Starbucks, Coca-Cola, Netflix: How Are They Actually Making Money with AI?

A while back, a friend who works in branding came to me with a question.

"AI is everywhere right now — should I be investing in it? Is it genuinely useful, or is this just another round of snake oil?"

I didn't rush to answer. Instead, I threw the question back at him.

Before you ask whether you should invest, think about this: the big companies already using AI — what specific problem are they solving with it?

Once you figure that out, you'll know whether you should follow suit.

I dug through several case studies recently. After reading them all, I walked away with one impression: AI is long past being a "trend" that marketers talk about. It's already helping these companies make money, retain customers, and produce hits.

Today, I'm not going to give you theory. I'm going to tell you five real stories. You listen, and you decide for yourself.

1. Starbucks: Taking "We Know What You Want" to the Extreme

What does personalized recommendation really mean?

Simply put: the platform knows what you want to drink in the morning better than your own mother does.

Starbucks has nearly 35 million monthly active users on its app. Every user — when they order, which store they visit, what they buy, whether they add sugar, whether they're in a rush — Starbucks has all of this data.

So what does it do with it?

It did one thing: push every user the next drink they probably want.

A rainy afternoon? The app suggests a hot cocoa. You just got off a flight? It pushes a double espresso. You've been on a diet lately? It recommends an oat milk latte.

This isn't spray-and-pray coupon blasting. This is precision targeting.

It even comes with an AI customer service agent — can't find your order, wondering how to redeem points? The bot answers you directly.

The result? Users stick around. They buy more frequently. Average order value goes up.

Starbucks turned AI into a star store manager who never forgets a single regular's preferences. 35 million users — it remembers every single one.

2. Sephora: Letting You Try Lipstick Without Stepping Into a Store

Think about it — what's the biggest worry for a woman buying lipstick online?

The shade is wrong.

The picture looks like a dusty rose, but on your lips it turns into Barbie pink. You're too lazy to return it, but you can't bear to throw it away either. One bad experience, and next time she won't dare order online again.

Sephora came up with a solution.

They built a feature called Virtual Artist. You open your phone camera, and it recognizes your face shape and skin tone, then layers different shades of lipstick, foundation, and eyeshadow right onto your face.

In real time. Switch a shade, your face changes instantly. Try ten, see ten different looks.

Behind this is AI plus AR. The AI handles face and skin tone recognition; the AR handles "painting" the color on.

The result? Women feel confident enough to place orders. They know what they're getting. Return rates drop.

What Sephora did, at its core, comes down to one sentence:

They took the beauty counter assistant who says "try this shade" and put one in every customer's phone. And this assistant never clocks out — 24/7.

3. Coca-Cola: Turning Users Into Creators

The Coca-Cola case is particularly interesting, I think.

They didn't use AI for precision targeting. They didn't use AI to optimize creative assets. They did something bolder:

They used AI to turn hundreds of millions of consumers into co-creators of their brand.

The campaign was called "Create Real Magic." The rules were simple: we're opening up Coca-Cola's logos, color palettes, and classic visual assets to you. You use our AI tools to generate your own artwork.

You draw it. You share it. You spread it.

Think about it — when an ordinary consumer uses Coca-Cola's brand assets and AI to create a genuinely cool poster, what do they do?

They post it on social media. They put it on Instagram. They tag Coca-Cola.

This is UGC. Users produce the content themselves, distribute it themselves, and bring in new users themselves.

Coca-Cola just handed over the materials and tools. The rest, the users took care of.

And this deepened the emotional connection the younger generation feels toward the brand — they felt like they "participated," not just got "sold to."

This is the most valuable application of AI on the brand side: not replacing your creative work, but making the user part of the creative process itself.

4. Nike: Using AI to Predict the Next Hit Product

In the sneaker business, the hardest part isn't design. It's not marketing either.

It's betting on the wrong product.

A shoe goes through R&D, material sourcing, production, and channel distribution — only for nobody to buy it when it hits the market. You discount it to clear inventory. An entire year wasted.

How does Nike solve this problem?

It uses AI to analyze three things: user behavior data within its apps, what styles people are talking about on social media, and overall market sales trends.

And then? AI tells them: next quarter, dad shoes might be coming back. This color combination is generating buzz among Gen Z.

What does Nike do with this insight? Two things.

First, it guides product design. Designers no longer go with their gut — they make decisions based on the signals AI provides.

Second, it optimizes the launch cadence. When to release, how much stock to deploy, which channel goes first — all backed by data.

The result? New product launch cycles got shorter. Inventory pressure decreased. The products that were supposed to hit actually hit.

Nike turned AI into a head buyer who never misses a trend. The designers listen to it. The channels listen to it too.

5. Netflix: Knowing What You Want to Watch Better Than You Do

You've probably heard of the Netflix case. But you might not realize how deep they go.

They have a massive user base. What each person watches, how long they watch, when they pause, when they skip the intro, which show they abandoned halfway through — they don't miss a single piece of behavioral data.

So what do they do with all this data?

They tell your fortune. They predict what you want to watch tonight.

Going one step further — they even personalize the cover art. The same show: the thumbnail you see and the one your roommate sees might be completely different images. Because you're more likely to click on a suspense-style cover, that's the one they show you.

This is what personalization looks like at its absolute peak. It's not helping you find a show — it's helping you make one less decision.

In an era of content overload, whoever can make users decide less, scroll less, and spend less time searching wins.

That's exactly how Netflix keeps its retention rate so stable.

So, Back to You

After telling these five stories, let me come back to my friend's original question.

Should you invest in AI?

I think you're asking the wrong question.

The right question is: in your business, is there a bottleneck that humans simply cannot handle?

• Starbucks couldn't handle personalization for 35 million users. So they used AI.
• Sephora couldn't handle giving every online customer a makeup try-on. So they used AI.
• Coca-Cola couldn't handle getting hundreds of millions of people to create for them. So they used AI.
• Nike couldn't handle predicting the next trend. So they used AI.
• Netflix couldn't handle picking the right show for each user out of an ocean of content. So they used AI.

They didn't use AI "for the sake of using AI." They first spotted a bottleneck they couldn't handle, then discovered that AI happened to solve it.

That's how you should be reading these case studies.

Don't copy their tools — copy their thinking:

Find your bottleneck first. Then look for tools.

The rest is just rolling up your sleeves.