Shervin: 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: 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: 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.
(upbeat music)
Sam: 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: Yeah, great to be here.
Thanks for having me.
Sam: 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: 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.
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, Codeacademy,
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: Nothing particular 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 a 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: 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, or every financial services organization
asked 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, that 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're 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 machine learning
now for a couple decades, 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: 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?
Will: Well, 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, 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, or 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 professionals
who are trying to answer a difficult question
in a short period of time for the head of the line
of business.
And unless you have that level of data sophistication,
machine learning will probably be out of reach
for the foreseeable future.
Shervin: Yeah, Will, 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.
Now, 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.
Will: 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 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,
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 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 it is going to be worth the investment
of all of these cross-functional teams' time to create
the data pipelines and to curate them and to test
for potential bias within these pipelines
or 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.
Shervin: That's really insightful, especially that
think 10x piece, which I think is really, really helpful.
I really like that.
Sam: You're lobbying I think, for what we 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 ...
Is that idiosyncratic to Google?
And 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?
Will: 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.
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.
And in a lot of ways, the mathematical exploration
is an analog to the human exploration.
It's that we try to encourage people.
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 they would never succeed.
And we find that this is a very common path.
And before Google, I've been an entrepreneur
a couple of times in my own company and somebody else's.
And I've worked in other large companies
that had world-class engineering teams as well.
And I can tell you, this is a pattern, which is giving
people just enough freedom 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 roofshots, because the moonshots are often
accomplished by a series of these roofshots.
And if people don't believe in the end state,
the big transformation, they're usually much less likely
to journey across those roofshots, and to keep going
when things get hard.
And we don't flood people with resources and help
at the beginning, because...
This is hard for me to say
as a senior executive leading 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.
They'll find their way to the roofshot,
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.
Shervin: Tell us a bit more about your role at
Google Cloud.
Will: I think I have the best job in the 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 SAS,
quantum security.
And I get to lead this incredible team.
And our mission is to create this bridge
between our customers, our top customers, 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 we have these approaches to machine learning
that we think could be pretty interesting.
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,
or we get together 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 is 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.
Shervin: That's a great example, can you say a bit more
about how you set the strategy for projects like that?
Will: 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, for its own innovation.
And 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.
So the 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 the 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 to the point at which corporate strategy
is delivered.
In fact, this is one of the things I learned a lot
from working for 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.
Shervin: 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 and work with AI collaboratively.
It's also about how managers and middle 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?
Will: We had an exercise like this with a large
retail customer. And it turned out that someone from outside
of the organization, the kind of physical security
and kind of 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, we can't have everybody in every brainstorming session,
despite that 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.
Sam: 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, 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.
And I think, certainly the task level,
what point does it get elevated to more a strategic level?
Is that too far away?
Will: 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 chatbot.
And it turned out we used raw logs, all privacy assured
and everything, but we used these logs the 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.
Sam: Well, it seems like there's a role here too,
that I don't know, these machines, there's going to be bias
in these things.
This is inevitable.
And in some sense, I'm often happy to see bias 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.
And 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.
Will: Yeah, the responsible AI push, it's never over.
It's one of those things that ensuring those responsible
in 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 why I referred 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 smaller, this isn't just a Google scale thing.
This is any company that wants to distinguish itself,
with AI, at any type of scale
is going to bump into that.
Sam: Well, we really appreciate you taking the time
to talk with us today.
It's been fabulous.
We've learned so much.
Shervin: Really, really an insightful and
candid conversation.
We really appreciate it.
Will: Oh, absolutely my pleasure.
Thanks for having me.
(upbeat music)
Shervin: 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.
Sam: 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.
Shervin: 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 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'll always have to keep your eye on the moon,
which I think is really really insightful.
Sam: 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 on the problem.
Shervin: The other thing I thought that is an important
point is the point about collaboration.
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.
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
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?
Sam: (laughing) Yes, I knew you were going to make a
chemical reference there.
There we go, amalgam.
Shervin: "Ah-mal-gum" or "am-all-gam"?
I should know this as a chemical reference.
Sam: Exactly, we're not going to be tested on.
Shervin: I hope my Caltech colleagues aren't
listening to this.
(laughing)
Sam: 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 they had process
around it.
And they had what sounded like instructure and incentives
so that people were incentivized to align well.
Shervin: I like the gaming analog.
The objective function in the game,
whether it's adversarial, or you're trying to beat or coerce
or unleash some hidden prize somewhere,
that there is some kind of an optimization or simulation
or approximation or correlation going on in these games.
And so the analog of that to a business problem
resting so heavily on the very definition
of the objective function.
Sam: Yeah, I thought the twist that he said on games
was important because he did pull out immediately
that we can think about these as games.
What do we learn from games?
We've learned from games that we need an objective,
we need the structure, we need to define the problem.
And he tied that really well into the transition
from what we think of as super well-defined games
of perfect information to unstructured.
It still needs that problem definition.
I thought that was a good switch.
Shervin: That's right.
(upbeat music)
Sam: Will 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 he uses AI to address to his platform.
Please join us next time.
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(upbeat music)