SAM RANSBOTHAM: What's in a name?
Today we talk with Gerri Martin-Flickinger,
former chief technology officer at Starbucks,
about how the names we use can make a big difference
in innovation and motivation.
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 Ideas program at MIT Sloan Management Review.
SHERVIN KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I colead BCG's AI practice
in North America.
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 and really transform
the way organizations operate.
SAM RANSBOTHAM: Today we're talking
with Gerri Martin-Flickinger, former executive vice
president and chief technology officer at Starbucks.
Gerri, thanks for taking the time to talk with us.
Welcome.
SHERVIN KHODABANDEH: It's really great to have you here, Gerri.
GERRI MARTIN-FLICKINGER: It's great to be here.
Thanks for having me.
SHERVIN KHODABANDEH: So, Gerri, tell us a little bit about
yourself --
your background, your journey to this point,
and what it's been like.
GERRI MARTIN-FLICKINGER: This topic today
is going to be about AI, so I would really
love to go way back in time to the 1980s.
I know you probably didn't expect me to start there when
you asked me that question, but I have to start there,
because I went to Washington State University -- go Cougs!
-- and I had an emphasis in artificial intelligence way
back in the day and, in fact, did my senior project with
a neural net that was built in Lisp,
which nobody even remembers what that is anymore.
But the reason why I start there is
because I have had a love for AI and the potential of AI
techniques for all of this time.
And it's been really exciting in the last five or six years
to see that we're finally at a place where technology,
both compute and storage -- and data --
have gotten to the point where we can actually start
to achieve some of those visions we had way back then.
But let me start in '85.
After going to school and getting
a degree in computer science, I went to Chevron,
the oil/energy company.
[I] spent my early years there and did some really cool AI
work while I was there, which was really more in a research
mode, and did some technology that actually
was sold and purchased by some other companies
outside of Chevron.
After that, I became the first CIO for McAfee
in the late '90s, when McAfee was still quite small
but grew quite large.
And that was really fun, because if you think about antivirus
in those early days, it was one of the first SaaS companies,
because you would buy your antivirus software,
but then you would get these payloads every month
that would be the new virus signatures to keep
protecting your machine.
So, in effect, it was like a subscription business.
Being part of that in the early days of,
frankly, the internet and right during the whole dot-com period
was super insightful and really taught me
a lot about scale and consumer digital
before we called it consumer digital.
I then took a little bit of time out of my career
to have two beautiful twin daughters,
and then I came back as the CIO for Verisign, another security
company, and then became the CIO for Adobe
and was at Adobe for about 10 years and part of the team
who migrated their product offerings into the cloud,
into a true subscription SaaS business.
And then [I] joined Starbucks six years ago,
a little over six years ago, as their first chief technology
officer and really helped them migrate and move into a more
modern architecture stack; evolve their entire digital
platform, including mobile order and pay;
[and] innovate IoT into all of the stores -- so really,
just a whole lot of really fun at-scale technology
and, in the process, got back to my roots and did some AI
at the same time.
SHERVIN KHODABANDEH: This is a phenomenal series
of things you've been part of.
And I'm curious, as someone whose interest and exposure
and practicing of AI goes back to the '80s, as you said,
and you've seen the various eras of technological
and organizational innovations, and the playing field has
continuously increased -- what do you think are some
of the biggest misconceptions that still exist in the minds
of executives, particularly when it comes to this topic?
GERRI MARTIN-FLICKINGER: That is a great question.
One of them is that if you build a model, it can solve anything.
There's no such thing as a generalized AI model that will
solve anything or everything.
That just isn't possible.
If you think about where people are having the most
success right now with AI at scale, a lot of it
is tightly coupled to statistical analysis, frankly,
and is a lot about taking very large learning sets
and building very sophisticated models that
can be predictive in nature.
And that's awesome, and what that takes
is a lot of data to make it accurate.
And so I think one of the misconceptions
that I've certainly talked to a lot of executives
about in the last few years is that in order
to do AI in a really meaningful way,
you've got to get your data in order.
It isn't as simple as just saying, "Hey, we
have loads of data!
We should be able to have amazing AI models."
You've got to have a little bit of structure to that data.
You have to have a little bit of thought about where
that data sits, even something as simple as,
what does your data lake look like?
Where are you putting that data?
What is the currency of that data?
