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SAM RANSBOTHAM:
What's in a name?

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Today we talk with
Gerri Martin-Flickinger,

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former chief technology
officer at Starbucks,

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about how the names we use
can make a big difference

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in innovation and motivation.

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Welcome to Me, Myself,
and AI, a podcast

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on artificial
intelligence in business.

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Each episode, we introduce you
to someone innovating with AI.

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I'm Sam Ransbotham, professor
of information systems

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at Boston College.

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I'm also the guest editor for
the AI and Business Strategy

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Big Ideas program at MIT
Sloan Management Review.

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SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

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senior partner with BCG, and
I colead BCG's AI practice

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in North America.

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Together, MIT SMR
and BCG have been

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researching AI for five
years, interviewing hundreds

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of practitioners and
surveying thousands

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of companies on what it takes
to build and to deploy and scale

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AI capabilities and
really transform

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the way organizations operate.

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SAM RANSBOTHAM:
Today we're talking

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with Gerri Martin-Flickinger,
former executive vice

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president and chief technology
officer at Starbucks.

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Gerri, thanks for taking
the time to talk with us.

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Welcome.

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SHERVIN KHODABANDEH: It's really
great to have you here, Gerri.

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GERRI MARTIN-FLICKINGER:
It's great to be here.

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Thanks for having me.

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SHERVIN KHODABANDEH: So, Gerri,
tell us a little bit about

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yourself --

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your background, your
journey to this point,

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and what it's been like.

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GERRI MARTIN-FLICKINGER:
This topic today

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is going to be about
AI, so I would really

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love to go way back
in time to the 1980s.

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I know you probably didn't
expect me to start there when

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you asked me that question,
but I have to start there,

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because I went to Washington
State University -- go Cougs!

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-- and I had an emphasis in
artificial intelligence way

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back in the day and, in fact,
did my senior project with

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a neural net that
was built in Lisp,

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which nobody even remembers
what that is anymore.

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But the reason why
I start there is

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because I have had a love for
AI and the potential of AI

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techniques for all of this time.

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And it's been really exciting
in the last five or six years

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to see that we're finally
at a place where technology,

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both compute and
storage -- and data --

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have gotten to the point
where we can actually start

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to achieve some of those
visions we had way back then.

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But let me start in '85.

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After going to
school and getting

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a degree in computer
science, I went to Chevron,

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the oil/energy company.

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[I] spent my early years there
and did some really cool AI

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work while I was there, which
was really more in a research

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mode, and did some
technology that actually

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was sold and purchased
by some other companies

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outside of Chevron.

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After that, I became
the first CIO for McAfee

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in the late '90s, when
McAfee was still quite small

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but grew quite large.

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And that was really fun, because
if you think about antivirus

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in those early days, it was one
of the first SaaS companies,

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because you would buy
your antivirus software,

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but then you would get
these payloads every month

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that would be the new
virus signatures to keep

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protecting your machine.

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So, in effect, it was like
a subscription business.

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Being part of that
in the early days of,

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frankly, the internet and right
during the whole dot-com period

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was super insightful
and really taught me

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a lot about scale
and consumer digital

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before we called it
consumer digital.

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I then took a little bit
of time out of my career

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to have two beautiful
twin daughters,

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and then I came back as the CIO
for Verisign, another security

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company, and then
became the CIO for Adobe

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and was at Adobe for about
10 years and part of the team

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who migrated their product
offerings into the cloud,

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into a true subscription
SaaS business.

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And then [I] joined
Starbucks six years ago,

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a little over six years ago,
as their first chief technology

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officer and really helped them
migrate and move into a more

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modern architecture stack;
evolve their entire digital

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platform, including
mobile order and pay;

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[and] innovate IoT into all
of the stores -- so really,

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just a whole lot of really
fun at-scale technology

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and, in the process, got back
to my roots and did some AI

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at the same time.

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SHERVIN KHODABANDEH: This
is a phenomenal series

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of things you've been part of.

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And I'm curious, as someone
whose interest and exposure

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and practicing of AI goes
back to the '80s, as you said,

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and you've seen the various
eras of technological

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and organizational innovations,
and the playing field has

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continuously increased --
what do you think are some

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of the biggest misconceptions
that still exist in the minds

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of executives, particularly
when it comes to this topic?

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GERRI MARTIN-FLICKINGER:
That is a great question.

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One of them is that if you build
a model, it can solve anything.

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There's no such thing as a
generalized AI model that will

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solve anything or everything.

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That just isn't possible.

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If you think about where
people are having the most

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success right now with
AI at scale, a lot of it

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is tightly coupled to
statistical analysis, frankly,

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and is a lot about taking
very large learning sets

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and building very
sophisticated models that

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can be predictive in nature.

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And that's awesome,
and what that takes

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is a lot of data to
make it accurate.

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And so I think one
of the misconceptions

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that I've certainly talked
to a lot of executives

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about in the last few
years is that in order

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to do AI in a really
meaningful way,

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you've got to get
your data in order.

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It isn't as simple as
just saying, "Hey, we

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have loads of data!

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We should be able to
have amazing AI models."

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You've got to have a little
bit of structure to that data.

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You have to have a little
bit of thought about where

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that data sits, even
something as simple as,

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what does your data
lake look like?

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Where are you putting that data?

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What is the currency
of that data?

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Do you need the models
to retrain in real time?

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Do you need to build
them once and then

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retrain them once a quarter?

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And all of that
starts to get really

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wrapped up into your
data architecture.

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So I think the one
thing that I always

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ask people when they
want to talk about AI

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is, "Tell me about your data.

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Do you have data?

