Applying AI Knowledge at McDonald's

"Would you like fries with that? McDonald's already knows the answer."

That headline from a 2019 New York Times article sounds ominous, but in reality, it captures the essence of why many brands are adopting artificial intelligence (AI) into their day-to-day operations: The goal is to make life easier for the consumer. 

Since 2019, McDonald's has spent millions of dollars to acquire multiple AI companies in an effort to automate and improve their customer experience, particularly at the drive-through. Examples of this include digital menus that provide food and drink recommendations based on the current weather, or license plate-detecting software that offers menu recommendations based on previous purchases.   

"They definitely have been in the spotlight in the last two years when it comes to data science," said Rhett D'souza (MSAI '19), who joined McDonald's as a data scientist manager in early 2021.  

D'souza is part of McDonald's Global Data Analytics Hub, where he and his colleagues provide automation, machine learning, analytical, governance, and other data-related expertise across the organization.  

"We have business agnostic data scientists who can go to each business group of the company and solve their data science and analytics problems," he said. "My role is to hop on one or two of these projects at a time and come up with best practices and solutions, and then implement them for the specific projects."

D'souza said his role is perfectly calibrated for a graduate of Northwestern Engineering's Master of Science in Artificial Intelligence (MSAI) program. MSAI puts an emphasis on usability and helping students learn how to identify opportunities where advanced technologies can be applied, rather than simply making up problems to solve. The program is technical which prepares graduates to smoothly transition into full-time work and make an immediate impact.

That's what happened for D'souza, who worked at Deloitte as an AI consultant and data scientist after graduation.

"Coming to a company like Deloitte, or a company like McDonald's, you recognize the tech stack right away," he said. "You already know what the applications do and what you're supposed to do with them, so all you have to do is look at your new problem and find a good solution for it."

At Deloitte, D'souza helped develop propensity models for a retail pharmaceutical client trying to initiate a new program so customers could pick up prescriptions less frequently. He also helped a business-to-business commodities and food supply client do customer segmentation as part of a larger push to centralize its data. The work he's most proud of was the development of a sequence-based recurrent model for a health insurance company that predicts a patient's likelihood of developing type 2 diabetes, chronic kidney disease, or end-stage renal disease.

D'souza is convinced he would not have been able to impact those projects or have his current role at McDonald's if it weren't for his time in MSAI.  

"There's general development that happens over time as you work and learn new things," D'souza said, "but MSAI set the groundwork for me."