SAM RANSBOTHAM: Even digital-fist companies
approach technology implementations with caution,
ensuring they limit their exposure to risk.
In today's episode, find out how one e-commerce retailer thinks
about implementing -- and scaling --
AI.
FIONA TAN: I'm Fiona Tan from Wayfair,
and you're listening 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 excited to be joined by Fiona Tan.
Fiona's the CTO at Wayfair.
Fiona, thanks for joining us.
Welcome.
FIONA TAN: Thank you for having me.
SAM RANSBOTHAM: Let's get started.
We've got listeners throughout the world that may not be
as familiar with Wayfair as Shervin and I are --
we could probably look around our rooms and find Wayfair
items -- so can you start by describing Wayfair?
What does Wayfair do?
FIONA TAN: For sure.
And first of all, thank you for being customers;
[I'm] always happy to have customers to talk to.
Basically, we are a digital-first retailer
in the home goods category.
We've also augmented that now with some stores,
opening our second store in the Boston, Massachusetts,
area, and so [I'm] really excited
about that as well, as we move toward being
an omnichannel retailer.
SAM RANSBOTHAM: That's the opposite direction
than most people go.
FIONA TAN: You know, it is, but it's actually kind of neat;
it does afford us some interesting ways of approaching
it because we are digital first.
I think, hopefully, you'll find that there
are some really nice ways that we are able to tie
in the digital aspects.
You go into the store, you see what's there,
but you can also see the rest of our catalog
in a way that's hopefully really useful and a little bit
different than the typical brick-and-mortar shopping
experience.
Part of it, too, that's really interesting about Wayfair
and our approach to AI/ML -- a lot of ...
it's under the covers, and you don't realize,
but what is actually powering the entire experience that you
have as a customer -- and then also for our suppliers --
there's a lot of machine learning and AI behind it.
It's not that visible, but it is actually
powering everything that we do.
For example, there's a lot around
trying to understand the customer's intent.
And we do that inasmuch as what they can tell us in the search
strings, etc., but also based on where they've looked
and how much time they've spent looking at something
versus another thing.
So we try to build up our customer graph,
and then we also look at the products, the items
that we're listing on our site.
Because of the category that we're in,
we don't really have as [many] branded items.
So it's, how do we use AI and ML to upload as many items
as possible -- and we have tens of millions of items
on our site -- and be able to get as much product information
as possible?
Some of that we get from our suppliers,
but [for] a lot of the product information,
we are using AI and ML to actually glean [it]
from the photos they give us, from the text that they
give us, to be able to form our product understanding.
So we build a customer graph, we build a product graph,
all using AI/ML, and then we do that matchmaking.
When you're on our site and you're looking for something
we can personalize based on what we already know about you.
That's the magic: How do we find you
that perfect couch when you can't really describe it
to me in a very succinct way?
SHERVIN KHODABANDEH: Fiona, tell us
a bit about your own journey.
How did you get into technology, and how'd that evolve?
FIONA TAN: I went to MIT as an undergrad,
and I took my first computer science class, 6001,
and fell in love with it.
And it's one of those things I look back [on] and I'm like,
"I'm so fortunate to find something that I enjoy doing,"
and I realized, "They're going to pay me money for it."
And this was one of those really fortuitous moments,
I think, when I realized, hey, I've
always loved solving problems, I've always
loved optimizing whatever I was doing,
and here's a field where I get to do that in practice.
I did my master's as well in computer science,
and then I've worked in technology my whole career.
I started out building out enterprise software, so really
looking at, how do you build solutions that can be adopted
by any and all industries?
I spent some time at Oracle, starting out, and then [was]
at a company called Tipco for a long time,
essentially building enterprise platforms.
And then I moved over to Walmart and now Wayfair.
The platform mindset that I got from the first two-thirds
of my career is still very prevalent.
Even when you're building a very specific use case,
you still want to try to use that platform mindset,
because it then allows you to build out solutions in a much
more scalable and sensible way.
It's still relevant, and then you
get to focus on a very specific problem set.
You get to be much more business-outcome-based.
And then those are the things that
are really different about a very specific retail use
case, for example, versus building out an enterprise
platform.
In one case, you're very, very close to the customer,
and you get to focus on [solving]
a particular problem set but still building it, I would say,
with the right architecture [and]
platform mindset that allows you to then scale, whether it's
horizontally or vertically.