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Where is your data?

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Do you own it?

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Can you use it?

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Do you have the
right to use it?"

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This is the other thing
about consumer data

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that's so very
important: You have

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to make sure you're
not doing things

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that you shouldn't be
doing with that data.

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So I really think that one of
the misconceptions out there

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is this idea that there's
this thing you buy called AI,

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and you plug it in
and it all works.

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That is almost the end of a long
series of things you need to do

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and architect.

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SAM RANSBOTHAM: OK, so, once
an organization has their data

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in place, what happens next?

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What does having
that data enable?

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GERRI MARTIN-FLICKINGER: I
do think the evolution of AI

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and ML specifically has
led all parts of businesses

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that are sophisticated
to ask very

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different questions than
they did even five years ago.

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Suddenly, there's
an expectation of,

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"No, we should be able
to predict supply chain

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forecast based on X, Y, and Z."

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And that isn't going
to take somebody

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to build a giant
spreadsheet to do it.

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There's a better
way to do that now.

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And I think you're
seeing that evolution.

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What I think I would
compare this to, though,

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is do we all remember the
days before we all had Excel?

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It's hard to remember
those days, but --

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SAM RANSBOTHAM: VisiCalc?

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GERRI MARTIN-FLICKINGER:
Yeah, VisiCalc!

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I remember VisiCalc.

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But there was a time --

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SHERVIN KHODABANDEH:
Lotus 1-2-3.

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GERRI MARTIN-FLICKINGER:
There was

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a time when the idea that
all of us in business

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would be able to
build a spreadsheet

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and then share it and
collaborate on it.

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That was like, "What?

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Why would you "

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SAM RANSBOTHAM: Crazy talk.

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GERRI MARTIN-FLICKINGER:
Crazy talk, right?

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Same thing when I
first started working;

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there was a typing pool.

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We didn't have email;
you sent memos.

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My point being that
as tools become

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more available to more
people, more people

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are able to explore more ideas.

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How many times do all of us
open spreadsheets all week

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long to do everything from
help our kids with homework

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to track our personal
finances, or just keep a list?

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The best list thing I
have is a spreadsheet.

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And suddenly, that has
changed how we all work.

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So I just go back to
these fundamental shifts

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we've seen in the past,
like the spreadsheet,

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and I don't think this
is so very different.

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I think as we continue
to watch this evolve,

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we're going to find better
tools and more effective

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ways that we can all
explore these techniques.

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I'll use a couple of
examples from Starbucks:

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doing models for
labor scheduling.

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That's not a leap to
think about; certainly,

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[it's] something that you
can do with the kind of data

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that you have today.

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Now, do you think a store
manager knows or cares

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that when they build a labor
schedule for their store,

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there's actually a reasonably
sophisticated ML model

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behind the scenes doing that?

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No.

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So I think the amount
of embedded AI and ML

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that we all We all probably
have some in our cars right now;

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a lot, probably.

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Your power companies,
your phone companies

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It's embedded everywhere.

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Your credit card companies have
had it probably longer than you

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even realize, for fraud
detection, so it's already

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pretty pervasive.

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And I think the question
is, how much more accessible

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could it be as people
become more sophisticated?

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I don't know.

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I have kids in high school.

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Kids in high school talk
about data science now;

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it's classes in high
school, so there's

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nothing to think that in five,
six, seven years, when they

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come out of college, they're
going to be probably pretty

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fluent in some of
these techniques,

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even if it seems completely
inconceivable to all of us.

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SHERVIN KHODABANDEH: No, I
think it actually reminds me

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of a Wall Street [Journal]
article I was reading today

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about chess.

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Chess grandmasters have had
to become experts, somewhat,

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in AI, because
everybody's using it.

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And they have these
AI teams to be

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able to handicap different lines
of thinking of the algorithm

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to throw off their opponents,
because everybody's using

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AI to plan and win their games.

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And so you need to
really understand

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how the engine works if you're
going to beat somebody who's

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using that engine to beat you.

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And it builds on the
point you were making on,

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it is quite pervasive.

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SAM RANSBOTHAM: In
your analogy, I guess,

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00:10:52.650 --> 00:10:54.650
that would be "Out-schedule
the competition."

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You mentioned Starbucks.

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00:10:55.750 --> 00:10:58.417
Is there something particular
that you're excited about

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00:10:58.417 --> 00:10:59.500
that you want to showcase?

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00:10:59.500 --> 00:11:01.750
GERRI MARTIN-FLICKINGER:
I can certainly

251
00:11:01.750 --> 00:11:04.140
highlight a couple of examples.

252
00:11:04.140 --> 00:11:07.350
We have a moniker,
Deep Brew, which

253
00:11:07.350 --> 00:11:11.650
stands for a broad
section of AI projects

254
00:11:11.650 --> 00:11:13.270
underway across the company.

255
00:11:13.270 --> 00:11:15.800
The ones that folks
are most familiar with

256
00:11:15.800 --> 00:11:18.410
are personalization models.

257
00:11:18.410 --> 00:11:21.330
Whether it's personalization
on the mobile app

258
00:11:21.330 --> 00:11:24.800
or personalization when you
pull into a drive-through

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00:11:24.800 --> 00:11:26.810
where they have a
digital display,

260
00:11:26.810 --> 00:11:31.390
those experiences
are being driven

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00:11:31.390 --> 00:11:35.620
from models that are based on
lots of different inputs, some

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00:11:35.620 --> 00:11:38.950
of which are very personal, like
maybe your own buying patterns.

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00:11:38.950 --> 00:11:40.660
Some of them are
regional, like, "What's

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00:11:40.660 --> 00:11:43.120
going on with buying
patterns in this region?"

