI at the Top, we will look into the backend of Starbucks.

We will understand how the company uses AI across consumer experience, supply chain, employee productivity, and more.

As we study enterprises, we’re learning this fact about successful AI transformations in leading non-tech companies: Data is the moat.

It means the earlier you get access to your customers’ first-party data and the faster you make your data AI-ready (might be on public clouds or on-prem), the more efficient your systems and processes will get.

We have already noticed this pattern in enterprises like Walmart, JPMorgan Chase, PepsiCo, Nike, etc.

Starbucks aced its first-party data with its mobile app.

In 2011, Starbucks launched its mobile app with a rewards program. Every time you order via app, you get stars to redeem on your next purchase.

This simple DTC tactic drove customer engagement on the app, unlocking insights into ordering patterns. 

The company knew what menus their customers preferred, popular locations, favourite beverages at what times, etc.

By 2023, a quarter of its 100 million transactions were through the app.

…and by the end of Q1 2024, the app had 34.3 million active members in the US alone.

Similar to Walmart’s Wally, Sparky and PepsiCo’s PepGenX, Starbucks built its proprietary AI. It’s called Deep Brew and is deployed across core business domains.

Deep Brew analyses multiple variables like purchase history, frequently visited cafes, local weather, community behaviour, time of the day/week, etc., and ties insights to send unique promotions to each customer.

One specific example is when the AI insights led to new beverages like unsweetened iced teas when the company realised 43% of tea drinkers add no sugar.

This cycle of studying complex data from multiple perspectives and delivering a personalised experience increased customer engagement by 15%.

Starbucks has also introduced chatbots and voice assistants to reduce wait times at stores and answer questions in natural language. You can just say, “Order my most preferred cold brew on Wednesday afternoon.” It knows what to order.

The enterprise uses AI to solve a simple supply chain outcomes: Save money, reduce wastage, reduce overstocking, and make sure ingredients and popular items are always available.

Deep Brew analyses weather, local community events, sales data, consumer trends, etc., to ensure the supply chain is intact in real time. Like stocking the stores with ice and milk for cold coffees when a heat wave is around the corner.

In one of the use cases against climate risks, Deep Brew credited Starbucks’ system with $125 million in annual financial benefits. $50m in preserved revenue. $40m in direct cost savings. $15m in sustainability.

Plus each of the espresso machines and grinders is connected to Deep Brew. The AI predicts the maintenance needs based on the machines’ life value way before a critical disruption.

That’s the reason we don’t often hear, “Sorry {{first_name|Sir/Ma’am}}, we can’t brew a Cappuccino because our machine isn’t working.”

One of the core Starbucks philosophies is about its connection with its customers. The brand wants the baristas to spend more time having conversations with visitors and building relationships.

To free their time, Starbucks introduced Green Dot Assist in June 2025. The use case was simple. Baristas shouldn’t read pages of manuals, reports on equipment, etc. You get real-time answers to your questions with the chat assistant.

Questions like “What’s the espresso shot count for a veti americano?”, “How much steamed milk versus the foam should a flat white have?”, or even “When will I get a raise?”

Okay maybe not the third one.

Tangible outcomes from when Starbucks first deployed Green Dot Assist in 3500 North American stores:

It’s risky with real estate.

It’s a commitment to choose a location, then build a store and run it successfully.

To reduce the chances of opening an underperforming store, the enterprise uses a tool called Atlas AI to process variables like income levels, traffic, business ecosystems, mobile app data, etc., to predict the profits from a new store.

Starbucks has previously reported a 30% ROI on its AI investments.

The enterprise, of course, will look to improve these numbers. 

In 2026, Starbucks will launch Grow Report, a tool to help coffeehouse leaders scale growth by identifying key factors. Looks like all the focus is on optimising growth and sales without compromising on customers’ experiences, as it should be for any large enterprise.

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