Transforming Transactions With Technology: eBay’s Nitzan Mekel-Bobrov
27:14 Share

Today, we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast.

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. I'm Nitsan Mekel-Babrog from eBay, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and 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.

And I'm Sherwin Kodobande, senior partner with BCG, and I also co-lead 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.

Sherwin and I are excited today to be talking with Nitsan Mekelbovov, the Chief AI Officer at eBay. Nitsan, thanks for taking the time to talk with us. Welcome. Thanks so much. Excited to be here. You've got a relatively new position within the last year or so. Can you tell us what your role is at eBay? Sure. Before me, my predecessors, so to speak, have all been Chief AI Scientists.

And the change to 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.

All right. So eBay is pretty excited about this. What is eBay hoping to gain from this? I think 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. I think that's really exciting. Give us an example of one of those. 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 super easy way that doesn't require professional equipment.

What do you think that eBay is doing uniquely in artificial intelligence? 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 that.

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 sort of double downing 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 and thinking of it as intelligent visual experiences that are immersive, interactive, adaptive, etc. 3D is sort of the tip of the iceberg, right? But as we get deeper into the quote-unquote 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 a screen is something that will be transformational for eBay.

You've got a very interesting background, bookings.com, Capital One, Hearst, Boston Scientific. Maybe you could 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?

It's interesting. Actually, I 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 healthcare because it was very natural. I did my graduate work in essentially machine learning methods for genomic analysis because he was the human genome era, etc.

And so I stayed in healthcare, 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. 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?

Always people, they always get in the way. Spoken like a true engineer. 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. While typically everyone is on board that it's the right answer, the prioritization of that versus the very immediate term business objectives is what typically end up faltering.

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 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? Are humans still continuing to outpace? I'm kind of curious what your perspective is on those two things.

You really dusted off some old things when you were looking at my background. I appreciate you digging. You know, 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. And 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 human species is that the variation in signals and the speed of that variation being introduced is just accelerating massively, right? 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. 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.

Nitsan, 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. Which they are. But 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. Yeah, I've probably been pretty vocal because I learned it the hard way getting somewhat burnt in my earlier years in 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. 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 understanding 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 the sort of hundreds of millions of dollars a year. And to me, it felt obvious

that certain applications, for example, with speech recognition and sort of intent detection, et cetera, 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 the 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 to deploy and get these things up and running. I don't know anyone 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 till 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. I want to pick up on something you said about automation, which is one of the larger themes that AI is being used. 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 human. And you're 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. 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. 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. 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 the customer is 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 backend 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. We have also 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. The verb you used, inject, was interesting. You used 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.

Well, another thing I wanted to pick up on, Sam, is 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 technology.

functions or processes and whether you think there's any misconceptions there. Like people anchoring 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.

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 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. So Nitsan, 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?

Probably on the healthcare 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 life-saving benefit. What worries you about artificial intelligence?

misuse, misuse by bad actors, whether it's in military operations or in other types of nefarious activities. You can think of deep fakes as an example, just as it become more and more accessible and easy for everyone to use. I do have concerns. What's your favorite activity that involves no technology at all?

Oh, crap. I was about to say photography, and then I realized, wait, that's not even a good example. We can count that. It's not strictly artificial intelligence. We'll give you credit for that. What did you want to be when you were a child? What did you want to be when you grow up? AI engineer at eBay? I actually started as a creative writing major. So I guess writer was my initial passion. What's your greatest wish for what we're going to do with artificial intelligence in the future? I think making it...

easier for almost anyone to make a living pursuing their passion. It's a great meeting and talking with you today. I think one thing that's going to resonate with a lot of our listeners is this idea that

AI implementations are different than existing implementations like ERPs. That siloed approach that you might take towards 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. Thank you for being with us. It's been quite insightful and really, really appreciate it. Thank you, Sherman.

Thank you for joining us today. Next time, we'll talk with Helen Lee, Technical Fellow and Regional Director at Boeing. Please join us. Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights,

and gain access to valuable resources about AI implementation from MIT SMR and BCG, you can access it by visiting mitsmr.com forward slash AI for Leaders. We'll put that link in the show notes, and we hope to see you there.