265
00:11:43.120 --> 00:11:45.840
They could be environmental
factors, like, "What's

266
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the weather today?"

267
00:11:47.000 --> 00:11:50.850
It could have to do with supply
chain loads, like, "What do we

268
00:11:50.850 --> 00:11:53.300
actually have in stock
that we need to sell?"

269
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Those are actually much
harder to do than they sound.

270
00:11:57.090 --> 00:11:58.310
It sounds very simple.

271
00:11:58.310 --> 00:12:02.010
But let me give you an example
to illustrate why some of this

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00:12:02.010 --> 00:12:04.530
is hard and why I
always start with data.

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Doesn't it sound
easy to figure out

274
00:12:07.860 --> 00:12:10.670
if there are the
ingredients for a latte

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so that you can promote
a latte on the phone?

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00:12:13.180 --> 00:12:14.956
SAM RANSBOTHAM:
Naively, I'll say yes.

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00:12:14.956 --> 00:12:15.033


278
00:12:15.033 --> 00:12:16.450
GERRI MARTIN-FLICKINGER:
It sounds

279
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like it's a thing, right?

280
00:12:17.492 --> 00:12:18.860
Like, "Yeah, we have lattes."

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00:12:18.860 --> 00:12:21.310
Well, actually, lattes
are manufactured

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in the moment at a
store, and they're

283
00:12:23.130 --> 00:12:24.930
made of component parts.

284
00:12:24.930 --> 00:12:27.160
They're made of
some espresso, which

285
00:12:27.160 --> 00:12:29.920
could be different
kinds of espresso, made

286
00:12:29.920 --> 00:12:31.580
with some type of
a milk product,

287
00:12:31.580 --> 00:12:33.630
which could be
cow milk, it could

288
00:12:33.630 --> 00:12:34.770
be an alt [nondairy] milk.

289
00:12:34.770 --> 00:12:37.240
And then it could be heated
to different temperatures

290
00:12:37.240 --> 00:12:39.280
based on what the
customer has asked for.

291
00:12:39.280 --> 00:12:40.730
And that's a simple drink.

292
00:12:40.730 --> 00:12:43.840
That is the simplest
espresso drink, probably,

293
00:12:43.840 --> 00:12:45.530
you can get in a store.

294
00:12:45.530 --> 00:12:47.440
So here's why
that's complicated.

295
00:12:47.440 --> 00:12:50.220
If you're in the
store, the customer

296
00:12:50.220 --> 00:12:51.730
just knows it as a latte.

297
00:12:51.730 --> 00:12:53.760
But if you think about
the entire supply

298
00:12:53.760 --> 00:12:55.630
chain of all of
the component parts

299
00:12:55.630 --> 00:12:59.010
that have to be available
at that moment in the front

300
00:12:59.010 --> 00:13:03.050
of the house, behind the
counter, to make that latte,

301
00:13:03.050 --> 00:13:04.500
that's a whole
different problem.

302
00:13:04.500 --> 00:13:06.792
And now you've got to get
all the way back to your data

303
00:13:06.792 --> 00:13:10.030
master, the data master
that is the component parts,

304
00:13:10.030 --> 00:13:13.550
and understanding if they
were delivered that night

305
00:13:13.550 --> 00:13:16.590
in the back of the store.

306
00:13:16.590 --> 00:13:18.430
Now you've got a
data problem that

307
00:13:18.430 --> 00:13:20.515
requires you to
decompose and restructure

308
00:13:20.515 --> 00:13:23.140
the data all the way back to the
origin, if you haven't already

309
00:13:23.140 --> 00:13:24.000
done that.

310
00:13:24.000 --> 00:13:27.590
And I'm only illustrating this
because so often you think,

311
00:13:27.590 --> 00:13:29.800
"Well, it's an easy
ML problem to say,

312
00:13:29.800 --> 00:13:33.480
'We want to promote lattes.'"
But the second you do that,

313
00:13:33.480 --> 00:13:36.690
you actually have to know the
deepest level of data possible

314
00:13:36.690 --> 00:13:39.210
to ensure you actually
have the product to sell.

315
00:13:39.210 --> 00:13:41.210
SAM RANSBOTHAM: That's
tricky because, actually,

316
00:13:41.210 --> 00:13:43.090
when you say "latte," I
know exactly what you mean,

317
00:13:43.090 --> 00:13:45.180
because you mean exactly
the one that I would drink.

318
00:13:45.180 --> 00:13:47.150
You don't mean the one
that Shervin would drink.

319
00:13:47.150 --> 00:13:47.880
GERRI MARTIN-FLICKINGER: Right.

320
00:13:47.880 --> 00:13:49.010
SAM RANSBOTHAM: And to
answer the question,

321
00:13:49.010 --> 00:13:51.010
you've got to answer it
for every single person.

322
00:13:51.010 --> 00:13:51.180


323
00:13:51.180 --> 00:13:52.070
GERRI MARTIN-FLICKINGER: Right.

324
00:13:52.070 --> 00:13:53.903
And you're not going
to enumerate all those.

325
00:13:53.903 --> 00:13:56.100
There's infinite possibilities
for customization.

326
00:13:56.100 --> 00:13:56.870
Infinite.

327
00:13:56.870 --> 00:13:58.020
So you can't do that.

328
00:13:58.020 --> 00:14:01.413
You have to actually work
at it as a data problem.

329
00:14:01.413 --> 00:14:03.330
And then you can do the
AI model on top of it,

330
00:14:03.330 --> 00:14:05.975
because you've actually figured
out what you have to work with.

