Games, Teams, and Moonshots: Google Cloud’s Will Grannis
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Can you get to the moon without first getting to your own roof?

This will be the topic of our conversation with Will Granis, Google Cloud CTO. Will Granis: 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.

And I'm Sherven Kodabande, 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.

We're talking with Will Granis today. He's the founder and leader of the Office of the CTO at Google Cloud. Thank you for joining us today, Will. Yeah, great to be here. Thanks for having me. 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? Call it maybe a mix of formal education and informal education. Formally, 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, you know, today's manifestation, Khan Academy, edX, Code Academy, platforms like that, you know, this entire online catalog of knowledge, thanks to my current employer, Google.

And, you know, 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, you know, 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.

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?

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. You know, that could be an adversarial game where you're trying to, you know, 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 of 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.

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 right.

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.

I can't speak to exactly why every company is successful or unsuccessful with AI, but I can give you a couple of principles that we try to apply and that I try to apply generally. I think today we hear and we see a lot about AI and the magic that it creates. And I think sometimes that does a disservice to people who are trying to implement it in production. I'll give you an example.

Where did we start with AI at Google? Well, it was in a place where we already had really well-constructed data pipelines, where we had already exhausted the heuristics that we were using to determine performance. And instead, we looked at machine learning as one option to improve our lift on advertising, for example.

And it was only because we already had all the foundational work done, we understood how to curate, extract, transform, load data, how to share it, how to think about what that data might yield in terms of outcomes, how to construct experiments, design of experiments, and utilize that data effectively and efficiently, that we were able to test the frontier of machine learning within our organization. And maybe to your question, maybe one of the biggest opportunities for most organizations today

Maybe it will be machine learning, but maybe today it's actually in how they leverage data, how they share, how they collaborate around data, how they enrich it, how they make it easy to share with groups that have high sophistication levels like data scientists, but also analysts and business intelligence analysts.

professionals who are trying to answer a difficult question in a short period of time for the head of a line of business. And unless you have that level of data sophistication, machine learning will probably be out of reach for the foreseeable future. Yeah, well, one other place I thought you might go is building on what you were saying earlier about the analog between gaming and business.

all-around problem definition, how it's important to get the problem definition right. And what resonated with me when you were saying that was probably a lot of companies just don't know how to make that connection and don't know where to get started, which is actually what is the actual problem that we're trying to solve with AI? And many are focusing on what are all the cool things AI can do and what's all the data and technology we need, rather than actually starting with the problem definition and working their way

backwards from the problem definition to the data, and then how can AI help them solve that problem? It's really a mindset. I'll share a little inside scoop. At Google, we have an internal document that our engineers have written to help each other out with getting started on machine learning. And the number one, there's a list of like 72 factors, things you need to do to be successful in machine learning. And number one is you don't need machine learning.

And the reason why it's stated so strongly is actually to get the mindset of uncovering the richness of the problem.

And the nuances of that problem actually create all of the downstream, to your point, all of the downstream implementation decisions. So if you want to reduce friction in online checkout, that is a different problem than trying to optimize really great recommendations within someone's e-commerce experience online for retail. Those are two very different problems and you might approach them very differently. They might have completely different data sets, but

They might have completely different outcomes on your business. And so one of the things that we've done here at Google over time is we've tried to take our internal shorthand for innovation, approach to innovation and creativity, and we've tried to codify it so that we can be consistent in how we execute projects, especially the ones that venture into the murkiness of the future. And this framework, it really has three principles.

And the first one, as you might expect, is to focus on the user, which is really a way of saying, let's get after the problem, the pain that they care the most about. The second step is to think 10x, because we know if it's going to be worth the investment of all of these cross-functional teams' time and to create the data pipelines and to curate them and to test for potential bias within these pipelines and within data sets to build models.

