SAM RANSBOTHAM: What do Lego have
to do with how PayPal thinks about AI?
Find out on today's episode.
KHATEREH KHODAVIRDI: I'm Khatereh Khodavirdi
from PayPal, and you're listening
to Me, Myself, and AI.
SAM RANSBOTHAM: Welcome to Me, Myself, and AI,
a podcast on artificial intelligence in business.
Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of analytics at Boston College.
I'm also the AI and business strategy guest editor
at MIT Sloan Management Review.
SHERVIN KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I colead BCG's AI practice
in North America.
Together, MIT SMR and BCG have been researching and publishing
on AI for six years, interviewing hundreds
of practitioners and surveying thousands
of companies on what it takes to build and to deploy and scale
AI capabilities and really transform
the way organizations operate.
SAM RANSBOTHAM: Today, Shervin and I
are talking with Khatereh Khodavirdi.
She's the senior director of data science at PayPal.
Khatereh, thanks for joining us.
Welcome.
KHATEREH KHODAVIRDI: Thanks for having me.
Great to be here.
SAM RANSBOTHAM: You build and oversee giant data science
teams for the many different entities within PayPal.
A lot of people know PayPal, but probably some people
don't realize the extent of all the PayPal activities,
so maybe let's start there.
Tell us a bit about what PayPal does
and what all these different subentities do as well.
How are they connected?
KHATEREH KHODAVIRDI: Yes.
Obviously, a majority of the people
know PayPal through PayPal Checkout, which
is a core product that we have, and the company
started from that product.
But we have a wealth of different products,
especially on the consumer and merchant side.
We have whole suites of products to provide people
the capability on the merchant side to run their business,
from invoicing [to] having the offline capability
for financial services and then the online transactions,
and on the consumer side, starting
from peer-to-peer payment to financial services
to different types of credit and "buy now, pay
later" capability, and savings accounts.
I can go on and on, but on the consumer side,
we have a wealth of different products.
Over time, we also acquired many other companies to help us
accelerate and also be incremental in terms
of the value chain that we are creating for the consumer
and merchant, such as on the consumer side,
a couple years ago, we acquired a company --
its name is Honey -- which actually helps you to find
the best deal on the internet when you're shopping online.
I started at PayPal on the consumer side,
so I was helping the small-business group,
helping accelerate and solve the problems for our customers
through data and data science capability.
And then, over time, I supported all the merchant side
of our equation -- all the enterprise merchants,
channel partners, our relationship with Shopify,
WooCommerce, Magenta -- programmatically bringing
a merchant for us.
And then, a couple months ago, I actually
switched completely to the consumer side of the house.
So I'm new on the consumer side but super excited,
because you can leverage a lot of AI and data science
capability to solve a lot of interesting problems
in this domain.
SHERVIN KHODABANDEH: That's great.
So obviously, PayPal is a big, multifaceted company
with different business and subdivisions,
and you talked about those.
But since you're in the consumer group, let's talk about that.
Share with us a bit how AI's being used in the consumer
business to drive the themes you want with your consumers
and what use cases is AI being used for.
KHATEREH KHODAVIRDI: We can actually
bring all the different accounts together,
so we have a point of view around the user interactions
with us.
For example, if Shervin has a relationship to Honey with us,
but at the same time he's using peer-to-peer or the Checkout
product or our credit capability,
how can we make sure that we can actually
look at Shervin's relationship with PayPal through one lens?
So that is, I would say, one of the fundamental problems we are
trying to solve as a company.
But the piece I'm personally super excited about is around
the fact of understanding the customer journey with us
and how we can leverage personalization and AI
to actually solve customer use cases --
that "What are the jobs they are coming to PayPal to get done?"
and "How can we show them the personalized and relevant
messages to help them?"
SO THINK ABOUT IT: Internally, we always said, "Hey,
there are a bunch of happy paths with PayPal,
and there are a bunch of sad paths with PayPal.
How can we migrate people from one happy path
to a happier path with us, and how can we avoid the sad path
with the customer?"
And I truly believe that this is basically an AI capability
that we need to develop for customers.
If you build that, it will unlock a humongous amount
of value for our consumers and for us as a company.
