Games, Teams, and Moonshots: Google Cloud’s Will Grannis

**Shervin Khodabandeh** (0:03)
Can you get to the moon without first getting to your own roof? This will be the topic of our conversation with Will Grannis, Google Cloud CTO.

**Sam Ransbotham** (0:13)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence in 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** (0:34)
And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

**Sam Ransbotham** (1:04)
We're talking with Will Grannis today. He's the founder and leader of the Office of the CTO at Google Cloud.
Thank you for joining us today, Will.

**Will Grannis** (1:11)
Yeah, great to be here. Thanks for having me.

**Sam Ransbotham** (1:13)
So it's quite a difference between being at Google Cloud and your background. So can you tell us a little bit about how you ended up where you are?

**Will Grannis** (1:22)
Call it maybe a mix of formal education and informal education, formerly Arizona Public School System, and then later on West Point, math and engineering undergrad, and then later on UPenn, University of Pennsylvania, Wharton for my MBA.
Now, maybe the more interesting part is the informal education. And this started in the third grade. And back then, I think it was gaming that originally spiked my curiosity in technology. And so this was Pong, Oregon Trail, Intellivision, Nintendo, all the gaming platforms. I was just fascinated that you could turn a disc on a handset and you could see Tron move around on a screen. That was like the coolest thing ever. So today's manifestation, Khan Academy, edX, Code Academy, platforms like that, this entire online catalog knowledge, thanks to my current employer, Google.
And just as an example, like this week, I'm porting some machine learning code to a microcontroller and brushing up on my C thanks to these, what I'd call informal education platforms. So a journey that started with formal education, but was really accelerated by others, by curiosity and by these informal platforms where I could go explore the things I was really interested in.

**Sam Ransbotham** (2:42)
I think particularly with artificial intelligence, we're so focused about games and whether or not the machine has beat a human at this game or that game, when there seems to be such difference between games and business scenarios.
So how can we make that connection? How can we move from what we can learn from games to what businesses can learn from artificial intelligence?

**Will Grannis** (3:05)
Gaming is exciting and it is interesting, but let's take a foundational element of games. So understanding the environment that you're in and defining the problem you want to solve. What's the objective function, if you will?
That is exactly the same question that every manufacturer, every retailer, every financial services organization asks themselves when they're first starting to apply machine learning.
And so in games, the objective functions tend to be a little bit more fun. It could be an adversarial game where you're trying to win and beat others. But those underpinnings of how to win in a game actually are very, very relevant to how you design machine learning in the real world to maximize any other type of objective function that you have. So for example, in retail, if you're trying to decrease the friction of a consumer's online experience, you actually have some objectives that you are trying to optimize. And thinking about it like a game is actually a useful construct at the beginning of problem definition. What is it that we really want to achieve? And I'll tell you that being around AI and machine learning now for a couple decades, you know, when it was cool, when it wasn't cool, I can tell you that the problem definition and really getting a rich sense of the problem you're trying to solve is absolutely the number one most important criteria for being successful with AI and machine learning.

**Shervin Khodabandeh** (4:19)
Yeah, I think that's quite insightful, Will. And it's probably a very good segue to my question. That is, it feels like in almost any sector, what we're seeing is that there are winners and losers in terms of getting impact from AI. There are a lot less winners than there are losers.
And I'm sure that many CEOs are looking at this wondering what is going on. And I deeply believe a lot of it is what you said, which is it absolutely has to start with the problem definition and getting the perspective of business users and process owners and line managers into that problem definition, which should be critical. And since we're talking about this, it would be interesting to get your views on what are some of the success factors from where you're sitting and where you're observing to get maximum impact from AI.