WEBVTT

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SAM RANSBOTHAM:
What do Lego have

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to do with how PayPal
thinks about AI?

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Find out on today's episode.

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KHATEREH KHODAVIRDI:
I'm Khatereh Khodavirdi

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from PayPal, and
you're listening

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to Me, Myself, and AI.

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SAM RANSBOTHAM: Welcome
to Me, Myself, and AI,

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a podcast 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 analytics at Boston College.

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I'm also the AI and business
strategy guest editor

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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 researching and publishing

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on AI for six 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, Shervin and I

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are talking with
Khatereh Khodavirdi.

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She's the senior director
of data science at PayPal.

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Khatereh, thanks for joining us.

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Welcome.

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KHATEREH KHODAVIRDI:
Thanks for having me.

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Great to be here.

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SAM RANSBOTHAM: You build and
oversee giant data science

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teams for the many different
entities within PayPal.

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A lot of people know PayPal,
but probably some people

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don't realize the extent of
all the PayPal activities,

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so maybe let's start there.

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Tell us a bit about
what PayPal does

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and what all these different
subentities do as well.

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How are they connected?

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KHATEREH KHODAVIRDI: Yes.

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Obviously, a majority
of the people

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know PayPal through
PayPal Checkout, which

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is a core product that
we have, and the company

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started from that product.

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But we have a wealth
of different products,

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especially on the consumer
and merchant side.

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We have whole suites of
products to provide people

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the capability on the merchant
side to run their business,

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from invoicing [to] having
the offline capability

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for financial services and
then the online transactions,

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and on the consumer
side, starting

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from peer-to-peer payment
to financial services

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to different types of
credit and "buy now, pay

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later" capability,
and savings accounts.

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I can go on and on, but
on the consumer side,

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we have a wealth of
different products.

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Over time, we also acquired
many other companies to help us

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accelerate and also be
incremental in terms

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of the value chain that we
are creating for the consumer

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and merchant, such as
on the consumer side,

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a couple years ago, we
acquired a company --

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its name is Honey -- which
actually helps you to find

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the best deal on the internet
when you're shopping online.

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I started at PayPal
on the consumer side,

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so I was helping the
small-business group,

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helping accelerate and solve
the problems for our customers

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through data and data
science capability.

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And then, over time, I
supported all the merchant side

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of our equation -- all
the enterprise merchants,

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channel partners, our
relationship with Shopify,

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WooCommerce, Magenta --
programmatically bringing

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a merchant for us.

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And then, a couple
months ago, I actually

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switched completely to the
consumer side of the house.

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So I'm new on the consumer
side but super excited,

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because you can leverage a
lot of AI and data science

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capability to solve a lot
of interesting problems

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in this domain.

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SHERVIN KHODABANDEH:
That's great.

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So obviously, PayPal is a
big, multifaceted company

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with different business
and subdivisions,

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and you talked about those.

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But since you're in the consumer
group, let's talk about that.

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Share with us a bit how AI's
being used in the consumer

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business to drive the themes
you want with your consumers

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and what use cases
is AI being used for.

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KHATEREH KHODAVIRDI:
We can actually

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bring all the different
accounts together,

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so we have a point of view
around the user interactions

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with us.

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For example, if Shervin has a
relationship to Honey with us,

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but at the same time he's using
peer-to-peer or the Checkout

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product or our
credit capability,

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how can we make sure
that we can actually

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look at Shervin's relationship
with PayPal through one lens?

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So that is, I would say, one of
the fundamental problems we are

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trying to solve as a company.

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But the piece I'm personally
super excited about is around

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the fact of understanding
the customer journey with us

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and how we can leverage
personalization and AI

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to actually solve
customer use cases --

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that "What are the jobs they are
coming to PayPal to get done?"

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and "How can we show them
the personalized and relevant

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messages to help them?"

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SO THINK ABOUT IT: Internally,
we always said, "Hey,

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there are a bunch of
happy paths with PayPal,

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and there are a bunch of
sad paths with PayPal.

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How can we migrate people
from one happy path

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to a happier path with us, and
how can we avoid the sad path

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with the customer?"

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And I truly believe that this
is basically an AI capability

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that we need to
develop for customers.

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If you build that, it will
unlock a humongous amount

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of value for our consumers
and for us as a company.

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SHERVIN KHODABANDEH: Let's
talk about some of those paths.

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You said, "What kind of jobs
are people trying to get done?"

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so let's talk about that.

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Tell us how personalization
could be helpful there.

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KHATEREH KHODAVIRDI:
Think about it this way:

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Imagine Shervin is one of
the people that is actually

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using PayPal Checkout to shop
through different merchants

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throughout the internet as well.