331
00:14:05.975 --> 00:14:07.350
SHERVIN KHODABANDEH:
And then you

332
00:14:07.350 --> 00:14:11.440
also talked about the supply
chain issue and the inventory

333
00:14:11.440 --> 00:14:12.150
management.

334
00:14:12.150 --> 00:14:16.970
And the point to me is, these
use cases are not in silos

335
00:14:16.970 --> 00:14:17.470
anymore.

336
00:14:17.470 --> 00:14:18.845
GERRI MARTIN-FLICKINGER:
Exactly.

337
00:14:18.845 --> 00:14:18.940


338
00:14:18.940 --> 00:14:21.440
SHERVIN KHODABANDEH: The whole
foundational data, of course,

339
00:14:21.440 --> 00:14:23.740
is critical to power them.

340
00:14:23.740 --> 00:14:28.540
But how we market impacts
what happens in the store,

341
00:14:28.540 --> 00:14:32.482
and supply chain issues
impact what we should be able

342
00:14:32.482 --> 00:14:33.440
to market or shouldn't.

343
00:14:33.440 --> 00:14:33.860
And so --

344
00:14:33.860 --> 00:14:34.780
GERRI MARTIN-FLICKINGER:
Totally.

345
00:14:34.780 --> 00:14:37.350
SHERVIN KHODABANDEH: My
follow-up is, for the business

346
00:14:37.350 --> 00:14:39.800
leaders who are
listening to this,

347
00:14:39.800 --> 00:14:44.860
I think there are many analogs
of what you just described

348
00:14:44.860 --> 00:14:48.440
that would resonate in
any line of business,

349
00:14:48.440 --> 00:14:50.580
because you've got
these groups that

350
00:14:50.580 --> 00:14:53.090
are different lines of business
or different functional

351
00:14:53.090 --> 00:14:57.400
components that, in today's
world with today's data,

352
00:14:57.400 --> 00:15:00.490
are much more
interactive, and there's

353
00:15:00.490 --> 00:15:02.930
a network effect of all
of these things, which

354
00:15:02.930 --> 00:15:05.570
requires teams to come
together that normally wouldn't

355
00:15:05.570 --> 00:15:07.090
work together.

356
00:15:07.090 --> 00:15:10.980
What advice do you have for
the CEO or the president

357
00:15:10.980 --> 00:15:14.310
of a business unit to break
these silos, because you've

358
00:15:14.310 --> 00:15:17.150
got different teams
with different tools,

359
00:15:17.150 --> 00:15:19.600
different incentives, right?

360
00:15:19.600 --> 00:15:22.690
That must be a daunting
organizational problem.

361
00:15:22.690 --> 00:15:25.250
It's not just a
technology problem.

362
00:15:25.250 --> 00:15:26.920
And you've seen that work well.

363
00:15:26.920 --> 00:15:29.427
I'm just curious -- what
advice would you have?

364
00:15:29.427 --> 00:15:31.010
GERRI MARTIN-FLICKINGER:
Well, I don't

365
00:15:31.010 --> 00:15:32.250
think there's any magic here.

366
00:15:32.250 --> 00:15:34.980
I think, as in most
things in business,

367
00:15:34.980 --> 00:15:37.470
you have to start by
being really clear on,

368
00:15:37.470 --> 00:15:38.620
what is the objective?

369
00:15:38.620 --> 00:15:40.650
What are you solving for?

370
00:15:40.650 --> 00:15:42.980
That sounds so simple,
but sometimes that's

371
00:15:42.980 --> 00:15:44.530
really hard to figure out.

372
00:15:44.530 --> 00:15:47.780
Is what you're solving
for increased revenue?

373
00:15:47.780 --> 00:15:50.410
Is it increased
customer retention?

374
00:15:50.410 --> 00:15:52.760
Is it improved margin?

375
00:15:52.760 --> 00:15:55.130
Is it something else?

376
00:15:55.130 --> 00:15:57.800
Getting really clear on that
is part of what gets all

377
00:15:57.800 --> 00:16:01.370
the constituencies to
go at loggerheads --

378
00:16:01.370 --> 00:16:03.307
somebody carries
the hat of revenue,

379
00:16:03.307 --> 00:16:05.140
and somebody else carries
the hat of margin,

380
00:16:05.140 --> 00:16:07.820
and somebody else carries
customer experience.

381
00:16:07.820 --> 00:16:09.980
You've got to get clear on
what you're solving for.

382
00:16:09.980 --> 00:16:12.105
And you can't solve for
all of it at the same time.

383
00:16:12.105 --> 00:16:13.860
Now, you can benefit
it all, but you

384
00:16:13.860 --> 00:16:16.440
have to get really clear on
"What are we going after?"

385
00:16:16.440 --> 00:16:19.370
So that's my first advice: [to]
be really clear on what problem

386
00:16:19.370 --> 00:16:20.870
you're trying to solve.

387
00:16:20.870 --> 00:16:24.810
I think one thing I believe a
lot in bringing people together

388
00:16:24.810 --> 00:16:27.410
who have different expertise.

389
00:16:27.410 --> 00:16:30.110
I actually think
it's a good thing.

390
00:16:30.110 --> 00:16:31.860
It's a good thing to
bring people together

391
00:16:31.860 --> 00:16:34.970
who have five different
specialties, because they're

392
00:16:34.970 --> 00:16:37.830
going to bring the very best
thinking for that domain.

393
00:16:37.830 --> 00:16:39.310
But then you also
have to have them

394
00:16:39.310 --> 00:16:41.430
feel like they're
in it together,

395
00:16:41.430 --> 00:16:43.848
and that's good,
old-fashioned teamwork.