SHERVIN KHODABANDEH: Let's talk about some of those paths.
You said, "What kind of jobs are people trying to get done?"
so let's talk about that.
Tell us how personalization could be helpful there.
KHATEREH KHODAVIRDI: Think about it this way:
Imagine Shervin is one of the people that is actually
using PayPal Checkout to shop through different merchants
throughout the internet as well.
As you can imagine, once we acquired Honey,
we also had a whole wealth of the coupons that are
[available] out there, and over time,
if you get a better sense around what type of categories or what
type of merchant Shervin is interested in,
we can actually show Shervin the right deals at the right time.
For example, it's back-to-school time,
and we know that historically, at this time,
you shop in this type of category.
Currently, you know, Target or Walmart
or some of the top merchants for back to school
are running these deals, and then
we remind you and show you relevant and personalized deals
to actually drive activity with us.
Or you might use Windmill on a weekly or biweekly basis
to send somebody who cleans your house some money.
We can remind you that, "Hey, Shervin, it
looks like you're doing this," and through just
one-click touch, you go and do that.
So basically, we become part of Shervin's life
and understand what type of activity
Shervin is trying to do and make it much easier for him.
The other part of it that we are all super excited about it
is tracking.
When you shop online to a different retailer,
one part of it is, you want to track that order
and see where your order is and when you are getting it.
We also want to make it much easier --
you can actually get the notification and see where
your order is, and you can look at your whole commerce activity
and financial activity in one place.
I would say for me, also, the other part is that, yes,
I'm in a data function and we build models,
and I look at the outcome of our model,
but also the more important part for me is --
I always call it qualitative and quantitative.
I would say, "Hey, I want a sample of some
of the customers in this group.
I want to understand their journey, the activity they
had with us," or, you know, actually
attend the user research studies that we
are doing with customers, so first-hand, I actually
hear the challenges and the problems
we are trying to solve.
Because for me, the most important part is that I just
cannot go and in isolation build the model and solve all
the problems.
It's really doing the qualitative
and quantitative aspects and learning from each other
and improving it over time.
SHERVIN KHODABANDEH: That's very helpful.
I have to imagine that being more of a tech company than,
let's say, a financial services company,
that some of these challenges are actually
a lot easier for you to deal with than it would be,
let's say, for a gigantic global bank
that's trying to personalize across products and business
lines and all that.
Share with us some of the challenges.
What's difficult about what you need to do?
KHATEREH KHODAVIRDI: I think one of the biggest challenges we
have is that some of the other companies that we acquired
over time, each of them is using different data stacks,
so basically migrating all of them to one data lake
and having one kind of technology
to use across the board and being
able to [build] that common data layer platform end to end,
and understanding all the touch points and all the data
points we have with the customer.
So having that common data technology platform
is one of the common challenges we have internally.
SAM RANSBOTHAM: That's huge, I think, for everybody.
I remember my first experience with this.
Back in my past life, I used to work at the United Nations,
and I was working with databases, and I looked down
and we had just literally dozens of databases
we were supporting, and I asked, "Well, which is the standard?"
And they said, "Well, this is the standard."
And, I said, "Well, what are all the rest of them?"
"Well, those were the standards then."
And so you're in that same situation when you're acquiring
companies, where you're pulling together lots
of technology stacks [but] you don't
want to just kind of rip them out and start over.
How do you manage that process?
How do you get those in a cohesive data science process?
KHATEREH KHODAVIRDI: Yeah, so, I would say, Sam,
you brought a very good question as well --
that it's not only the technology problem aspect
of it.
I call it the data governance aspect of it
as well: what the definitions are,
and how different people define and look at different things
differently, and how you can define
that common language across the board
internally within the company as well.
So that is why we are trying internally to develop the best
practices that every new company that we have here
are the steps one to end that we are going through
to incorporate them as part of the rest of the data assets
we have for the company.
But you can imagine it's not an easy exercise.
It's a humongous task.
But that data governance aspect of is it also very important,
because when you are bringing different components together,
you need to take a step back and look
at the definition from a different lens as well
and see if those definitions still
are relevant in the new construct or not.
SHERVIN KHODABANDEH: Let's talk about teams a little bit.