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As you can imagine,
once we acquired Honey,

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we also had a whole wealth
of the coupons that are

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[available] out
there, and over time,

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if you get a better sense around
what type of categories or what

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type of merchant Shervin
is interested in,

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we can actually show Shervin the
right deals at the right time.

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For example, it's
back-to-school time,

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and we know that
historically, at this time,

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you shop in this
type of category.

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Currently, you know,
Target or Walmart

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or some of the top
merchants for back to school

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are running these
deals, and then

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we remind you and show you
relevant and personalized deals

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to actually drive
activity with us.

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Or you might use Windmill on
a weekly or biweekly basis

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to send somebody who cleans
your house some money.

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We can remind you
that, "Hey, Shervin, it

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looks like you're doing
this," and through just

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one-click touch,
you go and do that.

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So basically, we become
part of Shervin's life

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and understand what
type of activity

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Shervin is trying to do and
make it much easier for him.

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The other part of it that we
are all super excited about it

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is tracking.

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When you shop online to
a different retailer,

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one part of it is, you
want to track that order

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and see where your order is
and when you are getting it.

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We also want to make
it much easier --

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you can actually get the
notification and see where

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your order is, and you can look
at your whole commerce activity

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and financial
activity in one place.

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I would say for me, also,
the other part is that, yes,

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I'm in a data function
and we build models,

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and I look at the
outcome of our model,

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but also the more
important part for me is --

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I always call it qualitative
and quantitative.

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I would say, "Hey, I
want a sample of some

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of the customers in this group.

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I want to understand their
journey, the activity they

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had with us," or,
you know, actually

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attend the user
research studies that we

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are doing with customers,
so first-hand, I actually

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hear the challenges
and the problems

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we are trying to solve.

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Because for me, the most
important part is that I just

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cannot go and in isolation
build the model and solve all

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the problems.

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It's really doing
the qualitative

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and quantitative aspects
and learning from each other

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and improving it over time.

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SHERVIN KHODABANDEH:
That's very helpful.

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I have to imagine that being
more of a tech company than,

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let's say, a financial
services company,

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that some of these
challenges are actually

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a lot easier for you to
deal with than it would be,

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let's say, for a
gigantic global bank

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that's trying to personalize
across products and business

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lines and all that.

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Share with us some
of the challenges.

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What's difficult about
what you need to do?

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KHATEREH KHODAVIRDI: I think
one of the biggest challenges we

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have is that some of the other
companies that we acquired

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over time, each of them is
using different data stacks,

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so basically migrating all
of them to one data lake

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and having one
kind of technology

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to use across the
board and being

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able to [build] that common
data layer platform end to end,

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and understanding all the
touch points and all the data

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points we have
with the customer.

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So having that common
data technology platform

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is one of the common
challenges we have internally.

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SAM RANSBOTHAM: That's huge,
I think, for everybody.

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I remember my first
experience with this.

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Back in my past life, I used
to work at the United Nations,

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and I was working with
databases, and I looked down

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and we had just literally
dozens of databases

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we were supporting, and I asked,
"Well, which is the standard?"

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And they said, "Well,
this is the standard."

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And, I said, "Well, what
are all the rest of them?"

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"Well, those were
the standards then."

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And so you're in that same
situation when you're acquiring

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companies, where you're
pulling together lots

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of technology stacks
[but] you don't

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want to just kind of rip
them out and start over.

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How do you manage that process?

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How do you get those in a
cohesive data science process?

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KHATEREH KHODAVIRDI: Yeah,
so, I would say, Sam,

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you brought a very good
question as well --

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that it's not only the
technology problem aspect

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of it.

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I call it the data
governance aspect of it

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as well: what the
definitions are,

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and how different people define
and look at different things

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differently, and
how you can define

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that common language
across the board

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internally within
the company as well.

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So that is why we are trying
internally to develop the best

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practices that every new
company that we have here

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are the steps one to end
that we are going through

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to incorporate them as part
of the rest of the data assets

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we have for the company.

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But you can imagine it's
not an easy exercise.

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It's a humongous task.

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But that data governance aspect
of is it also very important,

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because when you are bringing
different components together,

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you need to take a
step back and look

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at the definition from
a different lens as well

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and see if those
definitions still

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are relevant in the
new construct or not.

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SHERVIN KHODABANDEH: Let's
talk about teams a little bit.

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You're talking about
a series of challenges

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with personalization,
with other use cases,

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and then the capabilities that
are required to get that done,

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from data coming together
and identity resolution

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and many, many other things.

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Tell us about the team.

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Clearly, they have to have very
strong technical capabilities,

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but what else do they need
to have to work in that

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kind of an environment?

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KHATEREH KHODAVIRDI: I
would say this area is

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one of the areas that
is so multidisciplinary,

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and you can imagine the
different types of problems

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00:10:48.120 --> 00:10:51.090
you want to solve need
different types of skill sets

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and these types of talent.