I think that's one of the things that I
have found that has been useful: my background in enterprise
platforms.
SHERVIN KHODABANDEH: Great.
Can you comment a little bit about the overall philosophy
of how you're thinking about use cases?
FIONA TAN: Absolutely.
And I think that is another key tenet of how we operate.
At Wayfair, we actually started out in marketing.
This was an area where we felt like AI and ML could really
play a big part.
We say, hey, look -- from a marketing standpoint,
the bidding, and how much I should bid for, and where I
should spend the money from a channel perspective ...
those are things that we feel like we can control and are
lower risk.
If we get it wrong, maybe we pay a little bit more for an ad
than we needed to, but these were areas
that we actually invested in first, because we could
learn and use AI and ML for that and control the amount of risk
that we were following.
And then once we figured that out,
once we got more into the use of ML,
we then looked at other areas we could apply it to.
So how do we apply similar technologies
in terms of our pricing and demand generation?
How do we expand that out to the rest of our supply chain,
the catalog, and understanding about products into search,
and an understanding of the customers?
So if you look at where we apply AI and ML now,
it's much more prevalent, but we started out
with this very specific use case around marketing and customer
acquisition.
That was the first place that we started using AI and ML.
SHERVIN KHODABANDEH: You have these two rules --
sort of the rules of thumb [that could serve as] good advice
to many, many retailers and in other industries as well --
what are the two rules of what use cases lent themselves more
to AI/ML?
Tell us more about that.
FIONA TAN: We use a little bit of a risk framework
around what is the reputational risk
or other risk to the company if we get it wrong.
Back to marketing, a lot of it's going
to be, if we get it wrong, we pay a little bit more.
Other areas where we don't go fully automated because we're
a little bit more concerned from a risk perspective
could be, for example, product information or product quality.
I think we try to do that as much as possible.
But then, to some degree, this is also
something where we would include the humans in the loop
to do that extra level of checks.
So we don't fully automate, because if we get that wrong,
that is problematic.
We use that as a way for us to first figure out
what we lean into first.
If you can automate fully and control the risk,
that's where we feel like we can go a little faster.
And then, other areas we might go in but then also involve
the humans in the loop -- the controls to make sure that we
have that extra level of checks.
So that's one way that we look at it.
The other is around data, and this is obviously something
that I think a lot of other technology organizations
are also thinking about when they think about ML: Are we
ready from a quantity and availability of data
[perspective], as well as the usability of the data?
I think that's something, frankly,
that a lot of companies, struggle with: making
sure that there's one source of truth versus now
there's five people who use the source of truth,
have done some adjustments to it,
and now I've got five things that
are sort of similar to that first source of truth,
and the manageability of it becomes a bit of an issue.
We look at where I've [got] good, stable sources of truth.
SHERVIN KHODABANDEH: You talked about [keeping the] human
in the loop, and this idea -- as fundamental or simple as it
sounds --
I think that it's still a misconception for many,
because many still think it's not AI
if there's a human involved, or it
must be that it does everything all by itself,
otherwise it's really, really not
full AI, which Sam and I have looked a lot at this
across companies, and what we've seen
is, there's a whole bunch of use cases
that you're either not going to approach at all if you expect
fully automated, or would be suboptimal or substandard
or, as you said, highly risky.
What are some examples of humans in the loop?
I can imagine there might be ideas where humans and AI work
together, and AI has some ideas, and the human says,
"Well, maybe not quite this one.
Let's try this other idea."
Is that also something prevalent in your organization?
FIONA TAN: Yeah, it is.
It is actually quite prevalent, and this
is the part where it's really much more
business-driven and pragmatic in terms of our application.
And so, to some degree, there's probably
someone out there who might say, "Hey, look, that's not
pure AI or pure ML, because you are involving
this human or that human."
But in our case, it doesn't really matter.
We're trying to achieve a strong outcome from a business
perspective.
The way we've thought about it is, sometimes
we use the automation and AI/ML to narrow down the choices.
So we do some of the work initially,
and then we narrow it down to, say, maybe five, six,
whatever it is -- a smaller number --
that we can bring the experts, the humans,
in to make that final decision.
So quality is one of them.
The other is style -- that's something that's always
a little tricky to be able to get right.
If we can narrow it down, it just
makes the human part a lot easier as well,
but then also very valuable.
Because some of those [decisions]
are sometimes pretty nuanced.