396
00:16:43.848 --> 00:16:45.890
And I hate to say "good,
old-fashioned teamwork,"

397
00:16:45.890 --> 00:16:48.860
but for as long as I've
been in the business world,

398
00:16:48.860 --> 00:16:52.670
it all comes down to the same
things: Are you getting people

399
00:16:52.670 --> 00:16:54.380
together with a common vision?

400
00:16:54.380 --> 00:16:58.600
Are you giving them room
to fail so that they

401
00:16:58.600 --> 00:17:00.490
can get onto a path to success?

402
00:17:00.490 --> 00:17:03.120
Are you giving
them a goal that's

403
00:17:03.120 --> 00:17:07.240
really clear, with a timeline
that's achievable but also

404
00:17:07.240 --> 00:17:08.430
really clear?

405
00:17:08.430 --> 00:17:11.060
And then are you supporting
them with the resources

406
00:17:11.060 --> 00:17:13.260
and the budget that they
need to be successful?

407
00:17:13.260 --> 00:17:14.700
It's all that same stuff.

408
00:17:14.700 --> 00:17:17.200
There's nothing new there.

409
00:17:17.200 --> 00:17:18.922
I do think where a
lot of people fail

410
00:17:18.922 --> 00:17:21.130
is, they don't start off
with a clear problem they're

411
00:17:21.130 --> 00:17:22.030
trying to solve.

412
00:17:22.030 --> 00:17:25.410
And that tends to get people
to all get really entrenched

413
00:17:25.410 --> 00:17:27.385
in their silos and
then go off and try

414
00:17:27.385 --> 00:17:28.510
to solve their own problem.

415
00:17:28.510 --> 00:17:30.380
SHERVIN KHODABANDEH:
That's very well said.

416
00:17:30.380 --> 00:17:32.260
SAM RANSBOTHAM:
I'm struck as I'm

417
00:17:32.260 --> 00:17:34.320
listening about how
much depth you obviously

418
00:17:34.320 --> 00:17:38.317
have in making a latte, but
that wasn't your background;

419
00:17:38.317 --> 00:17:40.400
we didn't hear that stop
[in your career journey].

420
00:17:40.400 --> 00:17:43.400
How do you get people to know
so much about the domain area

421
00:17:43.400 --> 00:17:45.670
to then be able to solve
it with the technology

422
00:17:45.670 --> 00:17:47.030
that you're using to solve it?

423
00:17:47.030 --> 00:17:49.648
It seems like a very difficult
thing to pull together?

424
00:17:49.648 --> 00:17:51.190
GERRI MARTIN-FLICKINGER:
I don't know

425
00:17:51.190 --> 00:17:54.190
which question to answer: the
one about how did I end up

426
00:17:54.190 --> 00:17:55.782
learning to make a
latte at Starbucks

427
00:17:55.782 --> 00:17:57.740
or, in general, how do
you do that in business?

428
00:17:57.740 --> 00:17:58.700
SHERVIN KHODABANDEH:
Let's start there.

429
00:17:58.700 --> 00:18:01.117
GERRI MARTIN-FLICKINGER: OK,
we can start with my journey.

430
00:18:01.117 --> 00:18:04.180
So, yeah, I came
out of technology.

431
00:18:04.180 --> 00:18:07.490
I had been in enterprise
software for many, many years

432
00:18:07.490 --> 00:18:09.180
in Silicon Valley.

433
00:18:09.180 --> 00:18:12.010
And my decision to
come to Starbucks

434
00:18:12.010 --> 00:18:13.100
was kind of interesting.

435
00:18:13.100 --> 00:18:15.020
I was, first of
all, just intrigued.

436
00:18:15.020 --> 00:18:17.140
I was intrigued by the scale.

437
00:18:17.140 --> 00:18:19.640
And the scale is interesting
when you think about Starbucks,

438
00:18:19.640 --> 00:18:23.350
because today there's over
30,000 stores around the world.

439
00:18:23.350 --> 00:18:26.960
There's over 300,000
baristas around the world.

440
00:18:26.960 --> 00:18:29.350
And why it's an
interesting scale problem

441
00:18:29.350 --> 00:18:33.550
is not just the number of
customers that visit Starbucks.

442
00:18:33.550 --> 00:18:37.060
But if you think about
those 30,000-some stores,

443
00:18:37.060 --> 00:18:40.360
each one is like a little
business unto itself.

444
00:18:40.360 --> 00:18:41.890
When you're in
enterprise software,

445
00:18:41.890 --> 00:18:44.648
you might have a hundred
offices around the world.

446
00:18:44.648 --> 00:18:46.440
You might have a ton
of people, but they're

447
00:18:46.440 --> 00:18:49.330
in these big offices
with big pipes

448
00:18:49.330 --> 00:18:53.020
and lots of infrastructure, and
you have a support team there.

449
00:18:53.020 --> 00:18:56.560
When you've got a store
in the middle of Oklahoma

450
00:18:56.560 --> 00:18:59.980
on a dial-up line, that's
a whole different thing

451
00:18:59.980 --> 00:19:00.980
to manage.

452
00:19:00.980 --> 00:19:04.180
And to have the same expectation
of quality for a customer who's

453
00:19:04.180 --> 00:19:07.010
got their mobile order-and-pay
application, that's

454
00:19:07.010 --> 00:19:08.660
just a whole different game.

455
00:19:08.660 --> 00:19:11.740
And I was really intrigued
by, of course, IoT

456
00:19:11.740 --> 00:19:15.050
and how much more could be done
in brick-and-mortar [retail]

457
00:19:15.050 --> 00:19:16.720
with IoT devices.