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So I always said that
in data science, AI,

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or the overall data field,
one of the things that

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00:10:57.690 --> 00:11:00.910
is really important is
diversity of talent.

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And by diversity of
talent, I don't only

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mean diversity of gender
or background, which

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is very important, but
diversity of thought leadership,

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00:11:09.230 --> 00:11:12.500
diversity of problem-solving,
diversity of technical skill

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set, because at the different
stages of the problem,

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00:11:15.720 --> 00:11:18.980
basically, you need to practice
a different type of muscle

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in order to get the
desired outcome.

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So, for example, one of the
areas that I really feel [is]

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underappreciated in the data
world is business acumen --

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people who can actually tackle
the problem through a very

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structured framework and
be able to synthesize

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the recommendation and the
"so what" to the business.

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Because the worst thing that
can happen is that you look

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at your data team and you feel
like they are building a bunch

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of black boxes for the
rest of the organization,

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and you're not investing
in the last mile --

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that people actually translating
the "what" and the "why"

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and the "so what" to
the business group,

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to the product group, and to
the rest of the organization.

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So you would not
get the adoption

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that you are hoping
from the capabilities

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that you are building.

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So the way I'm looking at it
is that we are actually also

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building a product
organization who supports

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the personalization and AI,
because like any other product

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development cycle, you basically
have a product strategy behind

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this -- that we are not
practically building the AI

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model to solve
specific use cases.

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We actually take a step back,
understanding our consumer

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personas -- what are the jobs
they are coming to us to get

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done? -- and build a product
road map and product vision

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around this and tackle this
problem in a cross-functional

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fashion instead of just
internally within the data

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group.

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SAM RANSBOTHAM: Is it
harder to get that last mile

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00:12:49.970 --> 00:12:51.660
with AI-type projects?

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Is that something the
people have a harder time

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understanding?

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Is it something that is harder
for people to relate to?

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KHATEREH KHODAVIRDI: It
might be a little bit harder

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00:13:00.780 --> 00:13:04.340
because of just the scale of
the problems that you are trying

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to solve here, because people
cannot relate to it when

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you're talking about
millions of customers.

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So for me, it's how I can
actually break down the problem

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and solve smaller
use cases through AI

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00:13:16.900 --> 00:13:19.550
to create that adoption
and championship

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00:13:19.550 --> 00:13:21.820
in the organization [so]
that it will help me

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to solve the biggest problem.

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It's a humongous task,
because I'm not only

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00:13:26.160 --> 00:13:29.340
talking about the product
touch points that we have.

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I'm talking about every touch
point that the customers have

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with us, either through customer
service, through risk --

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00:13:36.170 --> 00:13:39.260
through all the different
functions within the company.

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00:13:39.260 --> 00:13:42.360
Rallying all of those
cross-functional functions

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00:13:42.360 --> 00:13:45.190
around solving this problem
would be much harder,

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versus my approach
is that I will first

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00:13:47.090 --> 00:13:50.040
start with solving it within
the product organization,

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00:13:50.040 --> 00:13:53.130
understanding all the touch
points with all the products,

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00:13:53.130 --> 00:13:56.270
understanding how we can
understand the product task,

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and personalizing
that component.

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And then you can add
an additional layer --

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00:14:00.420 --> 00:14:03.760
like bring the risk component
-- with each of the product

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00:14:03.760 --> 00:14:06.290
components, then you add
the customer service.

314
00:14:06.290 --> 00:14:08.620
I would think about it
more of a Legoland --

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00:14:08.620 --> 00:14:11.870
that at the end of the day,
we will have an AI Legoland

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00:14:11.870 --> 00:14:12.800
for PayPal.

317
00:14:12.800 --> 00:14:16.010
But right now, the way I am
attacking to solve this problem

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00:14:16.010 --> 00:14:19.470
is to build each of the
individual Lego pieces

319
00:14:19.470 --> 00:14:21.930
with the hope that I can
orchestrate and build

320
00:14:21.930 --> 00:14:25.530
a Legoland and it won't become
like bunch of separate Lego

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00:14:25.530 --> 00:14:28.765
that are not orchestrated to
solve the common equation.

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00:14:28.765 --> 00:14:30.640
SHERVIN KHODABANDEH: I
love the Lego analogy,

323
00:14:30.640 --> 00:14:33.240
because my kids are
totally into Lego

324
00:14:33.240 --> 00:14:36.420
and we have probably like
900,000 different pieces

325
00:14:36.420 --> 00:14:38.390
of Lego going on
at any given time.

326
00:14:38.390 --> 00:14:41.350
And if you just look
at it in isolation,

327
00:14:41.350 --> 00:14:43.680
you think, "OK, this
is all we're doing."