And to test those models, that's a significant investment of time and expertise and attention. And so we want to make sure we're solving for a problem that also has the scale that will be worth it and really advances whatever we're trying to do, not in a small way, but in a really big way. And then the third one is rapidly prototyping. And you can't get to the rapid prototyping unless you've thought through the problem, you've constructed your environment so that you can conduct these experiments rapidly. And sometimes

We'll proxy outcomes just to see if we care about them at all without running them at full production. So that framework, that focusing on the user, thinking 10x, and then rapid prototyping is an approach that we use across Google, regardless of product domain. That's really insightful, especially the think 10x piece, which I think is really, really helpful. I really like that.

You're lobbying, I think, for a, I would call it, very strong exploration mindset towards your approach to artificial intelligence.

versus more of an incremental or let's do what we have better. Is that right for everybody? Do you think that that's idiosyncratic to Google? I guess almost everyone listening today is not going to be working at Google. Is that something that you think works in all kinds of places? That may be beyond what you can speak to, but how well do you think that that works across all organizations?

Well, I think there's a difference between a mindset and then the way that these principles manifest themselves. Machine learning just in its nature is exploration, right? It's approximations. And you're looking through the math and you're looking for the places where you're pretty confident that things have changed significantly for the better or for the worse so that you can do your feature engineering and you can understand the impact of choices that you're making.

In a lot of ways, the mathematical exploration is an analog to the human exploration. By the way, just because we have a great idea doesn't mean it gets funded at Google. Yes, we are a very large company. Yes, we're doing pretty well. But most of our big breakthroughs have not come from some top-down mandated gigantic project that everybody said was going to be successful.

Gmail was built by people who were told very early on that it would never succeed. We find that this is a very common path. Before Google, I've been an entrepreneur a couple of times, my own company and somebody else's. I've worked in other large companies that had world-class engineering teams as well. I can tell you this is a pattern, which is just giving people just enough freedom to

to think about what that future could look like. We have a way of describing 10X at Google you may have heard called moonshots. Well, our internal engineering team has also coined the term roof shots because the moonshots are often accomplished by a series of these roof shots. And

If people don't believe in the end state, the big transformation, they're usually much less likely to journey across those roof shots and to keep going when things get hard. And we don't flood people with resources and help at the beginning because it's, you know, this is hard for me to say as a senior executive leading, you know, technology innovation, but quite often I don't have perfect knowledge.

Of what will be the most impactful project that teams are working on, my job is to create an environment where people feel empowered, encouraged, and excited to try and to try to demotivate them as little as possible.

Because they'll find their way, you know, they'll find their way to the roof shot and then the next one and then the next one. And then pretty soon, you're three years in and I couldn't stop a project if I wanted to. It's going to happen because of that spirit, that Voyager spirit. Tell us a bit more about your role at Google Cloud. I think I have the best job in industry, which is I get to lead a collective of CTOs who have come from every industry,

and every geography and every kind of place in the stack from hardware engineering all the way up to SaaS, quantum security. And I get to lead this incredible team. And our mission is to create this bridge between our customers, our top customers and our top partners of Google who are trying to do incredible things with technology and the people who are building these foundational platforms at Google and to try to harmonize them. Because with the evolution of Google,

Now, especially with our cloud business, we have become a partner to many of the world's top organizations. And so, for example, if Major League Baseball wants to create a new immersive experience for you at home through a digital device, or eventually when we get back to it more into the stadiums, it's not just us creating technology, surfacing it to them, them telling us what they like about it, and then sending it back, and then we spin it.

It's actually collaborative innovation. So, you know, we have these approaches to machine learning that we think could be pretty interesting. You know, we have technologies in AR, VR, we have content delivery networks. We have all of these different platforms that we have at Google.

And in this exploratory mode, we get together with these large customers and they help guide not only the features, but they help us think about what we're going to build next. And then they end up depending, they layer on top of these foundational platforms, the experience that they want as Major League Baseball to us as baseball fans. And that intertwined collaborative technology development is at the heart and that collaborative innovation, that's at the heart

of what we do here in the CTO group. That's a great example. Can you say a bit more about how you set the strategy for projects like that? I'm very, very bullish about having the CTO and the CIO at the top table in an organization because the CIO often is involved in the technology that a company uses for itself, right? For its own innovation.

