SAM RANSBOTHAM: Launching a new AI initiative
is quite different from launching a new technology
platform like an ERP.
Find out the key differences on today's episode.
NITZAN MEKEL-BOBROV: I'm Nitzan Mekel-Bobrov from eBay,
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 information systems
at Boston College.
I'm also the guest editor for the AI and Business Strategy
Big Ideas program at MIT Sloan Management Review.
SHERVIN KHODABANDEH: And I'm Shervin Khodabandeh,
senior partner with BCG, and I also colead BCG's AI practice
in North America.
Together, MIT SMR and BCG have been
researching AI for six years now, interviewing hundreds
of practitioners and surveying thousands
of companies on what it takes to build and deploy and scale
AI capabilities across the organization
and really transform the way organizations operate.
SAM RANSBOTHAM: Shervin and I are excited today
to be talking with Nitzan Mekel-Bobrov, the chief AI
officer at eBay.
Nitzan, thanks for taking the time to talk with us.
Welcome.
NITZAN MEKEL-BOBROV: Thank you so much.
I'm excited to be here.
SAM RANSBOTHAM: You've got a relatively new position,
within the last year or so.
Can you tell us what your role is at eBay?
NITZAN MEKEL-BOBROV: Sure.
Before me, my predecessors, so to speak,
have all been chief AI scientists,
and the change to a chief AI officer was actually
a strategic one, with the recognition that AI is more
than just the machine learning models --
that AI is the engineering that it takes to "productionize"
those models at scale and, of course,
the business impact and business use cases.
We really think of AI as an end-to-end experience.
SAM RANSBOTHAM: All right; so eBay's
pretty excited about this.
What is eBay hoping to gain from this?
NITZAN MEKEL-BOBROV: I think [it's] opportunities
in every facet of our business, as probably most companies
our size would say as well.
I think for us, probably what we're most excited about,
and the reason that I joined eBay late last year --
what made me excited -- is the ability to create AI-led tools,
essentially, that we put into the hands of our customers,
both buyers and sellers, to create their own experiences
that they share with each other.
It's not just about us using AI to build things, but rather us
building AI tooling to enable our buyers and sellers to build
things, and I think that's really exciting.
SAM RANSBOTHAM: Give us an example of one of those.
NITZAN MEKEL-BOBROV: Most recently, we
rolled out our new 3D visualization experience,
which is using computer vision on the back end
to do the processing and some of the rendering.
It's not about us using this technology
to create 3D visualizations, but rather, we're
enabling, through our mobile app,
our sellers to create visualizations
of their own items at scale, and in a sort
of super easy way that doesn't require professional equipment.
SAM RANSBOTHAM: What do you think
that eBay's doing uniquely in artificial intelligence?
NITZAN MEKEL-BOBROV: I'll start with the approach,
and then I'll get into an example or two.
From an approach perspective, I think
we're going at it in a unique way
because we're a two-sided marketplace.
We worry about buyers and sellers,
and so much of our attention is on building capabilities
for our customers to use versus building experiences ourselves
directly.
Building tools for our customers to build experiences I do think
is unique.
It's unique also from a technology perspective,
because there's a different level of resilience that's
needed, and you have to test a far greater number of ways
in which it can fail when you're not actually
building the experience yourself;
you're putting tools in the hands of others to do.
So it's almost like we are software as a service
within an e-commerce company, right?
So that's sort of one aspect of it.
In terms of specific areas of focus that are maybe unique,
we are [doubling down] now on visual experiences.
We can say "computer vision," but I think of it
a little bit more broadly than computer vision as an AI
approach.
I'm thinking of it as intelligent visual experiences
that are immersive, interactive, adaptive, etc.
3D is sort of the tip of the iceberg,
but as we get deeper into the "metaverse" and deeper
and deeper into ways in which our digital platform is more
than just a place to do the commerce itself,
we think that visual experiences in real time, live interaction
between people, ways of visualizing products
that feel like they're in your hands versus just being
on the screen is something that will
be transformational for eBay.
SAM RANSBOTHAM: You've got a very interesting background:
Bookings.com, Capital One, Hearst, Boston Scientific.
Maybe you can tell us a little bit about how
you ended up where you are.
