Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

# Introduction

***In this chapter, we are going to deconstruct the AI and data strategies of some of the most renowned brands. To maximize learning, we will look at diverse examples — from cups of Lattes and movies to fraud detection and wildlife conservation. I believe that these kinds of case studies are important because, even if you are not into the coffee business, exposure to diverse use cases and/or industries can help us become more creative within our field as well. You never know where you might find a golden insight. Think of the growth that comes from traveling and being exposed to different cultures. This is just like that!***


What might seem like *just a cup of coffee*, in reality, is a cup that carries in its history many decisions and strategies. Let's dig into the details of the **tech-infused cup of coffee** to gain insights into making real-life, data-driven decisions.

While coffee beans are used to brew addictive coffees, Starbucks uses **“data beans”** primarily for: **expansion, personalization**, and **innovation**.

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# Expansion
Learning from failures is essential. In 2008, when Starbucks’ CEO, Howard Schultz, was faced with the tough decision of closing hundreds of stores, he insisted that they take a more analytical approach going forward. Choosing the right location is essential for the success of businesses like Starbucks. So today, the company uses a smart system called Atlas, developed by a **location-analytics** company called Esri. This data-driven approach takes into account features like population density, average incomes, and traffic patterns to estimate the profitability and economic viability of the new store. Additionally, it predicts the impact of new stores on already existing Starbucks in the area — compete with competitors and don’t bring your own brand down. This means that now the decisions are driven by data and not just human opinions and gut feelings. Of course, this does not imply that humans are completely out of the loop. At least not yet!

# Personalization
With ninety million transactions a week in 27,000 stores worldwide, Starbucks is on the right footing for making marketing and business decisions based on data. But where is this data exactly collected from?

Starbucks launched a **rewards program** and a **mobile app** so that they could get **“users in and data out.”** Data collected via the reward programs (more than 13 million active users) and the mobile app (more than 17 million active users) allows the coffee giant to learn about exact user behaviors, including: “Do you like to indulge more on a rainy day? Do you like muffins over croissants with your pumpkin-flavored tea? Do you order a different type of coffee on workdays than on the weekend?”

Here are some of the ways in which the data collected is being used for creating personalized experiences.

No matter which Starbucks store in the world you visit, your data collected via the app allows the smart system at the new store to know about your order preferences — your usual drink for “that kinda day.” Also, Starbucks has been trying to harness the power of AI to provide recommendations. They have enabled a program called **Digital Flywheel** for the rewards members’ accounts. Deciding what to eat is not always an easy decision, and AI is being used to lure customers into buying more by providing suggestions based on factors like purchase history, weather, time of the day, and birthdays. The same AI technology is also being used to send consumers personalized offers and to re-engage them in case it has been a while since they had last visited Starbucks.

Starbucks is trying to maximize their AI adoption by deploying voice and text recognition AI. My **Starbucks Barista** is a virtual barista in the mobile app for easily placing orders via voice or text messages as reduced friction in placing orders means potentially more sales.

Today, U.S. customers use the mobile app to pay for 34% of orders, and 12% of orders are placed in the app rather than at the counter. At 3,600 locations, that figure is 20% or above during peak business hours. This translates into a significant data footprint and reflects the **importance of investing in customer-facing technologies**. I believe the mobile app and rewards program are key to Starbucks’ continued success in the future. As the amount of data keeps accumulating, AI recommendations will keep getting better and better, and satisfied customers will keep coming back.

One important thing to note here is that adding a mobile ordering service is **more than just creating a mobile app**. The entire workflow needs to be reexamined and adjusted to complement these new technologies. Starbucks had to add new roles and resources to prevent customers (who had ordered on their app) from walking away because there was a mobile queue at the store upon arrival. We are living in an era of **instant gratification**.

# Innovation

## Product offerings
When Starbucks decided to expand their business and serve their products from grocery stores, they **let the data do the talking** in deciding which products to offer.

For example, the analysis that about 43% of tea drinkers don’t add sugar in their tea and about 25% don’t add milk to their iced coffee when drinking the beverage at home, led Starbucks to create two unsweetened iced tea K-Cups: Mango Green Iced Tea and Peachy Black Tea. Pumpkin spice cafe latte and iced coffee without milk or added flavors are some other fruits of such data crunching.

From offering alcohol in certain locations and coming up with special Frappuccino promotions to taking advantage of heatwaves, Starbucks is making business decisions led by data.

## Starbucks of the future


Let’s take a look at a real-life case study of AI.


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Starbucks: The Millions of AI Infused Cups of Coffee

Introduction