I'm actually not a style expert, so I probably couldn't tell you
the difference between, like, two, three styles,
and there's a lot of places where they cross over,
etc., right?
And so that's when you have an expert.
And we do have design experts on staff
that can help us with some of those definitions.
And then in our space, things change.
Styles change -- what's in, what's not, and all that.
So, again, having the ability to bring in humans in the loop
is super interesting and helpful to us.
SAM RANSBOTHAM: And people, as you mentioned
at the very beginning, may not even
know that you're using AI or using machine learning.
Shervin and I are kind of fascinated
with this at the moment, because it
seems like there's a whole lot of uses that people once it's
out there and you can actually do it practically, it couldn't
possibly be artificial intelligence, because that that
is a mythical being, but once you can do it,
well, then it seems normal.
You mentioned how widespread use of it is throughout
your organization, and actually --
how many people in your organization would say
they're using AI?
FIONA TAN: Yeah, I think that's the part
around we try to build it into the fabric of the technology
organization.
So we have a data science team, but they work very closely
with the software engineers.
We want to, again, even within the technology organization,
make sure that the scientists who
are building the models, that it's something that's actually
production worthy.
You want to make sure that the teams are well integrated ...
so even if you have a software engineer,
maybe they're not a data scientist,
but they work very closely with them and they understand what
the needs are.
And that's what we found to be successful.
SAM RANSBOTHAM: So enough of this pragmatic stuff, though.
FIONA TAN: [Laughs.]
SAM RANSBOTHAM: I mean, you've got ML and AI
throughout the organization; you're using it lots of places.
What's next?
What are you excited about?
What's the fun thing that's coming up next?
FIONA TAN: There's a bunch of things
that we're trying to do as well.
We're also looking at innovations
in terms of incorporating other tactile-type capabilities.
We have a small group that plays around
with looking out for technology, whether it's
advancements from mobile apps and the native capabilities
of the devices that will allow us to do more.
It's looking forward toward embedding
more augmented reality into our shopping experience,
for example.
One of the things that we looked at also
was, there was some technology out there that
was allowing us to get you to almost "feel" the thing
that you are trying to buy.
The other thing, too, is, because we are heavily invested
in imagery -- imagery is a big part of what sells in the home
category, and we have a lot of 3D models, etc.,
for a lot of the items that we sell --
how do we then potentially create a digital twin
of your home, for example, in the cloud,
so that you can almost furnish your home virtually to match
what you have in real life?
And you can use that to influence what
you're buying in real life.
Or maybe that's your home in the metaverse,
and you're going to furnish it a completely different way.
There's a lot of really interesting technology
and concepts out there that we are
trying to keep abreast of while we're
continuing to be practical and pragmatic, but yes.
SHERVIN KHODABANDEH: A good portfolio
of high risk and high reward, and practical stuff.
It's very good.
SAM RANSBOTHAM: The haptic things
you mentioned seem particularly interesting.
We do focus a lot on visual.
We've made so many advances on visual and sound.
FIONA TAN: Yeah, but not so much feel.
SAM RANSBOTHAM: What's the Pantone color set
equivalent of haptic or touch?
It seems like if we had some of those sorts of things,
where I could have an array at home,
and I could touch these four things,
and this is what this couch feels like, I feel like that's
kind of interesting.
I'm not sure if I want to go there with smell,
because I'm not sure if I want that Pantone array of smells
in my home, but it's exciting to see that you're thinking about
these, let's say, nontraditional or non-, you know,
first two primary senses that we tend to focus on.
FIONA TAN: Yeah.
Yeah.
SAM RANSBOTHAM: We have a segment where
we ask you a series of rapid-fire questions,
and you're just supposed to say the first thing that
comes to your mind.
FIONA TAN: OK.
[Laughs.]
SAM RANSBOTHAM: Are you ready?
FIONA TAN: Yeah.
OK.
We'll try.
SAM RANSBOTHAM: What's your proudest AI moment?
FIONA TAN: I think one of the ones that I'm most proud of is,
as we built out the AI capabilities
across different functions, we have one particular capability
that we're now building, which is what we call geo-sort.
Basically, it allows us to take advantage of the capabilities
that we have that are foundational --
on the understanding of a product,
the understanding of the customer --
and then being able to take that,
and then we also factor in where products are located.