And one of the things that I want to harp on,
perhaps, is that a lot of our guests
tend to focus on the artificial part
of artificial intelligence, but your background is actually
in the intelligence part, like the actual human intelligence,
with your research on brains and how brains evolve,
so I think that's a fascinating, different angle.
You're coming much more from the intelligence part
than from the technology part.
How did you get to where you are,
and how did you learn these lessons?
NITZAN MEKEL-BOBROV: It's interesting.
I actually was planning on being a lab biologist or geneticist.
The problem was that I was terrible at the bench.
Anything that I needed to use my hands for never worked.
None of my experiments worked; everything was a flop.
So I quickly learned, "Well, I'm good with coding and computer
algorithms; maybe I'll focus on more theoretical aspects
of biology."
And then I got immersed in the world
of neuroscience and computational genomics,
and that's, by the way, how I got
introduced to neural networks.
It's interesting because me and all
my peers, that's how we entered what ended up
being machine learning applications in business,
and I had no awareness that engineering, essentially,
and computer science were playing such a big role
on the other side of a somewhat academic fence.
To me, it seemed like a very obvious progression.
As I was working on modeling how real brains, so to speak, work
and how human intelligence works,
moving from there into artificial intelligence
seemed like a very natural progression,
and I wasn't alone in this.
But then I got introduced to this whole other set
of folks coming at it from the other end.
The progression -- you're seeing me jumping around from one
industry to another -- isn't accidental.
I spent the first half of my career in health care
because it was very natural.
I did my graduate work in, essentially,
machine learning methods for genomic analysis,
because it was the human genome era, etc.,
and so I stayed in health care.
But at some point, I really wanted
to see how AI can be used in other industries,
and so I fairly purposefully moved from financial services
to online travel to e-commerce.
SHERVIN KHODABANDEH: As you have traversed the wide array
of sectors and industries, what did you find
was one of the biggest hurdles in getting AI at scale
in these organizations?
NITZAN MEKEL-BOBROV: Always people;
they always get in the way.
SAM RANSBOTHAM: [Laughs].
Spoken like a true engineer.
NITZAN MEKEL-BOBROV: It's hard to get a whole group of people
with different incentives to coordinate together
in a way that is needed.
Doing AI at scale, and in a way that
could drive transformational value,
does require a much broader set of players
playing nicely together.
And while typically everyone is onboard
that it's the right answer, the prioritization
of that versus the very immediate-term business
objectives is what typically ends up faltering.
SAM RANSBOTHAM: Part of your background
that I was interested in is that one of your dissertation
findings was that the human brain is still
evolving and getting smarter.
I think you may be in a unique position to compare
but I assume that artificial intelligence is also
getting smarter.
Tell us a little about what you see happening
in terms of these rates.
Are the machines getting smarter faster
than humans are getting smarter, or are humans
still continuing to outpace?
I'm kind of curious what your perspective is on those two
things.
NITZAN MEKEL-BOBROV: You really dusted off
some old things when you were looking at my background.
I appreciate you digging.
I think there's two things I would pick up there
that I do find interesting on a sort of philosophical level
almost.
The first one is that when we look back in history,
both recent and longer term, we look at these macro changes,
and then we assume that because those happen over long periods
of time, that they're almost episodic --
like it's not something that you would observe day to day.
But as any geneticist would tell you,
evolution is just population genetics happening over
a longer time scale.
It's not as if it's something that is episodic;
it's something that's actually continuous.
In that sense, I think it's not surprising
that humans have continued to evolve
and are continuing to evolve.
It's just the nature of biology.
What's happened in the most recent history of the human
species is that the variation in signals and the speed of that
variation being introduced is just accelerating massively,
and because of that, we're able to adapt faster and faster
and faster and in that sense become "smarter" --
maybe "more adaptive" is a better way [to say this].
And I think it's the same with what's
happening with technology now.
There's a lot of discussion about children being exposed
to digital technologies, etc., and the rate at which
technology is changing, so the signals that people are getting
-- the variation in signals -- is just getting more and more
and more, and so people's brains are becoming increasingly
adaptive.