328
00:14:43.680 --> 00:14:46.150
But of course, you've
got to start there,

329
00:14:46.150 --> 00:14:47.830
and then the pieces
come together.

330
00:14:47.830 --> 00:14:52.860
So then my question to
you is, as with Lego,

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00:14:52.860 --> 00:14:56.770
when I see my 12-year-old or
eight-year-old building stuff,

332
00:14:56.770 --> 00:14:58.630
and I'm looking at
it in isolation,

333
00:14:58.630 --> 00:15:02.050
I might not have a full
sense of the vision [of what]

334
00:15:02.050 --> 00:15:03.342
the whole thing is going to be.

335
00:15:03.342 --> 00:15:04.967
So, I might say, "Oh,
this is nothing,"

336
00:15:04.967 --> 00:15:06.080
or "What are you building?

337
00:15:06.080 --> 00:15:06.920
It's like a small piece.

338
00:15:06.920 --> 00:15:08.670
Didn't you do something
like this before?"

339
00:15:08.670 --> 00:15:11.900
And then they bring out the
box with, like, 8,000 pieces

340
00:15:11.900 --> 00:15:13.450
that's going to look like this.

341
00:15:13.450 --> 00:15:15.020
And then, I go, "Uh-huh."

342
00:15:15.020 --> 00:15:17.090
How are you doing
the big "uh-huh"

343
00:15:17.090 --> 00:15:21.680
here at PayPal so people don't
lose sight of the big vision

344
00:15:21.680 --> 00:15:26.570
and don't get myopic about the
little things that it takes

345
00:15:26.570 --> 00:15:27.720
to get there?

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00:15:27.720 --> 00:15:30.240
KHATEREH KHODAVIRDI: You can
imagine there is no shortage

347
00:15:30.240 --> 00:15:34.690
of individual use cases -- that
there are many individual AI,

348
00:15:34.690 --> 00:15:36.970
or whatever you name
it, capabilities within

349
00:15:36.970 --> 00:15:37.980
the organization.

350
00:15:37.980 --> 00:15:39.650
But when you take
a step back, you

351
00:15:39.650 --> 00:15:42.000
do not have that
guiding principle

352
00:15:42.000 --> 00:15:44.830
to see how they can help you to
actually build that Legoland.

353
00:15:44.830 --> 00:15:47.430
And actually I want
to tackle this problem

354
00:15:47.430 --> 00:15:51.310
in a reverse order; first I want
to take a step back and say,

355
00:15:51.310 --> 00:15:55.550
"Hey, what will the blue sky
look like in terms of the AI

356
00:15:55.550 --> 00:15:58.150
capability and
personalization for us?"

357
00:15:58.150 --> 00:16:01.400
So building that product
strategy and vision around it,

358
00:16:01.400 --> 00:16:03.480
and then trying
to solve backward,

359
00:16:03.480 --> 00:16:07.700
and then breaking it down into
smaller pieces of the component

360
00:16:07.700 --> 00:16:11.880
Lego and being very prescriptive
around what are the key

361
00:16:11.880 --> 00:16:15.070
problems each of these
Lego will try to solve,

362
00:16:15.070 --> 00:16:18.010
and why we are building each
piece of the Legoland --

363
00:16:18.010 --> 00:16:20.820
what will be the "so
what" to the organization.

364
00:16:20.820 --> 00:16:22.560
Then you can actually
build something

365
00:16:22.560 --> 00:16:25.890
because if you show that
whole vision to everybody,

366
00:16:25.890 --> 00:16:29.540
it might be a little bit too
much for some of the people

367
00:16:29.540 --> 00:16:33.200
to absorb it, so it might
really slow down your progress

368
00:16:33.200 --> 00:16:34.150
in the organization.

369
00:16:34.150 --> 00:16:36.400
[In comparison,] when you
show the bigger vision,

370
00:16:36.400 --> 00:16:39.800
you have something to rally
the whole organization around.

371
00:16:39.800 --> 00:16:42.620
But at the same time,
you can break it down

372
00:16:42.620 --> 00:16:47.020
into more tangible components
so you can start making progress

373
00:16:47.020 --> 00:16:49.740
while you're actually
keeping that energy

374
00:16:49.740 --> 00:16:53.680
and enthusiasm around the
organization to the north star

375
00:16:53.680 --> 00:16:54.263
that you have.

376
00:16:54.263 --> 00:16:55.638
SHERVIN KHODABANDEH:
The breaking

377
00:16:55.638 --> 00:16:57.070
down actually is quite critical.

378
00:16:57.070 --> 00:16:59.800
To build on the Lego
analogy, usually

379
00:16:59.800 --> 00:17:02.020
you get these 5,000,
6,000 pieces of Lego,

380
00:17:02.020 --> 00:17:05.790
and they come in like 20,
50, 30 boxes or little bags,

381
00:17:05.790 --> 00:17:07.300
and so you first
do this, and then

382
00:17:07.300 --> 00:17:09.819
you do that, but then
you have the whole thing.