You're talking about a series of challenges
with personalization, with other use cases,
and then the capabilities that are required to get that done,
from data coming together and identity resolution
and many, many other things.
Tell us about the team.
Clearly, they have to have very strong technical capabilities,
but what else do they need to have to work in that
kind of an environment?
KHATEREH KHODAVIRDI: I would say this area is
one of the areas that is so multidisciplinary,
and you can imagine the different types of problems
you want to solve need different types of skill sets
and these types of talent.
So I always said that in data science, AI,
or the overall data field, one of the things that
is really important is diversity of talent.
And by diversity of talent, I don't only
mean diversity of gender or background, which
is very important, but diversity of thought leadership,
diversity of problem-solving, diversity of technical skill
set, because at the different stages of the problem,
basically, you need to practice a different type of muscle
in order to get the desired outcome.
So, for example, one of the areas that I really feel [is]
underappreciated in the data world is business acumen --
people who can actually tackle the problem through a very
structured framework and be able to synthesize
the recommendation and the "so what" to the business.
Because the worst thing that can happen is that you look
at your data team and you feel like they are building a bunch
of black boxes for the rest of the organization,
and you're not investing in the last mile --
that people actually translating the "what" and the "why"
and the "so what" to the business group,
to the product group, and to the rest of the organization.
So you would not get the adoption
that you are hoping from the capabilities
that you are building.
So the way I'm looking at it is that we are actually also
building a product organization who supports
the personalization and AI, because like any other product
development cycle, you basically have a product strategy behind
this -- that we are not practically building the AI
model to solve specific use cases.
We actually take a step back, understanding our consumer
personas -- what are the jobs they are coming to us to get
done? -- and build a product road map and product vision
around this and tackle this problem in a cross-functional
fashion instead of just internally within the data
group.
SAM RANSBOTHAM: Is it harder to get that last mile
with AI-type projects?
Is that something the people have a harder time
understanding?
Is it something that is harder for people to relate to?
KHATEREH KHODAVIRDI: It might be a little bit harder
because of just the scale of the problems that you are trying
to solve here, because people cannot relate to it when
you're talking about millions of customers.
So for me, it's how I can actually break down the problem
and solve smaller use cases through AI
to create that adoption and championship
in the organization [so] that it will help me
to solve the biggest problem.
It's a humongous task, because I'm not only
talking about the product touch points that we have.
I'm talking about every touch point that the customers have
with us, either through customer service, through risk --
through all the different functions within the company.
Rallying all of those cross-functional functions
around solving this problem would be much harder,
versus my approach is that I will first
start with solving it within the product organization,
understanding all the touch points with all the products,
understanding how we can understand the product task,
and personalizing that component.
And then you can add an additional layer --
like bring the risk component -- with each of the product
components, then you add the customer service.
I would think about it more of a Legoland --
that at the end of the day, we will have an AI Legoland
for PayPal.
But right now, the way I am attacking to solve this problem
is to build each of the individual Lego pieces
with the hope that I can orchestrate and build
a Legoland and it won't become like bunch of separate Lego
that are not orchestrated to solve the common equation.
SHERVIN KHODABANDEH: I love the Lego analogy,
because my kids are totally into Lego
and we have probably like 900,000 different pieces
of Lego going on at any given time.
And if you just look at it in isolation,
you think, "OK, this is all we're doing."
But of course, you've got to start there,
and then the pieces come together.
So then my question to you is, as with Lego,
when I see my 12-year-old or eight-year-old building stuff,
and I'm looking at it in isolation,
I might not have a full sense of the vision [of what]
the whole thing is going to be.
So, I might say, "Oh, this is nothing,"
or "What are you building?
It's like a small piece.
Didn't you do something like this before?"
And then they bring out the box with, like, 8,000 pieces
that's going to look like this.
And then, I go, "Uh-huh."
How are you doing the big "uh-huh"
here at PayPal so people don't lose sight of the big vision
and don't get myopic about the little things that it takes
to get there?
KHATEREH KHODAVIRDI: You can imagine there is no shortage
of individual use cases -- that there are many individual AI,
or whatever you name it, capabilities within
the organization.