There's somewhat of an analogy with AI there, where, really,
the amount of data -- the variation in signals that
we're feeding our models -- is continuously growing
exponentially, and so of course the models are becoming more
and more flexible and adaptive as well.
SHERVIN KHODABANDEH: Nitzan, you've
been pretty vocal about AI not being just a fancy technology
or a series of fancy algorithms just because they're
cool and awesome.
SAM RANSBOTHAM: Which they are.
SHERVIN KHODABANDEH: There has to be a purpose for it;
there has to be a real need for it.
Comment more about that meta framing of AI strategy
and its alignment with business and corporate strategy
at eBay or, in general, the purpose of AI,
because I know you've been pretty vocal about that.
NITZAN MEKEL-BOBROV: Yeah.
I've probably been pretty vocal because I learned it the hard
way, getting somewhat burned in [the] earlier years
of my career coming into a new company and thinking I knew it
all -- that I knew it better than everyone because I
understood the technology and what's going on under the hood.
And I learned the hard way, as I say,
that it's a lot more nuanced than brute-forcing
a technology onto a theoretical use case
that you might think of.
It's really important to understand the business context
in which these technologies are deployed
and understand it deeply.
So, for example, early on in my career,
when I was in financial services,
we were using AI for a lot of automation --
workflow automation.
There's huge amounts of savings in this --
hundreds of millions of dollars a year.
And to me, it felt obvious that certain applications --
for example, with speech recognition and intent
detection, etc., in the call center --
would be an ideal fit.
But it's only after I actually shadowed a number of agents
and spent probably about a good month deep-diving
into their workflows that I understood that there's so much
complexity there; that it's really
about the interaction between the human intelligence
and the machine intelligence, and it
was making assumptions that just weren't
going to bear out in real life.
So it's that move from what works
in the lab to what works in real life that is really critical.
And then, of course, thinking about what's
important to the business is not just a matter
of what's important today but really
what's in the DNA of the company,
because it takes time to not just build but deploy and get
these things up and running.
I don't know any one of my peers who's
ever been able to get anything up
and running at scale in a matter of probably
less than a year [before] you see real impact,
and honestly it's often quite a bit longer than that.
It's a marathon, it's not a sprint,
and so you really have to be conscious of what
will be the business strategy two, three,
four years down the road, not just what is on the executive's
mind today.
SHERVIN KHODABANDEH: I want to pick up
on something you said about automation, which
is one of the larger themes that AI's being used [for],
but I think you also alluded to it.
I think it's unfortunate that when most people think
about AI, they tend to think of it as the extreme case of "It's
going to replace humans," and you were talking
about the importance of what I call the middle ground, where
human and AI work together, so that human-AI interaction is
key.
Perhaps that's also one of the reasons it's so hard to scale,
because you've got to figure out how humans
will work differently with AI.
Can you share some stories or insights
about how you've done that?
Because it requires changing the mindset of humans and what
they normally do.
NITZAN MEKEL-BOBROV: I can give you a couple of examples
that I'm seeing at eBay, but honestly,
a lot of what I'm seeing at eBay, I've seen before.
The transformation stage that we're on now
is one that virtually every company is on,
and no one is fully there yet.
So if you think on the back-office side, specifically
customer service, I think that anxiety is typically most acute
there, because frankly they've seen it
before with other technologies; this isn't their first rodeo.
And in truth, over the course of the maturation
of the technology, there are individual roles
that are no longer needed.
That is true.
But it's not that humans aren't needed;
it's just that the role that they do changes.
For example, on the customer service side,
what we are doing at eBay -- we tried for the past few years
to inject AI in different places of the flow.
And it was very challenging, both from
a tech-debt perspective, because there's just
a lot of tech debt in different places
that made it hard to do that.
To make it more concrete, I'll give a real example: intent
detection.
A customer calls, and as they're talking to you,
there is a model that's picking up
on what they think that the customer's trying to answer,
so it's supposed to help the agent go
to the right pages for help, surface the right information.
Think about that poor agent, though:
He's talking to a customer who, typically, when they call,
it's not because they're happy; when
a customer calls customer service,
it's because they're frustrated.
They're trying to help them while at the same time
getting these messages on the screen,
and that ability to multitask and pay attention
to this thing that's flashing at them with intents
can be more of a distraction than a help.