383
00:17:09.819 --> 00:17:14.589
Well, I got one two days ago
that had 3,000 pieces in 20

384
00:17:14.589 --> 00:17:17.310
different bags, but all
the bags are unlabeled,

385
00:17:17.310 --> 00:17:19.800
so you don't know
what goes with what.

386
00:17:19.800 --> 00:17:21.750
And so then, what
we have is, like,

387
00:17:21.750 --> 00:17:25.040
3,000 pieces, and we're
trying to build a piece,

388
00:17:25.040 --> 00:17:27.760
and the analogy I'm
trying to draw here

389
00:17:27.760 --> 00:17:31.320
is, as you're building these
little capabilities that then

390
00:17:31.320 --> 00:17:34.710
come together and get stitched
together to support your bigger

391
00:17:34.710 --> 00:17:38.970
vision, how do you make sure
that these pieces are actually

392
00:17:38.970 --> 00:17:41.620
connecting rather than the
organization is looking

393
00:17:41.620 --> 00:17:44.880
to find what piece goes where,
or how does this connect,

394
00:17:44.880 --> 00:17:47.540
or do I have eight of these
instead of five of those?

395
00:17:47.540 --> 00:17:48.710
How do you avoid that?

396
00:17:48.710 --> 00:17:50.410
And that's, by
the way, something

397
00:17:50.410 --> 00:17:52.620
that goes on in a lot
of other organizations,

398
00:17:52.620 --> 00:17:55.580
where there are silos of
folks building things,

399
00:17:55.580 --> 00:17:57.450
and they don't
all come together.

400
00:17:57.450 --> 00:17:58.747
How do you approach that?

401
00:17:58.747 --> 00:18:01.330
KHATEREH KHODAVIRDI: You brought
up a very good question that,

402
00:18:01.330 --> 00:18:04.770
Shervin, basically, hey, I
cannot go to run a step from

403
00:18:04.770 --> 00:18:08.970
that vision and product strategy
for the AI and personalization

404
00:18:08.970 --> 00:18:12.350
to be a tactical component
of the puzzle that you have.

405
00:18:12.350 --> 00:18:16.410
But once you build that mental
model around the common teams

406
00:18:16.410 --> 00:18:18.580
of the problem you
are trying to solve,

407
00:18:18.580 --> 00:18:21.630
then I would say the other
reality also is that I cannot

408
00:18:21.630 --> 00:18:23.390
solve all these
problems by myself,

409
00:18:23.390 --> 00:18:27.150
or not that handful number of
people can solve these problems

410
00:18:27.150 --> 00:18:28.010
in the organization.

411
00:18:28.010 --> 00:18:30.130
You need to create
that culture, and you

412
00:18:30.130 --> 00:18:32.490
need to rally your
organization around this.

413
00:18:32.490 --> 00:18:34.250
SAM RANSBOTHAM:
That makes sense,

414
00:18:34.250 --> 00:18:37.190
because I like the Lego
analogy, but the world

415
00:18:37.190 --> 00:18:38.410
isn't as simple as Lego.

416
00:18:38.410 --> 00:18:40.530
You don't have that
perfectly labeled bag

417
00:18:40.530 --> 00:18:43.360
that you know will fit
together in the end.

418
00:18:43.360 --> 00:18:46.180
You've got to bring those
people along to pull that in.

419
00:18:46.180 --> 00:18:48.380
One of the things that we
did early in the pandemic

420
00:18:48.380 --> 00:18:51.010
was sort our giant
things of Lego

421
00:18:51.010 --> 00:18:54.200
and, when you have
those Lego that are not

422
00:18:54.200 --> 00:18:56.600
in the little bags, it's
practically impossible

423
00:18:56.600 --> 00:18:59.578
to find the right piece
to pull it back together.

424
00:18:59.578 --> 00:19:01.870
Shervin, it reminds me of
when we were talking to [Arti

425
00:19:01.870 --> 00:19:03.995
Zeighami at H&M](https://lin
k.chtbl.com/lplAIPMe),

426
00:19:03.995 --> 00:19:07.502
and he was talking about
working with individual pieces

427
00:19:07.502 --> 00:19:09.210
SHERVIN KHODABANDEH:
On the wheel, right?

428
00:19:09.210 --> 00:19:10.585
SAM RANSBOTHAM:
His analogy there

429
00:19:10.585 --> 00:19:13.740
was tightening each lug nut a
little bit as you go around,

430
00:19:13.740 --> 00:19:17.940
and there's some logic here,
too, that what's different,

431
00:19:17.940 --> 00:19:22.100
too, about Lego is that
you could solve one bag

432
00:19:22.100 --> 00:19:24.492
and move to the next bag,
but in reality, you've

433
00:19:24.492 --> 00:19:26.700
got a lot of people working
on lots of different bags

434
00:19:26.700 --> 00:19:28.147
who are moving at
different paces.