Basically, we have a sort order based
on my understanding of your intent, my best understanding
of all the products that we have,
and then we look at where the product is located,
what it costs to ship for you, and then
we do another round of optimization around that.
In a way, the reason why I'm proud of it
is, because we built the foundational capabilities,
we can now deliver second-order solutions on top of that.
And that's very specific to us, but I'm sure a lot of companies
are at that point, hopefully, too,
where they have foundational capabilities,
and they now figure out, "Oh, there's
a second-order solution I can now devise because I've
laid the groundwork."
SAM RANSBOTHAM: Yeah, you spend a lot of time and effort
on those foundations, and getting
to use that foundation seems fun,
because some of the foundation may be in the suffering
category of getting your data house in order
and getting things ready for those next things.
FIONA TAN: Yeah.
SAM RANSBOTHAM: OK.
What worries you about AI?
FIONA TAN: In our application, I would say,
it's part of back to the whole risk category
that we talked about.
We feel good about the way that we are using AI.
I don't think we are anywhere close to the boundaries
of where we start to worry, and part of it
is just around we'll be looking at how people are using it,
but it's all anonymized.
We're trying to figure out trends.
For example, when we do marketing,
we look at what channels are successful,
but it's not going into the details of who bought where.
But [it's] something that I think in general people
do need to think about in terms of how you're
using the data that you have and making sure
that it's at the aggregate, and how do you make sure
that it continues to be so?
SAM RANSBOTHAM: What's your favorite activity that
does not involve technology?
FIONA TAN: I have two current favorite activities.
I'm learning how to golf, and I think
that's going to be a lifelong endeavor, because it
seems like it's very hard.
And I enjoy cooking.
It's funny, because I approach cooking the same way
that I do with technology.
I'm always optimizing.
So I never follow one recipe; I pick out
the best parts of, like, six different recipes, and then
and it's funny because people ask me, "Well,
which one did you follow?"
I'm like, "Ah, it's actually very complicated.
Let me explain.
You do this and you do that and you trade off "
It's how I think, so that's how I cook and how I bake as well.
SAM RANSBOTHAM: It's an ensemble model approach, right?
FIONA TAN: Yeah.
SAM RANSBOTHAM: It's just like a random forest.
You just reach into the bag -- you're grabbing another
selection out and building an ensemble recipe.
FIONA TAN: Exactly.
SAM RANSBOTHAM: What's the first career that you wanted?
What did you want to be when you grew up?
FIONA TAN: My first career --
I wanted to be a vet.
Isn't that what most children want to be initially?
SAM RANSBOTHAM: Yeah, and then you
took your first computer science class and everything changed.
FIONA TAN: Yeah, exactly.
And then I was hooked.
SAM RANSBOTHAM: What's your greatest
wish for the future for AI?
What do you hope we're going to gain
from artificial intelligence?
FIONA TAN: I hope that we can continue
to use it and make just really good practical applications
of it.
I think there's so many, and obviously we're
using it in a commerce and retail arena, [but there are]
a lot of use cases where we can help with understanding health
care, etc.
There are just so many applications of it.
I'd love for it to just be prevalent
and for folks to continue to practice it,
and, again, it's looking at the data
and helping us understand things that we might not
have understood just from an analytical perspective.
I think that's the part around the AI part
of it, is it may not be things that we might think
of ourselves, but looking for solutions in a very novel way.
SAM RANSBOTHAM: Well, Fiona, thank you
for taking the time to talk with us.
I think a lot of the things you said about pragmatic approaches
and [being] careful about risk, I
think those are things that apply
in lots of different places, even if you're not
digital first and physical second.
I think those things apply to lots of people,
and I think people will learn from that.
Thank you for taking the time to talk with us today.
FIONA TAN: Yeah, thank you for having me.
I enjoyed it.
SAM RANSBOTHAM: That's a wrap on Season 5.
Thanks for listening.
We'll be back early next year with new episodes.
In the meantime, please follow Me, Myself, and AI
on LinkedIn to stay up to date and to be
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ALLISON RYDER: Thanks for listening
to Me, Myself, and AI.
We believe, like you, that the conversation
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That's why we've created a group on LinkedIn
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It's called AI for Leaders, and if you join us,
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ask your own questions, share your insights,
and gain access to valuable resources about AI
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You can access it by visiting mitsmr.com/AIforLeaders.
We'll put that link in the show notes,
and we hope to see you there.