I've often found that the tools and the collaboration, the culture that you have internally manifests itself in the technology that you build for others. A CIO's perspective on how to collaborate the tools, how people are working together, how they could be working together is just as important as a CTO's view.

into what technology could be most impactful, most disruptive kind of coming from the outside in. But you also want them sitting next to the CMO. You want them sitting next to the chief revenue officer. You want them with the CEO and the CFO. And the reason is because it creates a tension. I would never advocate that all of my ideas are great. Some of them are, but some of them have panned out.

And it's really important that that unfiltered tension is created at the point at which corporate strategy is delivered. In fact, this is one of the things I learned a lot from working from a couple CEOs, both outside of Google and here, is that it's a shared responsibility. The responsibility of the CTO to put themselves in the room to add that value, and it's the responsibility of the CEO to pull it through the organization when the mode of operation may not be that way today.

Yeah, that's very true. And it corroborates our work, Sam, to a large extent that it's not just about building the layers of tech. It's about process change. It's about strategy alignment. And also, it's about ultimately what humans have to do differently.

and to work with AI, work with AI collaboratively. It's also about how managers and mid-managers and the folks that are using AI to be more productive, to be more precise, to be more innovative, more imaginative in their day-to-day work. Can you comment a bit on that in terms of how it could have changed the roles of individual employees, let's say in different roles, whether it's in marketing or in pricing or customer servicing? Any thoughts or ideas on that?

We had an exercise like this with a large retail customer, and it turned out that someone from outside of the organization, the physical security and monitoring organization, it turns out that one of the most disruptive and interesting and impactful

framings of that problem came from someone who was in a product team, totally unrelated to this area that just got invited to this workshop as a representative of their org. So, you know, we can't have everybody in every brainstorming session, despite, you know, the technology allows us to put a lot of people in one place at one time.

But choosing who is in those moments is absolutely critical. And just going to default roles or going to default responsibilities is one way to just keep the same information coming back again and again and again. That's certainly something we're thinking about at a humanities-based university, that blend and that role of people. It's interesting to me that in all your examples, you've talked about joining people and people from cross-functional teams together.

You've never mentioned a machine as one of these roles or a player. Is that too far-fetched? How are these combinations of humans going to add the combination of machine in here? We've got a lot of learning from machines at, I think, certainly at a task level. At what point does it get elevated to more strategic level?

Is that too far away? No, I don't think so. But certainly in its early days. And one of the ways you can see this manifest is natural language processing, for example. I remember one project we had, we were training a chat bot and it turned out we used raw logs, all privacy assured and everything. But we used these logs that a customer had provided because they wanted to see if we could build a better model

And it turns out that the chat agent wasn't exactly speaking the way we'd want another human being to speak to us. And why? Because people get pretty upset when they're talking to customer support and the language that they use isn't necessarily language I think we would use with each other on this podcast. And so...

We do think that machines will be able to offer some interesting kind of response inputs, generalized inputs at some point. But I can tell you right now, you want to be really careful about letting loose a natural language enabled partner that is a machine inside of your creativity and innovation session because you may not hear things that you like.

Well, it seems like there's a role here too that, I don't know, there's going to be bias in these things. This is inevitable and in some sense, I'm often happy to see biased decisions coming out of these AI and ML systems because then it's at least surfaced. We've got a lot of that unconsciously going on in our world right now. If one of the things that we're learning is that the machines are pointing out how ugly we're talking to chatbots or how poorly we're making other decisions,

And that may be a step one to improving overall. Yeah, the responsible AI push, it's never over. It's one of those things that ensuring those responsible and ethical practices require a focus across the entire activity chain. And two areas that we've seen as really impactful when you can focus on principles

as an organization. So what are the principles through which you will take your projects and shine the light on them and examine them and think about the ramifications? Because you can't a priori define all of the potential outputs that machine learning and AI may generate. That's where I refer to it's a journey. And I'm not sure that there is a final destination.