458
00:19:16.720 --> 00:19:22.090
I was intrigued by how quickly
I saw consumer digital growing.

459
00:19:22.090 --> 00:19:26.230
And so all those things are
what made me come to Starbucks

460
00:19:26.230 --> 00:19:29.390
and be part of that
transformation journey.

461
00:19:29.390 --> 00:19:31.120
How do you learn
when you know nothing

462
00:19:31.120 --> 00:19:33.050
about food and beverage?

463
00:19:33.050 --> 00:19:35.670
First thing you do is you spend
time in the stores, you know?

464
00:19:35.670 --> 00:19:39.440
I spent my first few
weeks in a store learning

465
00:19:39.440 --> 00:19:41.870
about how people make lattes.

466
00:19:41.870 --> 00:19:46.210
Now, I cannot claim to be a
barista by any stretch at all,

467
00:19:46.210 --> 00:19:49.720
so when I was in the store, I
was mostly helping clean or I

468
00:19:49.720 --> 00:19:52.860
was greeting customers, or I
was trying to do things I could

469
00:19:52.860 --> 00:19:53.540
actually do.

470
00:19:53.540 --> 00:19:56.690
But in the process, you
learn a lot about what goes

471
00:19:56.690 --> 00:19:59.580
on in a store -- and not just
the really cool stuff that you

472
00:19:59.580 --> 00:20:03.500
see, like making the lattes
or greeting the customers,

473
00:20:03.500 --> 00:20:06.240
but what goes on in
the back of house.

474
00:20:06.240 --> 00:20:07.880
How do they receive inventory?

475
00:20:07.880 --> 00:20:08.540
Oh my gosh.

476
00:20:08.540 --> 00:20:09.940
How do they do payroll?

477
00:20:09.940 --> 00:20:12.390
How do they have to
do labor scheduling?

478
00:20:12.390 --> 00:20:14.550
What does that look like?

479
00:20:14.550 --> 00:20:16.970
And that's an eye-opener.

480
00:20:16.970 --> 00:20:19.850
And I would say, forget
what business you're in.

481
00:20:19.850 --> 00:20:22.680
Whatever business you're in, if
you're a technologist, if you

482
00:20:22.680 --> 00:20:26.530
are not sitting shoulder
[to] shoulder with whoever

483
00:20:26.530 --> 00:20:28.580
is the tip of the
spear of the business,

484
00:20:28.580 --> 00:20:29.850
you're missing an opportunity.

485
00:20:29.850 --> 00:20:30.950
You've got to do that.

486
00:20:30.950 --> 00:20:33.280
So when I was in
enterprise software,

487
00:20:33.280 --> 00:20:35.930
I spent a lot of time
going out with salespeople

488
00:20:35.930 --> 00:20:37.160
to visit customers.

489
00:20:37.160 --> 00:20:39.820
I just wanted to see, what
are the customers thinking?

490
00:20:39.820 --> 00:20:40.500
Do they love us?

491
00:20:40.500 --> 00:20:41.430
Do they hate us?

492
00:20:41.430 --> 00:20:43.100
What problems are they having?

493
00:20:43.100 --> 00:20:45.590
Spending time with
the customer support

494
00:20:45.590 --> 00:20:47.760
center to just sit down
and listen, occasionally,

495
00:20:47.760 --> 00:20:50.340
to the calls they were
getting: What is the world

496
00:20:50.340 --> 00:20:52.910
thinking about us, and how
are people who are depending

497
00:20:52.910 --> 00:20:54.453
on our software feeling?

498
00:20:54.453 --> 00:20:55.110


499
00:20:55.110 --> 00:20:57.360
SHERVIN KHODABANDEH: Did you
spend time on the oil rig

500
00:20:57.360 --> 00:20:58.797
when you were at Chevron?

501
00:20:58.797 --> 00:21:01.130
GERRI MARTIN-FLICKINGER: I
did spend time at refineries.

502
00:21:01.130 --> 00:21:02.630
Yeah, a little bit of
time at refineries.

503
00:21:02.630 --> 00:21:03.520
SHERVIN KHODABANDEH:
That's great.

504
00:21:03.520 --> 00:21:03.662


505
00:21:03.662 --> 00:21:06.120
SAM RANSBOTHAM: Actually, it
reminds me of Prakhar Mehrotra

506
00:21:06.120 --> 00:21:07.140
and Walmart.

507
00:21:07.140 --> 00:21:09.550
He went out there and --
this was one of our earlier

508
00:21:09.550 --> 00:21:12.320
interviews -- said a very
similar thing about how you

509
00:21:12.320 --> 00:21:15.360
understand how to automate
or how to put technology

510
00:21:15.360 --> 00:21:16.380
into these situations.

511
00:21:16.380 --> 00:21:19.020
And it was very much echoing the
kinds of things you're saying

512
00:21:19.020 --> 00:21:21.180
-- [it] can't be
done in isolation.

513
00:21:21.180 --> 00:21:23.080
GERRI MARTIN-FLICKINGER:
That's right.

514
00:21:23.080 --> 00:21:27.690
And I do think there's a couple
of things that, in my playbook,

515
00:21:27.690 --> 00:21:30.700
have continued to pay
off over and over again.

516
00:21:30.700 --> 00:21:32.730
And they're just
simple, simple things.

517
00:21:32.730 --> 00:21:35.260
The first is, words matter.

518
00:21:35.260 --> 00:21:36.270
Words matter.

519
00:21:36.270 --> 00:21:37.630
They really do.