Do you need the models to retrain in real time?
Do you need to build them once and then
retrain them once a quarter?
And all of that starts to get really
wrapped up into your data architecture.
So I think the one thing that I always
ask people when they want to talk about AI
is, "Tell me about your data.
Do you have data?
Where is your data?
Do you own it?
Can you use it?
Do you have the right to use it?"
This is the other thing about consumer data
that's so very important: You have
to make sure you're not doing things
that you shouldn't be doing with that data.
So I really think that one of the misconceptions out there
is this idea that there's this thing you buy called AI,
and you plug it in and it all works.
That is almost the end of a long series of things you need to do
and architect.
SAM RANSBOTHAM: OK, so, once an organization has their data
in place, what happens next?
What does having that data enable?
GERRI MARTIN-FLICKINGER: I do think the evolution of AI
and ML specifically has led all parts of businesses
that are sophisticated to ask very
different questions than they did even five years ago.
Suddenly, there's an expectation of,
"No, we should be able to predict supply chain
forecast based on X, Y, and Z."
And that isn't going to take somebody
to build a giant spreadsheet to do it.
There's a better way to do that now.
And I think you're seeing that evolution.
What I think I would compare this to, though,
is do we all remember the days before we all had Excel?
It's hard to remember those days, but --
SAM RANSBOTHAM: VisiCalc?
GERRI MARTIN-FLICKINGER: Yeah, VisiCalc!
I remember VisiCalc.
But there was a time --
SHERVIN KHODABANDEH: Lotus 1-2-3.
GERRI MARTIN-FLICKINGER: There was
a time when the idea that all of us in business
would be able to build a spreadsheet
and then share it and collaborate on it.
That was like, "What?
Why would you "
SAM RANSBOTHAM: Crazy talk.
GERRI MARTIN-FLICKINGER: Crazy talk, right?
Same thing when I first started working;
there was a typing pool.
We didn't have email; you sent memos.
My point being that as tools become
more available to more people, more people
are able to explore more ideas.
How many times do all of us open spreadsheets all week
long to do everything from help our kids with homework
to track our personal finances, or just keep a list?
The best list thing I have is a spreadsheet.
And suddenly, that has changed how we all work.
So I just go back to these fundamental shifts
we've seen in the past, like the spreadsheet,
and I don't think this is so very different.
I think as we continue to watch this evolve,
we're going to find better tools and more effective
ways that we can all explore these techniques.
I'll use a couple of examples from Starbucks:
doing models for labor scheduling.
That's not a leap to think about; certainly,
[it's] something that you can do with the kind of data
that you have today.
Now, do you think a store manager knows or cares
that when they build a labor schedule for their store,
there's actually a reasonably sophisticated ML model
behind the scenes doing that?
No.
So I think the amount of embedded AI and ML
that we all We all probably have some in our cars right now;
a lot, probably.
Your power companies, your phone companies
It's embedded everywhere.
Your credit card companies have had it probably longer than you
even realize, for fraud detection, so it's already
pretty pervasive.
And I think the question is, how much more accessible
could it be as people become more sophisticated?
I don't know.
I have kids in high school.
Kids in high school talk about data science now;
it's classes in high school, so there's
nothing to think that in five, six, seven years, when they
come out of college, they're going to be probably pretty
fluent in some of these techniques,
even if it seems completely inconceivable to all of us.
SHERVIN KHODABANDEH: No, I think it actually reminds me
of a Wall Street [Journal] article I was reading today
about chess.
Chess grandmasters have had to become experts, somewhat,
in AI, because everybody's using it.
And they have these AI teams to be
able to handicap different lines of thinking of the algorithm
to throw off their opponents, because everybody's using
AI to plan and win their games.
And so you need to really understand
how the engine works if you're going to beat somebody who's
using that engine to beat you.
And it builds on the point you were making on,
it is quite pervasive.
SAM RANSBOTHAM: In your analogy, I guess,
that would be "Out-schedule the competition."
You mentioned Starbucks.
Is there something particular that you're excited about
that you want to showcase?
GERRI MARTIN-FLICKINGER: I can certainly
highlight a couple of examples.
We have a moniker, Deep Brew, which
stands for a broad section of AI projects
underway across the company.
The ones that folks are most familiar with
are personalization models.