435
00:19:28.147 --> 00:19:29.980
KHATEREH KHODAVIRDI:
No, totally, Sam, and I

436
00:19:29.980 --> 00:19:33.830
would say that is why it is
very important that, in my view,

437
00:19:33.830 --> 00:19:37.180
in order to make progress
on such a complex topic,

438
00:19:37.180 --> 00:19:39.550
it's kind of a top-down
and bottom-up approach,

439
00:19:39.550 --> 00:19:43.090
and you just need to have that
check-in on a regular basis.

440
00:19:43.090 --> 00:19:46.540
The top down, I would call
it your blue-sky strategy

441
00:19:46.540 --> 00:19:49.940
around where you want
to be with AI strategy,

442
00:19:49.940 --> 00:19:53.500
and the bottom up is just mainly
like the tactical differences

443
00:19:53.500 --> 00:19:57.010
of the Lego that somehow
exist in the organization,

444
00:19:57.010 --> 00:19:58.390
or there are
different components

445
00:19:58.390 --> 00:19:59.807
that different
teams are building,

446
00:19:59.807 --> 00:20:02.560
and how you can actually bring
the two components together.

447
00:20:02.560 --> 00:20:05.860
And you're absolutely right that
the speed of the development

448
00:20:05.860 --> 00:20:07.540
and making progress
for some parts

449
00:20:07.540 --> 00:20:10.220
is more difficult than
the others as well.

450
00:20:10.220 --> 00:20:13.850
So you also coordinate the
different components together,

451
00:20:13.850 --> 00:20:15.920
so at the same time,
you are making progress,

452
00:20:15.920 --> 00:20:18.320
you can rally the
organization around that,

453
00:20:18.320 --> 00:20:21.285
but also be realistic that some
other parts are more complex

454
00:20:21.285 --> 00:20:22.410
and it will take more time.

455
00:20:22.410 --> 00:20:24.952
SHERVIN KHODABANDEH: And the
one thing that I think companies

456
00:20:24.952 --> 00:20:28.660
have that Lego pieces don't
is they still have P&Ls

457
00:20:28.660 --> 00:20:30.440
and targets and numbers.

458
00:20:30.440 --> 00:20:32.940
And so I think
that's probably why

459
00:20:32.940 --> 00:20:35.490
it's such a nuanced approach,
as you were saying, KK,

460
00:20:35.490 --> 00:20:38.180
that it's like you've got
to figure out where the most

461
00:20:38.180 --> 00:20:40.790
practical path is for
your organization,

462
00:20:40.790 --> 00:20:43.510
given all the players
and all the stakeholders

463
00:20:43.510 --> 00:20:45.270
and all the pieces,
and maybe for Arti,

464
00:20:45.270 --> 00:20:48.300
it was a bit "one
lug nut at a time"

465
00:20:48.300 --> 00:20:50.650
to get the whole thing
going, and maybe here it

466
00:20:50.650 --> 00:20:53.140
is like, "No, we've got to
get personalization perfect

467
00:20:53.140 --> 00:20:55.050
before we move on
to risk or pricing."

468
00:20:55.050 --> 00:20:58.530
And I think that's the nuances
of different organizations

469
00:20:58.530 --> 00:20:59.280
a little bit.

470
00:20:59.280 --> 00:21:00.100
KHATEREH KHODAVIRDI:
Yeah, and I would say,

471
00:21:00.100 --> 00:21:02.070
Shervin, you brought
up a really good point

472
00:21:02.070 --> 00:21:03.570
as well, that at
the end of the day,

473
00:21:03.570 --> 00:21:06.490
all the organizations
are very value-focused,

474
00:21:06.490 --> 00:21:09.620
both for the customers
and also for the company

475
00:21:09.620 --> 00:21:12.210
and the shareholder as well.

476
00:21:12.210 --> 00:21:15.060
And for me, one of the biggest
risks that people can do

477
00:21:15.060 --> 00:21:18.960
is just looking at the output
and the outcome of the P&L

478
00:21:18.960 --> 00:21:22.320
[whereas] for such big
projects like this,

479
00:21:22.320 --> 00:21:24.310
like personalization,
the reality

480
00:21:24.310 --> 00:21:27.010
is, it will definitely
take some time for you

481
00:21:27.010 --> 00:21:30.610
to actually get the true
benefit of this from the output

482
00:21:30.610 --> 00:21:33.720
standpoint of your P&L. But
what are the leading indicators

483
00:21:33.720 --> 00:21:36.910
and the KPIs you can
have to actually keep

484
00:21:36.910 --> 00:21:40.510
the team and the organization
accountable, to make progress

485
00:21:40.510 --> 00:21:43.300
so you make sure that you are
moving in the right direction,

486
00:21:43.300 --> 00:21:46.650
while it will take you more
time to see the whole benefit

487
00:21:46.650 --> 00:21:48.772
as an output in your P&L?