520
00:21:37.630 --> 00:21:40.570
When I say to you -- and
I'm going to ask both of you

521
00:21:40.570 --> 00:21:43.900
to answer back at me; I'm going
to ask you a question now:

522
00:21:43.900 --> 00:21:46.150
When I say the word "IT",
what do you think of?

523
00:21:46.150 --> 00:21:46.650


524
00:21:46.650 --> 00:21:50.510
SHERVIN KHODABANDEH: I think
of email servers and software

525
00:21:50.510 --> 00:21:52.670
patches and things like that.

526
00:21:52.670 --> 00:21:54.722
GERRI MARTIN-FLICKINGER:
Sam, what about you?

527
00:21:54.722 --> 00:21:56.180
SAM RANSBOTHAM: I
think I'm biased.

528
00:21:56.180 --> 00:21:58.310
I thought more of a
strategy-oriented,

529
00:21:58.310 --> 00:22:02.722
how you're enabling connectivity
within the organization --

530
00:22:02.722 --> 00:22:04.930
GERRI MARTIN-FLICKINGER:
We're not using your answer.

531
00:22:04.930 --> 00:22:05.450
No, no, no.

532
00:22:05.450 --> 00:22:06.720
SAM RANSBOTHAM: OK.

533
00:22:06.720 --> 00:22:09.880
I'm an IT prof, so that's
maybe my bias there.

534
00:22:09.880 --> 00:22:12.010
Do you think most
people go, "Operations"?

535
00:22:12.010 --> 00:22:13.635
GERRI MARTIN-FLICKINGER:
When I say IT,

536
00:22:13.635 --> 00:22:15.445
most people will talk
about the help desk;

537
00:22:15.445 --> 00:22:16.570
they'll talk about outages.

538
00:22:16.570 --> 00:22:17.730
SAM RANSBOTHAM: Trouble tickets.

539
00:22:17.730 --> 00:22:19.938
GERRI MARTIN-FLICKINGER:
They'll talk about services,

540
00:22:19.938 --> 00:22:21.340
trouble tickets, data centers.

541
00:22:21.340 --> 00:22:21.890
Right?

542
00:22:21.890 --> 00:22:22.400
OK.

543
00:22:22.400 --> 00:22:26.220
When I say technology,
people say, "The future.

544
00:22:26.220 --> 00:22:27.190
Innovation.

545
00:22:27.190 --> 00:22:27.770
New things."

546
00:22:27.770 --> 00:22:28.270
Right?

547
00:22:28.270 --> 00:22:30.580
So if you're in a
business, and someone

548
00:22:30.580 --> 00:22:33.630
introduces someone who's
in the IT department,

549
00:22:33.630 --> 00:22:35.180
they have one reaction.

550
00:22:35.180 --> 00:22:37.675
If I introduce someone to
you and I say, "This is" --

551
00:22:37.675 --> 00:22:38.800
in the case of Starbucks --

552
00:22:38.800 --> 00:22:43.380
"Starbucks Technology,"
which one sounds and feels

553
00:22:43.380 --> 00:22:44.470
more future-leaning?

554
00:22:44.470 --> 00:22:46.290
SAM RANSBOTHAM: Actually,
that's a huge difference.

555
00:22:46.290 --> 00:22:47.580
GERRI MARTIN-FLICKINGER:
It's a huge difference.

556
00:22:47.580 --> 00:22:49.470
SAM RANSBOTHAM: I definitely
see the difference there.

557
00:22:49.470 --> 00:22:50.762
GERRI MARTIN-FLICKINGER: Right.

558
00:22:50.762 --> 00:22:52.530
Which is why I said
words really matter.

559
00:22:52.530 --> 00:22:54.750
And so we were talking
about transformation

560
00:22:54.750 --> 00:22:57.100
and how do you
transform a technology

561
00:22:57.100 --> 00:22:58.262
organization to the future.

562
00:22:58.262 --> 00:23:00.720
And so, one thing that's one
of these tried-and-true things

563
00:23:00.720 --> 00:23:03.300
is, did you name
your organization

564
00:23:03.300 --> 00:23:06.797
in a way that makes the
organization proud, so

565
00:23:06.797 --> 00:23:08.630
that every single person
in the organization

566
00:23:08.630 --> 00:23:11.150
sits up a little straighter and
maybe works a little harder?

567
00:23:11.150 --> 00:23:14.230
Have you named the organization
in a way that really represents

568
00:23:14.230 --> 00:23:16.040
what you want it to become?

569
00:23:16.040 --> 00:23:18.820
And have you named it in
a way that everyone else

570
00:23:18.820 --> 00:23:21.040
in the business looks at
it and goes, "Oh, that's

571
00:23:21.040 --> 00:23:22.640
something a little different"?

572
00:23:22.640 --> 00:23:23.140
OK.

573
00:23:23.140 --> 00:23:26.707
I know a long answer
to one of the things

574
00:23:26.707 --> 00:23:28.790
that I think is really
important in transformation

575
00:23:28.790 --> 00:23:30.660
is to signal that
you're doing it.

576
00:23:30.660 --> 00:23:34.120
And so, for example,
with Starbucks,

577
00:23:34.120 --> 00:23:35.950
when I joined, 90
days after I joined,

578
00:23:35.950 --> 00:23:38.900
[I] changed the name of IT
to Starbucks Technology.

579
00:23:38.900 --> 00:23:40.600
Never used the word IT again.

580
00:23:40.600 --> 00:23:42.850
And if I was ever in a meeting
where somebody said IT,

581
00:23:42.850 --> 00:23:45.140
I'd stop the meeting and
I'd say, "We don't have IT.