Whether it's personalization on the mobile app
or personalization when you pull into a drive-through
where they have a digital display,
those experiences are being driven
from models that are based on lots of different inputs, some
of which are very personal, like maybe your own buying patterns.
Some of them are regional, like, "What's
going on with buying patterns in this region?"
They could be environmental factors, like, "What's
the weather today?"
It could have to do with supply chain loads, like, "What do we
actually have in stock that we need to sell?"
Those are actually much harder to do than they sound.
It sounds very simple.
But let me give you an example to illustrate why some of this
is hard and why I always start with data.
Doesn't it sound easy to figure out
if there are the ingredients for a latte
so that you can promote a latte on the phone?
SAM RANSBOTHAM: Naively, I'll say yes.
GERRI MARTIN-FLICKINGER: It sounds
like it's a thing, right?
Like, "Yeah, we have lattes."
Well, actually, lattes are manufactured
in the moment at a store, and they're
made of component parts.
They're made of some espresso, which
could be different kinds of espresso, made
with some type of a milk product,
which could be cow milk, it could
be an alt [nondairy] milk.
And then it could be heated to different temperatures
based on what the customer has asked for.
And that's a simple drink.
That is the simplest espresso drink, probably,
you can get in a store.
So here's why that's complicated.
If you're in the store, the customer
just knows it as a latte.
But if you think about the entire supply
chain of all of the component parts
that have to be available at that moment in the front
of the house, behind the counter, to make that latte,
that's a whole different problem.
And now you've got to get all the way back to your data
master, the data master that is the component parts,
and understanding if they were delivered that night
in the back of the store.
Now you've got a data problem that
requires you to decompose and restructure
the data all the way back to the origin, if you haven't already
done that.
And I'm only illustrating this because so often you think,
"Well, it's an easy ML problem to say,
'We want to promote lattes.'" But the second you do that,
you actually have to know the deepest level of data possible
to ensure you actually have the product to sell.
SAM RANSBOTHAM: That's tricky because, actually,
when you say "latte," I know exactly what you mean,
because you mean exactly the one that I would drink.
You don't mean the one that Shervin would drink.
GERRI MARTIN-FLICKINGER: Right.
SAM RANSBOTHAM: And to answer the question,
you've got to answer it for every single person.
GERRI MARTIN-FLICKINGER: Right.
And you're not going to enumerate all those.
There's infinite possibilities for customization.
Infinite.
So you can't do that.
You have to actually work at it as a data problem.
And then you can do the AI model on top of it,
because you've actually figured out what you have to work with.
SHERVIN KHODABANDEH: And then you
also talked about the supply chain issue and the inventory
management.
And the point to me is, these use cases are not in silos
anymore.
GERRI MARTIN-FLICKINGER: Exactly.
SHERVIN KHODABANDEH: The whole foundational data, of course,
is critical to power them.
But how we market impacts what happens in the store,
and supply chain issues impact what we should be able
to market or shouldn't.
And so --
GERRI MARTIN-FLICKINGER: Totally.
SHERVIN KHODABANDEH: My follow-up is, for the business
leaders who are listening to this,
I think there are many analogs of what you just described
that would resonate in any line of business,
because you've got these groups that
are different lines of business or different functional
components that, in today's world with today's data,
are much more interactive, and there's
a network effect of all of these things, which
requires teams to come together that normally wouldn't
work together.
What advice do you have for the CEO or the president
of a business unit to break these silos, because you've
got different teams with different tools,
different incentives, right?
That must be a daunting organizational problem.
It's not just a technology problem.
And you've seen that work well.
I'm just curious -- what advice would you have?
GERRI MARTIN-FLICKINGER: Well, I don't
think there's any magic here.
I think, as in most things in business,
you have to start by being really clear on,
what is the objective?
What are you solving for?
That sounds so simple, but sometimes that's
really hard to figure out.
Is what you're solving for increased revenue?
Is it increased customer retention?
Is it improved margin?
Is it something else?
Getting really clear on that is part of what gets all
the constituencies to go at loggerheads --
somebody carries the hat of revenue,
and somebody else carries the hat of margin,
and somebody else carries customer experience.
You've got to get clear on what you're solving for.
And you can't solve for all of it at the same time.
Now, you can benefit it all, but you
have to get really clear on "What are we going after?"
So that's my first advice: [to] be really clear on what problem
you're trying to solve.