488
00:21:48.772 --> 00:21:51.230
Because the reality is that
the worst thing that can happen

489
00:21:51.230 --> 00:21:54.360
is that all of us know that this
is absolutely the right thing

490
00:21:54.360 --> 00:21:57.690
to do for the company, but
because you wouldn't there

491
00:21:57.690 --> 00:22:00.730
is no magic, there
is all this good work

492
00:22:00.730 --> 00:22:04.180
consistently over a
long period of time.

493
00:22:04.180 --> 00:22:07.870
There is no magic that you
see the output overnight,

494
00:22:07.870 --> 00:22:10.800
so how can you keep the team
accountable, the working team,

495
00:22:10.800 --> 00:22:13.540
to make progress, but
to the leading indicator

496
00:22:13.540 --> 00:22:16.048
that you know that eventually
it will get you to the outcome

497
00:22:16.048 --> 00:22:17.090
that you are looking for?

498
00:22:17.090 --> 00:22:17.900
SHERVIN KHODABANDEH:
For sure, right?

499
00:22:17.900 --> 00:22:20.360
As you're saying, the
worst possible thing

500
00:22:20.360 --> 00:22:27.410
would be to set a big
outcome goal for the vision,

501
00:22:27.410 --> 00:22:29.150
but for the wrong time.

502
00:22:29.150 --> 00:22:30.780
KK, this has been
quite insightful.

503
00:22:30.780 --> 00:22:34.120
I think, Sam, we should move
to the rapid-fire question

504
00:22:34.120 --> 00:22:34.700
segment.

505
00:22:34.700 --> 00:22:36.840
So this is a segment
we do, KK, where

506
00:22:36.840 --> 00:22:40.170
we ask you a bunch of
questions in rapid-fire style,

507
00:22:40.170 --> 00:22:42.320
and please tell us what
comes to your mind first.

508
00:22:42.320 --> 00:22:43.230
KHATEREH KHODAVIRDI:
Sounds good.

509
00:22:43.230 --> 00:22:45.760
SHERVIN KHODABANDEH: What has
been your proudest AI moment?

510
00:22:45.760 --> 00:22:46.610


511
00:22:46.610 --> 00:22:49.110
KHATEREH KHODAVIRDI: My proudest
AI moment goes back to grad

512
00:22:49.110 --> 00:22:50.770
school, when I was
at Carnegie Mellon,

513
00:22:50.770 --> 00:22:53.190
before this field was
this much in demand,

514
00:22:53.190 --> 00:22:56.770
and as part of my
graduate research studies,

515
00:22:56.770 --> 00:23:00.990
I was building capabilities and
platforms for energy management

516
00:23:00.990 --> 00:23:04.160
-- smart energy management
for residential buildings.

517
00:23:04.160 --> 00:23:07.297
That was the most proud
AI moment of my life.

518
00:23:07.297 --> 00:23:08.380
SHERVIN KHODABANDEH: Cool.

519
00:23:08.380 --> 00:23:12.120
What's your favorite activity
that involves no technology?

520
00:23:12.120 --> 00:23:13.620
KHATEREH KHODAVIRDI:
Playing tennis,

521
00:23:13.620 --> 00:23:16.760
because it really helps
me to focus on the moment.

522
00:23:16.760 --> 00:23:20.340
SHERVIN KHODABANDEH: What was
the first career you wanted?

523
00:23:20.340 --> 00:23:22.240
What'd you want to
be when you grew up?

524
00:23:22.240 --> 00:23:24.670
KHATEREH KHODAVIRDI:
Probably, for my first career

525
00:23:24.670 --> 00:23:27.690
I wanted to become a
professor or a pilot.

526
00:23:27.690 --> 00:23:30.500
I don't exactly remember
which one came first,

527
00:23:30.500 --> 00:23:34.180
because in our family,
education is a big piece,

528
00:23:34.180 --> 00:23:37.390
and my mom actually had
an educational career,

529
00:23:37.390 --> 00:23:39.800
but probably either a
professor or a pilot.

530
00:23:39.800 --> 00:23:40.792


531
00:23:40.792 --> 00:23:42.750
SHERVIN KHODABANDEH: What
worries you about AI?

532
00:23:42.750 --> 00:23:45.580
KHATEREH KHODAVIRDI: I would
say there have been a lot

533
00:23:45.580 --> 00:23:49.100
of conversations about
responsible AI and bias,

534
00:23:49.100 --> 00:23:50.780
and I chatted about
this earlier --

535
00:23:50.780 --> 00:23:53.780
that it's qualitative
and quantitative as well.