582
00:23:45.140 --> 00:23:47.390
We have Starbucks Technology,"
and it's kind of funny,

583
00:23:47.390 --> 00:23:50.830
because that one change
made a big difference.

584
00:23:50.830 --> 00:23:53.290
The next thing that I
think can make a difference

585
00:23:53.290 --> 00:23:55.247
is, you need a tagline.

586
00:23:55.247 --> 00:23:57.080
I hate to say it, but
everybody in business,

587
00:23:57.080 --> 00:23:58.860
everyone who's a CEO, knows it.

588
00:23:58.860 --> 00:24:02.300
You've got to tell your story,
and you don't get five hours

589
00:24:02.300 --> 00:24:03.280
to tell your story.

590
00:24:03.280 --> 00:24:06.560
You get six to 10
words, and you'd better

591
00:24:06.560 --> 00:24:09.200
get people curious to ask more.

592
00:24:09.200 --> 00:24:12.090
And so, put a tagline in place.

593
00:24:12.090 --> 00:24:18.440
Super simple: "Talented
technologists delivering today,

594
00:24:18.440 --> 00:24:20.900
leading into the future.

595
00:24:20.900 --> 00:24:22.440
Starbucks Technology."

596
00:24:22.440 --> 00:24:23.760
That's it.

597
00:24:23.760 --> 00:24:28.030
And that simple phrase,
which is used today still,

598
00:24:28.030 --> 00:24:32.970
after six years, just continues
to reinforce the value

599
00:24:32.970 --> 00:24:35.370
of the organization,
the value of the people,

600
00:24:35.370 --> 00:24:37.620
the importance of
getting the work done --

601
00:24:37.620 --> 00:24:40.440
as well as continuing
to build for the future.

602
00:24:40.440 --> 00:24:43.260
And so, for me, transformation
comes down to people.

603
00:24:43.260 --> 00:24:45.003
And to do any
transformation with tech

604
00:24:45.003 --> 00:24:46.420
has nothing to do
with the tech as

605
00:24:46.420 --> 00:24:48.360
much as it has to do
with the people who

606
00:24:48.360 --> 00:24:49.520
are making it happen.

607
00:24:49.520 --> 00:24:52.010
They have to feel
inspired, they have

608
00:24:52.010 --> 00:24:53.780
to feel what they're
doing is important,

609
00:24:53.780 --> 00:24:56.180
and they have to feel like
they have room to be part

610
00:24:56.180 --> 00:24:58.330
of the invention of the future.

611
00:24:58.330 --> 00:25:00.760
And I think that's all
we have to do as leaders,

612
00:25:00.760 --> 00:25:01.740
is make room for that.

613
00:25:01.740 --> 00:25:04.160
SAM RANSBOTHAM: Gerri, it
was great talking with you.

614
00:25:04.160 --> 00:25:07.180
[You have] such a vast
experience and a great ability

615
00:25:07.180 --> 00:25:08.950
to connect those
experiences together

616
00:25:08.950 --> 00:25:11.443
to give us a holistic
view of what's happening

617
00:25:11.443 --> 00:25:12.860
and what may happen
in the future.

618
00:25:12.860 --> 00:25:13.770
Thank you for taking the time.

619
00:25:13.770 --> 00:25:15.340
SHERVIN KHODABANDEH: It's
been really wonderful.

620
00:25:15.340 --> 00:25:16.010
Thank you.

621
00:25:16.010 --> 00:25:16.980
GERRI MARTIN-FLICKINGER:
It's been fun.

622
00:25:16.980 --> 00:25:17.770
Thanks so much.

623
00:25:17.770 --> 00:25:18.123


624
00:25:18.123 --> 00:25:19.790
SAM RANSBOTHAM: Next
time, Shervin and I

625
00:25:19.790 --> 00:25:21.260
talk with Barbara
Martin Coppola,

626
00:25:21.260 --> 00:25:23.300
the chief digital
officer for IKEA Retail.

627
00:25:23.300 --> 00:25:24.920
Join us as we hear
what Barbara thinks

628
00:25:24.920 --> 00:25:26.337
about the meaning
behind the words

629
00:25:26.337 --> 00:25:28.830
we use when we talk about
artificial intelligence.

630
00:25:28.830 --> 00:25:30.360
ALLISON RYDER:
Thanks for listening

631
00:25:30.360 --> 00:25:31.860
to Me, Myself, and AI.

632
00:25:31.860 --> 00:25:34.320
We believe, like you,
that the conversation

633
00:25:34.320 --> 00:25:36.540
about AI implementation
doesn't start and stop

634
00:25:36.540 --> 00:25:37.660
with this podcast.

635
00:25:37.660 --> 00:25:40.210
That's why we've created a
group on LinkedIn, specifically

636
00:25:40.210 --> 00:25:41.330
for leaders like you.

637
00:25:41.330 --> 00:25:44.070
It's called AI for Leaders,
and if you join us,

638
00:25:44.070 --> 00:25:46.100
you can chat with show
creators and hosts,

639
00:25:46.100 --> 00:25:49.710
ask your own questions, share
insights, and gain access

640
00:25:49.710 --> 00:25:52.210
to valuable resources
about AI implementation

641
00:25:52.210 --> 00:25:54.300
from MIT SMR and BCG.

642
00:25:54.300 --> 00:25:59.380
You can access it by visiting
mitsmr.com/AIforLeaders.

643
00:25:59.380 --> 00:26:02.140
We'll put that link
in the show notes,

644
00:26:02.140 --> 00:26:04.580
and we hope to see you there.

645
00:26:04.580 --> 00:26:10.000