What we're doing now is undertaking a more end-to-end
approach, where we're really replacing or transforming
customer service on our back end so that both the systems that
the agents are using and the systems that the models are
running on are designed together from the get-go --
so there's a much better interplay.
And we also have a lot of our designers --
much more creative than our AI folks, in many ways --
helping think through what that interplay should look like,
doing a lot of user research testing with agents on how they
would interface with customers and technology at the same
time.
SAM RANSBOTHAM: The verb you used, inject, was interesting.
You use that as saying that it didn't work to inject;
it didn't work to Band-Aid it on or just to paste it on.
SHERVIN KHODABANDEH: Well, another thing
I wanted to pick up on, Sam, is, Nitzan,
I think you made a comment like, "This
is not their first rodeo with technology;
they've seen other technologies."
And I'm interested in your views on how AI might be or how AI is
different compared to all the prior, let's say,
technologies that came and transformed functions
or processes, and whether you think there are any
misconceptions there -- like people anchoring [on], maybe,
"Oh well, this is just another ERP technology or like we did
with some other technologies; it's the same."
And I wonder whether you agree with that
or whether you think there's a misconception
to treat it the same, and what the difference
might be with anything else people
might be referencing or anchoring on
based on their prior experiences.
NITZAN MEKEL-BOBROV: Yeah, I think
any specific piece of AI technology
is comparable to an ERP system or some other version,
but as a paradigm, it's a much bigger thing.
I think the better analogy is the digital transformation.
The change from brick-and-mortar to digital
is probably more at the level of [an] analogy.
Even where we are now, I would say,
is a paradigm shift akin to the digital paradigm shift
much more than just the introduction
of some new ERP or CRM, etc.
SAM RANSBOTHAM: Nitzan, we have a new segment
where we ask our guests a series of rapid-fire questions.
Just answer with the first response
that comes to your mind.
What's your proudest moment with artificial intelligence?
NITZAN MEKEL-BOBROV: Probably on the health care side.
When I was in the Boston Scientific days,
we rolled out a feature that was predictive,
basically using signals from a pacemaker
to predict heart failure.
On average, it was 30 days in advance
of when physicians would detect it otherwise,
which is a huge lifesaving benefit.
SAM RANSBOTHAM: What worries you about artificial intelligence?
NITZAN MEKEL-BOBROV: Misuse.
Misuse by bad actors, whether it's in military operations
or in other types of nefarious activities.
If you think of deepfakes as an example,
just as it becomes more and more accessible and easy
for everyone to use, I do have concerns.
SAM RANSBOTHAM: What's your favorite activity that
involves no technology at all?
NITZAN MEKEL-BOBROV: Oh, crap.
I was about to say "photography,"
and then I realized that's not even a good example.
SAM RANSBOTHAM: No, actually we can count that.
I mean it's not strictly artificial intelligence,
so we'll give you credit for that.
SAM RANSBOTHAM: What did you want
to be when you were a child?
What did you want be when you grew up?
AI engineer at eBay?
NITZAN MEKEL-BOBROV: I actually started as a creative writing
major, so I guess writer was my initial passion.
SAM RANSBOTHAM: What's your greatest
wish for what we're going to do with artificial intelligence
in the future?
NITZAN MEKEL-BOBROV: I think, making
it easier for almost anyone to make a living pursuing
their passion.
SAM RANSBOTHAM: Nitzan, great meeting and talking with you
today.
I think one thing that is going to resonate
with a lot of our listeners is this idea
that AI implementations are different than existing
implementations like ERPs; that that siloed approach that you
might take toward a monolithic technology
is very different when you involve lots of users,
and particularly when you involve your two
platforms, with both your customers and your sellers.
Thanks for taking the time to talk with us.
We really appreciate it; thank you.
SHERVIN KHODABANDEH: Thank you for being with us.
It's been quite insightful, and we really, really
appreciate it.
NITZAN MEKEL-BOBROV: Thank you, Shervin.
SAM RANSBOTHAM: Thank you for joining us today.
Next time, we'll talk with Helen Lee, technical fellow
and regional director at Boeing.
Please join us.
ALLISON RYDER: Thanks for listening
to Me, Myself, and AI.
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