But when you take a step back, you
do not have that guiding principle
to see how they can help you to actually build that Legoland.
And actually I want to tackle this problem
in a reverse order; first I want to take a step back and say,
"Hey, what will the blue sky look like in terms of the AI
capability and personalization for us?"
So building that product strategy and vision around it,
and then trying to solve backward,
and then breaking it down into smaller pieces of the component
Lego and being very prescriptive around what are the key
problems each of these Lego will try to solve,
and why we are building each piece of the Legoland --
what will be the "so what" to the organization.
Then you can actually build something
because if you show that whole vision to everybody,
it might be a little bit too much for some of the people
to absorb it, so it might really slow down your progress
in the organization.
[In comparison,] when you show the bigger vision,
you have something to rally the whole organization around.
But at the same time, you can break it down
into more tangible components so you can start making progress
while you're actually keeping that energy
and enthusiasm around the organization to the north star
that you have.
SHERVIN KHODABANDEH: The breaking
down actually is quite critical.
To build on the Lego analogy, usually
you get these 5,000, 6,000 pieces of Lego,
and they come in like 20, 50, 30 boxes or little bags,
and so you first do this, and then
you do that, but then you have the whole thing.
Well, I got one two days ago that had 3,000 pieces in 20
different bags, but all the bags are unlabeled,
so you don't know what goes with what.
And so then, what we have is, like,
3,000 pieces, and we're trying to build a piece,
and the analogy I'm trying to draw here
is, as you're building these little capabilities that then
come together and get stitched together to support your bigger
vision, how do you make sure that these pieces are actually
connecting rather than the organization is looking
to find what piece goes where, or how does this connect,
or do I have eight of these instead of five of those?
How do you avoid that?
And that's, by the way, something
that goes on in a lot of other organizations,
where there are silos of folks building things,
and they don't all come together.
How do you approach that?
KHATEREH KHODAVIRDI: You brought up a very good question that,
Shervin, basically, hey, I cannot go to run a step from
that vision and product strategy for the AI and personalization
to be a tactical component of the puzzle that you have.
But once you build that mental model around the common teams
of the problem you are trying to solve,
then I would say the other reality also is that I cannot
solve all these problems by myself,
or not that handful number of people can solve these problems
in the organization.
You need to create that culture, and you
need to rally your organization around this.
SAM RANSBOTHAM: That makes sense,
because I like the Lego analogy, but the world
isn't as simple as Lego.
You don't have that perfectly labeled bag
that you know will fit together in the end.
You've got to bring those people along to pull that in.
One of the things that we did early in the pandemic
was sort our giant things of Lego
and, when you have those Lego that are not
in the little bags, it's practically impossible
to find the right piece to pull it back together.
Shervin, it reminds me of when we were talking to [Arti
Zeighami at H&M](https://lin k.chtbl.com/lplAIPMe),
and he was talking about working with individual pieces
SHERVIN KHODABANDEH: On the wheel, right?
SAM RANSBOTHAM: His analogy there
was tightening each lug nut a little bit as you go around,
and there's some logic here, too, that what's different,
too, about Lego is that you could solve one bag
and move to the next bag, but in reality, you've
got a lot of people working on lots of different bags
who are moving at different paces.
KHATEREH KHODAVIRDI: No, totally, Sam, and I
would say that is why it is very important that, in my view,
in order to make progress on such a complex topic,
it's kind of a top-down and bottom-up approach,
and you just need to have that check-in on a regular basis.
The top down, I would call it your blue-sky strategy
around where you want to be with AI strategy,
and the bottom up is just mainly like the tactical differences
of the Lego that somehow exist in the organization,
or there are different components
that different teams are building,
and how you can actually bring the two components together.
And you're absolutely right that the speed of the development
and making progress for some parts
is more difficult than the others as well.
So you also coordinate the different components together,
so at the same time, you are making progress,
you can rally the organization around that,
but also be realistic that some other parts are more complex
and it will take more time.
SHERVIN KHODABANDEH: And the one thing that I think companies
have that Lego pieces don't is they still have P&Ls
and targets and numbers.