I think one thing I believe a lot in bringing people together
who have different expertise.
I actually think it's a good thing.
It's a good thing to bring people together
who have five different specialties, because they're
going to bring the very best thinking for that domain.
But then you also have to have them
feel like they're in it together,
and that's good, old-fashioned teamwork.
And I hate to say "good, old-fashioned teamwork,"
but for as long as I've been in the business world,
it all comes down to the same things: Are you getting people
together with a common vision?
Are you giving them room to fail so that they
can get onto a path to success?
Are you giving them a goal that's
really clear, with a timeline that's achievable but also
really clear?
And then are you supporting them with the resources
and the budget that they need to be successful?
It's all that same stuff.
There's nothing new there.
I do think where a lot of people fail
is, they don't start off with a clear problem they're
trying to solve.
And that tends to get people to all get really entrenched
in their silos and then go off and try
to solve their own problem.
SHERVIN KHODABANDEH: That's very well said.
SAM RANSBOTHAM: I'm struck as I'm
listening about how much depth you obviously
have in making a latte, but that wasn't your background;
we didn't hear that stop [in your career journey].
How do you get people to know so much about the domain area
to then be able to solve it with the technology
that you're using to solve it?
It seems like a very difficult thing to pull together?
GERRI MARTIN-FLICKINGER: I don't know
which question to answer: the one about how did I end up
learning to make a latte at Starbucks
or, in general, how do you do that in business?
SHERVIN KHODABANDEH: Let's start there.
GERRI MARTIN-FLICKINGER: OK, we can start with my journey.
So, yeah, I came out of technology.
I had been in enterprise software for many, many years
in Silicon Valley.
And my decision to come to Starbucks
was kind of interesting.
I was, first of all, just intrigued.
I was intrigued by the scale.
And the scale is interesting when you think about Starbucks,
because today there's over 30,000 stores around the world.
There's over 300,000 baristas around the world.
And why it's an interesting scale problem
is not just the number of customers that visit Starbucks.
But if you think about those 30,000-some stores,
each one is like a little business unto itself.
When you're in enterprise software,
you might have a hundred offices around the world.
You might have a ton of people, but they're
in these big offices with big pipes
and lots of infrastructure, and you have a support team there.
When you've got a store in the middle of Oklahoma
on a dial-up line, that's a whole different thing
to manage.
And to have the same expectation of quality for a customer who's
got their mobile order-and-pay application, that's
just a whole different game.
And I was really intrigued by, of course, IoT
and how much more could be done in brick-and-mortar [retail]
with IoT devices.
I was intrigued by how quickly I saw consumer digital growing.
And so all those things are what made me come to Starbucks
and be part of that transformation journey.
How do you learn when you know nothing
about food and beverage?
First thing you do is you spend time in the stores, you know?
I spent my first few weeks in a store learning
about how people make lattes.
Now, I cannot claim to be a barista by any stretch at all,
so when I was in the store, I was mostly helping clean or I
was greeting customers, or I was trying to do things I could
actually do.
But in the process, you learn a lot about what goes
on in a store -- and not just the really cool stuff that you
see, like making the lattes or greeting the customers,
but what goes on in the back of house.
How do they receive inventory?
Oh my gosh.
How do they do payroll?
How do they have to do labor scheduling?
What does that look like?
And that's an eye-opener.
And I would say, forget what business you're in.
Whatever business you're in, if you're a technologist, if you
are not sitting shoulder [to] shoulder with whoever
is the tip of the spear of the business,
you're missing an opportunity.
You've got to do that.
So when I was in enterprise software,
I spent a lot of time going out with salespeople
to visit customers.
I just wanted to see, what are the customers thinking?
Do they love us?
Do they hate us?
What problems are they having?
Spending time with the customer support
center to just sit down and listen, occasionally,
to the calls they were getting: What is the world
thinking about us, and how are people who are depending
on our software feeling?
SHERVIN KHODABANDEH: Did you spend time on the oil rig
when you were at Chevron?
GERRI MARTIN-FLICKINGER: I did spend time at refineries.
Yeah, a little bit of time at refineries.
SHERVIN KHODABANDEH: That's great.
SAM RANSBOTHAM: Actually, it reminds me of Prakhar Mehrotra
and Walmart.