I think it's one that is a constant and kind of in the theme of a lot of what we talked about today is it's iterative. You think about how you want to approach it. You have principles, you have governance, and then you see what happens. And then you make the adjustments along the way. But not having that foundation means you're dealing with every single instance as its own unique instance.

And that becomes untenable at scale. Even small, you know, this isn't just a Google scale thing. This is a, any company that wants to distinguish itself with AI at any type of scale is going to bump into that. Well, we really appreciate you taking the time to talk with us today. It's been fabulous. We've learned so much. Really, really an insightful and candid conversation. Really appreciate it. Oh, absolutely. My pleasure. Thanks for having me.

Sam, I thought that was a really good conversation. We've been talking with Will Grannis, founder and leader of the Office of the CTO at Google Cloud.

Well, we may have lost some listeners saying that you don't need ML as item one on his checklist. But I think he had 71 other items on his checklist that I think do involve machine learning. But I thought he was making a really important point that don't get hung up on the technology and the feature functionality and think about the business problem and the impact and shoot really, really big things

for the impact. And then also don't think about you have to achieve the moonshot in one jump and that you could get there in progressive jumps, but you always have to keep your eye on the moon, which I think is really, really insightful.

That's a great way of putting it, because I do think we got focused on thinking about the 10x, and we maybe paid less attention to his number one, which was the user focus and the problem. The other thing I thought that is an important point is the point about collaboration. I

I think it's really an overused term because in every organization, every team would say, yes, yes, we're completely collaborative. Everybody's collaborating. They're keeping each other informed. But I think the true meaning of what Will was talking about is beyond that. You know, there's multiple meanings to collaboration. You could say as long as I'm keeping people informed or sending them documents that I'm collaborating. But what he said is there's not a single person on my team that can succeed on his or her own.

And that's a different kind of collaboration. It actually means you're so interlinked with the rest of your team that your own outcome and output is

depends on everybody else's work. So you can't succeed without them and they can't succeed without you. It's really beyond collaboration. It's like the team is an amalgam of all the people and they're all embedded in each other as just one substance. What's the chemical term for that? Yeah, see, I knew you were going to make a chemical reference there. There you go, amalgam. Amalgam or amalgam? I should know this as a chemical engineer. Exactly, we're not going to be tested on this part of the program. I hope my Caltech colleagues aren't listening to this.

Yeah, actually, the collaboration thing, it's easy to espouse collaboration. If you think about it, nobody we interview is going to say, all right, I really think people should not collaborate. I mean, just no one's going to take that. But what's different about what he said is they had process around it and they had sound like it's in structure and incentives so that people were incentivized to to align well. Mm hmm.

I like the gaming analog, the objective function in the game, whether it's adversarial or you're trying to beat a course or unleash some hidden prize somewhere, that there is some kind of a optimization or simulation or approximation or correlation going on in these games. And so the analog of that transformation

to a business problem resting so heavily on the very definition of the objective function. Yeah, I thought the twist that he said on games was important because he

he did pull out immediately that you know we can think about these as games what have we learned from games we've learned from games that we need we need an objective we need a structure we need to define the problem and he tied that really well into you know the transition from what we think of is super well defined games of perfect information to unstructured it still needs that problem definition i thought that was a good switch that's right who brought out the importance of having good data for ml to work

He also highlighted how Google Cloud collaborates both internally and with external customers. Next time, we'll talk with Amit Shah, president of 1-800-Flowers, about the unique collaboration challenges that it uses AI to address through its platform. Please join us next time. Thanks for listening to Me, Myself, and AI. If you're enjoying the show, take a minute to write us a review.

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