And so I think that's probably why
it's such a nuanced approach, as you were saying, KK,
that it's like you've got to figure out where the most
practical path is for your organization,
given all the players and all the stakeholders
and all the pieces, and maybe for Arti,
it was a bit "one lug nut at a time"
to get the whole thing going, and maybe here it
is like, "No, we've got to get personalization perfect
before we move on to risk or pricing."
And I think that's the nuances of different organizations
a little bit.
KHATEREH KHODAVIRDI: Yeah, and I would say,
Shervin, you brought up a really good point
as well, that at the end of the day,
all the organizations are very value-focused,
both for the customers and also for the company
and the shareholder as well.
And for me, one of the biggest risks that people can do
is just looking at the output and the outcome of the P&L
[whereas] for such big projects like this,
like personalization, the reality
is, it will definitely take some time for you
to actually get the true benefit of this from the output
standpoint of your P&L. But what are the leading indicators
and the KPIs you can have to actually keep
the team and the organization accountable, to make progress
so you make sure that you are moving in the right direction,
while it will take you more time to see the whole benefit
as an output in your P&L?
Because the reality is that the worst thing that can happen
is that all of us know that this is absolutely the right thing
to do for the company, but because you wouldn't there
is no magic, there is all this good work
consistently over a long period of time.
There is no magic that you see the output overnight,
so how can you keep the team accountable, the working team,
to make progress, but to the leading indicator
that you know that eventually it will get you to the outcome
that you are looking for?
SHERVIN KHODABANDEH: For sure, right?
As you're saying, the worst possible thing
would be to set a big outcome goal for the vision,
but for the wrong time.
KK, this has been quite insightful.
I think, Sam, we should move to the rapid-fire question
segment.
So this is a segment we do, KK, where
we ask you a bunch of questions in rapid-fire style,
and please tell us what comes to your mind first.
KHATEREH KHODAVIRDI: Sounds good.
SHERVIN KHODABANDEH: What has been your proudest AI moment?
KHATEREH KHODAVIRDI: My proudest AI moment goes back to grad
school, when I was at Carnegie Mellon,
before this field was this much in demand,
and as part of my graduate research studies,
I was building capabilities and platforms for energy management
-- smart energy management for residential buildings.
That was the most proud AI moment of my life.
SHERVIN KHODABANDEH: Cool.
What's your favorite activity that involves no technology?
KHATEREH KHODAVIRDI: Playing tennis,
because it really helps me to focus on the moment.
SHERVIN KHODABANDEH: What was the first career you wanted?
What'd you want to be when you grew up?
KHATEREH KHODAVIRDI: Probably, for my first career
I wanted to become a professor or a pilot.
I don't exactly remember which one came first,
because in our family, education is a big piece,
and my mom actually had an educational career,
but probably either a professor or a pilot.
SHERVIN KHODABANDEH: What worries you about AI?
KHATEREH KHODAVIRDI: I would say there have been a lot
of conversations about responsible AI and bias,
and I chatted about this earlier --
that it's qualitative and quantitative as well.
I think it would be a huge mistake
to assume that AI can solve basically
all the problems without having the right checks
and balances in place.
SHERVIN KHODABANDEH: What is your greatest
wish for AI in the future?
KHATEREH KHODAVIRDI: I would say there are a lot of challenges
that the humanities are facing, from climate
change or a bunch of other stuff,
so I really hope that more and more people actually
play a role in using AI to solve those problems.
A lot of my colleagues actually started
investing more of their time and energy,
and I really hope that eventually in my career,
I also can play a role as well.
SHERVIN KHODABANDEH: Great.
Thank you very much.
SAM RANSBOTHAM: So, KK, I think that, obviously,
the Lego analogy is going to be interesting for people,
but I also like what you were saying about things like
governance that, I think, are fundamentally important
for this, and the idea that you would come on and mention some
of that importance of things like governance getting you
to that scale that you need --
I think that's something that maybe is
more widespread or universal.
Thanks for taking the time to talk with us today.
We really appreciate it.
Thanks for joining us.
KHATEREH KHODAVIRDI: Thank you guys so much for having me.
SAM RANSBOTHAM: Thanks for listening.
Join us next time, when we talk with Fiona
Tan, chief technology officer at Wayfair.
Please join us.
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