He went out there and -- this was one of our earlier
interviews -- said a very similar thing about how you
understand how to automate or how to put technology
into these situations.
And it was very much echoing the kinds of things you're saying
-- [it] can't be done in isolation.
GERRI MARTIN-FLICKINGER: That's right.
And I do think there's a couple of things that, in my playbook,
have continued to pay off over and over again.
And they're just simple, simple things.
The first is, words matter.
Words matter.
They really do.
When I say to you -- and I'm going to ask both of you
to answer back at me; I'm going to ask you a question now:
When I say the word "IT", what do you think of?
SHERVIN KHODABANDEH: I think of email servers and software
patches and things like that.
GERRI MARTIN-FLICKINGER: Sam, what about you?
SAM RANSBOTHAM: I think I'm biased.
I thought more of a strategy-oriented,
how you're enabling connectivity within the organization --
GERRI MARTIN-FLICKINGER: We're not using your answer.
No, no, no.
SAM RANSBOTHAM: OK.
I'm an IT prof, so that's maybe my bias there.
Do you think most people go, "Operations"?
GERRI MARTIN-FLICKINGER: When I say IT,
most people will talk about the help desk;
they'll talk about outages.
SAM RANSBOTHAM: Trouble tickets.
GERRI MARTIN-FLICKINGER: They'll talk about services,
trouble tickets, data centers.
Right?
OK.
When I say technology, people say, "The future.
Innovation.
New things."
Right?
So if you're in a business, and someone
introduces someone who's in the IT department,
they have one reaction.
If I introduce someone to you and I say, "This is" --
in the case of Starbucks --
"Starbucks Technology," which one sounds and feels
more future-leaning?
SAM RANSBOTHAM: Actually, that's a huge difference.
GERRI MARTIN-FLICKINGER: It's a huge difference.
SAM RANSBOTHAM: I definitely see the difference there.
GERRI MARTIN-FLICKINGER: Right.
Which is why I said words really matter.
And so we were talking about transformation
and how do you transform a technology
organization to the future.
And so, one thing that's one of these tried-and-true things
is, did you name your organization
in a way that makes the organization proud, so
that every single person in the organization
sits up a little straighter and maybe works a little harder?
Have you named the organization in a way that really represents
what you want it to become?
And have you named it in a way that everyone else
in the business looks at it and goes, "Oh, that's
something a little different"?
OK.
I know a long answer to one of the things
that I think is really important in transformation
is to signal that you're doing it.
And so, for example, with Starbucks,
when I joined, 90 days after I joined,
[I] changed the name of IT to Starbucks Technology.
Never used the word IT again.
And if I was ever in a meeting where somebody said IT,
I'd stop the meeting and I'd say, "We don't have IT.
We have Starbucks Technology," and it's kind of funny,
because that one change made a big difference.
The next thing that I think can make a difference
is, you need a tagline.
I hate to say it, but everybody in business,
everyone who's a CEO, knows it.
You've got to tell your story, and you don't get five hours
to tell your story.
You get six to 10 words, and you'd better
get people curious to ask more.
And so, put a tagline in place.
Super simple: "Talented technologists delivering today,
leading into the future.
Starbucks Technology."
That's it.
And that simple phrase, which is used today still,
after six years, just continues to reinforce the value
of the organization, the value of the people,
the importance of getting the work done --
as well as continuing to build for the future.
And so, for me, transformation comes down to people.
And to do any transformation with tech
has nothing to do with the tech as
much as it has to do with the people who
are making it happen.
They have to feel inspired, they have
to feel what they're doing is important,
and they have to feel like they have room to be part
of the invention of the future.
And I think that's all we have to do as leaders,
is make room for that.
SAM RANSBOTHAM: Gerri, it was great talking with you.
[You have] such a vast experience and a great ability
to connect those experiences together
to give us a holistic view of what's happening
and what may happen in the future.
Thank you for taking the time.
SHERVIN KHODABANDEH: It's been really wonderful.
Thank you.
GERRI MARTIN-FLICKINGER: It's been fun.
Thanks so much.
SAM RANSBOTHAM: Next time, Shervin and I
talk with Barbara Martin Coppola,
the chief digital officer for IKEA Retail.
Join us as we hear what Barbara thinks
about the meaning behind the words
we use when we talk about artificial intelligence.
ALLISON RYDER: Thanks for listening
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