This Crazy Wave We’re Riding: Walmart’s Prakhar Mehrotra and the Ups and Downs of AI

**Sam Ransbotham** (0:01)
When you work in an established, successful company, current managers already know a ton. Still, AI solutions can offer insights to even experience managers, if you can get the humans and the AI to work together. In this episode, Prakhar Mehrotra describes some moments where human and AI efforts came together for Walmart. And even more fun, he describes the hard work that it took to make those moments happen.
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business.
Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Information Systems at Boston College. I'm also the guest editor for the AI and Business Strategy Big Idea Program at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:49)
And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. And together, BCG and MIT SMR have been researching AI for four years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:17)
Shervin, I'm looking forward to kicking off our series with today's episode.

**Shervin Khodabandeh** (1:20)
Thanks Sam, me too. Our guest today is Prakhar Mehrotra, Vice President of Machine Learning at Walmart. He's joining us from Sunnyvale, California.
Prakhar, thank you so much for speaking with us today. Could you introduce yourself and share a bit about what you do?

**Prakhar Mehrotra** (1:38)
Hi, I'm Prakhar Mehrotra. I'm the Vice President of Machine Learning at Walmart US. My responsibilities include building algorithms that will power the decision-making of our merchants. Into the core areas like assortment, pricing, inventory management, financial planning, all aspects of merchandising. I lead a team of 80 people. They are data scientists, a full-stack team from data scientists, data analysts, data engineers.
That's my role at Walmart.

**Sam Ransbotham** (2:09)
We're particularly interested in you because you're a top expert in artificial intelligence. Can you tell us how Walmart is using artificial intelligence to improve their business?

**Prakhar Mehrotra** (2:18)
Walmart wants to use AI to serve our consumers better.
And so my role is to make that happen. And my expertise that I gained from Uber and Twitter and my graduate studies have helped me achieve that dream. And the secret sauce that I realized was that AI will be successful in companies if we partner with business closely and take business stakeholders along the journey.
It's not just about algorithms. It's about business because the eventual goal of AI is to improve the business. I'm responsible for all the machine algorithmic developments for core areas of merchandising, which include how do you price something, how do you select the right assortment, replenishment strategies, forecasting and planning. So all the core aspects of merchandising is what we are trying to use machine learning and AI towards.

**Sam Ransbotham** (3:10)
So Prakhar, how did you get started on your path in AI? What are some of the more challenging aspects of implementing AI in your work now?

**Prakhar Mehrotra** (3:17)
So I started my career at Twitter when I was a data scientist. I picked up all the fundamentals of scaling and engineering at Twitter. At Uber, it gave me a massive break where it was like a juggernaut, right? It's like rolling. What disruption is it at Uber taught me? And then when I joined Walmart, I had learned something about AI.
Like I knew how to, I had got some experience about AI management algorithms. I picked up on the fundamentals of AI. And so the most challenging part about at least the work at Walmart on the store side is there are no labels in the data. There are no tags. When customer shops in our store, all we record is or all we have information about is that the transaction was made.
Unlike social media or unlike an app in a Netflix type of or a recommendation type of environment where you know, you can track the history of a consumer and you can learn from it. That environment is not present in the store side. We don't know what items customers are picking up and when they are making these choices. So the job of algorithms is actually a lot harder as they have to infer all this as opposed to directly learn from the data.
And so inference became a big part about Walmart and then translating that inference into actionable insight. That's something we can make a forward looking decision on. And so that was the key challenge at Walmart.

**Sam Ransbotham** (4:40)
How does it feel when you're going from a world where everything is highly quantified to things where everything is abstract but you're still asked to make a decision?