536
00:23:53.780 --> 00:23:55.510
I think it would
be a huge mistake

537
00:23:55.510 --> 00:23:58.100
to assume that AI
can solve basically

538
00:23:58.100 --> 00:24:00.170
all the problems without
having the right checks

539
00:24:00.170 --> 00:24:02.150
and balances in place.

540
00:24:02.150 --> 00:24:03.900
SHERVIN KHODABANDEH:
What is your greatest

541
00:24:03.900 --> 00:24:05.105
wish for AI in the future?

542
00:24:05.105 --> 00:24:05.605


543
00:24:05.605 --> 00:24:08.950
KHATEREH KHODAVIRDI: I would say
there are a lot of challenges

544
00:24:08.950 --> 00:24:11.560
that the humanities are
facing, from climate

545
00:24:11.560 --> 00:24:13.770
change or a bunch
of other stuff,

546
00:24:13.770 --> 00:24:16.350
so I really hope that more
and more people actually

547
00:24:16.350 --> 00:24:19.620
play a role in using AI
to solve those problems.

548
00:24:19.620 --> 00:24:22.100
A lot of my colleagues
actually started

549
00:24:22.100 --> 00:24:23.900
investing more of
their time and energy,

550
00:24:23.900 --> 00:24:26.490
and I really hope that
eventually in my career,

551
00:24:26.490 --> 00:24:28.105
I also can play a role as well.

552
00:24:28.105 --> 00:24:29.230
SHERVIN KHODABANDEH: Great.

553
00:24:29.230 --> 00:24:30.360
Thank you very much.

554
00:24:30.360 --> 00:24:30.540


555
00:24:30.540 --> 00:24:32.540
SAM RANSBOTHAM: So, KK,
I think that, obviously,

556
00:24:32.540 --> 00:24:36.180
the Lego analogy is going to
be interesting for people,

557
00:24:36.180 --> 00:24:40.520
but I also like what you
were saying about things like

558
00:24:40.520 --> 00:24:43.870
governance that, I think,
are fundamentally important

559
00:24:43.870 --> 00:24:46.820
for this, and the idea that you
would come on and mention some

560
00:24:46.820 --> 00:24:50.090
of that importance of things
like governance getting you

561
00:24:50.090 --> 00:24:52.530
to that scale that you need --

562
00:24:52.530 --> 00:24:54.280
I think that's
something that maybe is

563
00:24:54.280 --> 00:24:56.998
more widespread or universal.

564
00:24:56.998 --> 00:24:59.040
Thanks for taking the time
to talk with us today.

565
00:24:59.040 --> 00:25:00.150
We really appreciate it.

566
00:25:00.150 --> 00:25:01.220
Thanks for joining us.

567
00:25:01.220 --> 00:25:03.637
KHATEREH KHODAVIRDI: Thank you
guys so much for having me.

568
00:25:03.637 --> 00:25:05.190
SAM RANSBOTHAM:
Thanks for listening.

569
00:25:05.190 --> 00:25:07.090
Join us next time,
when we talk with Fiona

570
00:25:07.090 --> 00:25:09.490
Tan, chief technology
officer at Wayfair.

571
00:25:09.490 --> 00:25:11.080
Please join us.

572
00:25:11.080 --> 00:25:14.580
ALLISON RYDER:
Thanks for listening

573
00:25:14.580 --> 00:25:16.080
to Me, Myself, and AI.

574
00:25:16.080 --> 00:25:18.540
We believe, like you,
that the conversation

575
00:25:18.540 --> 00:25:20.760
about AI implementation
doesn't start and stop

576
00:25:20.760 --> 00:25:21.877
with this podcast.

577
00:25:21.877 --> 00:25:23.710
That's why we've created
a group on LinkedIn

578
00:25:23.710 --> 00:25:25.550
specifically for
listeners like you.

579
00:25:25.550 --> 00:25:28.300
It's called AI for Leaders,
and if you join us,

580
00:25:28.300 --> 00:25:30.320
you can chat with show
creators and hosts,

581
00:25:30.320 --> 00:25:33.030
ask your own questions,
share your insights,

582
00:25:33.030 --> 00:25:35.680
and gain access to
valuable resources about AI

583
00:25:35.680 --> 00:25:38.520
implementation from
MIT SMR and BCG.

584
00:25:38.520 --> 00:25:43.640
You can access it by visiting
mitsmr.com/AIforLeaders.

585
00:25:43.640 --> 00:25:46.360
We'll put that link
in the show notes,

586
00:25:46.360 --> 00:25:48.800
and we hope to see you there.

587
00:25:48.800 --> 00:25:54.000