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Welcome, Nikhil. We are glad to have you.
Thanks, Manish. It’s always a great pleasure to talk to you.
Wonderful. So, we would love to hear from thought leaders, such as yourself, on how AI and ML are impacting our ability to influence the customer journey, and how some of the changes, from a platform and ecosystems perspective, are enabling this digital transformation that we’re on the cusp of—that we’re a part of. So, Nikhil, let’s get started by thinking about the work that you’ve been doing in organizations such as eBay and PayPal, and now at Viasat. You’ve been leading innovation. Give us an overview of how you see AI and ML being used.
I started off at Silicon Graphics very early in the day, and then moved over to Sun Microsystems, through the whole internet evolution there. Then I worked at eBay for some time; I built the sales side of their platform. Then I had a great time at PayPal, enabling an API platform and their API strategy. Right now, I’m working at Viasat. They are kind of leaders in satellite-based communications and its applications to a whole broad range of things, from military applications, all the way to consumer connectivity. So this has brought in a lot of challenges in how we deal with consumer experiences, how we bring intelligence in all of those customer journeys, and how we make sense of what the customer wants and what it is that we can offer them. [The] other thing is, even the notion of “customer” is kind of questionable now. Whose customer is it, if they are interacting with an integrated experience that involves six providers? So that itself is a hazy world, and it’s going to become more interesting over time.
We have a lot of challenges in applying AI and machine learning to a lot of these nuanced use cases that we have not seen before.
Awesome. That’s great, Nikhil, that’s a great overview. I think we all understand the need for democratization of data, but tell us a little bit about how you think about it, some of the challenges. And given your work in building platforms, API’s, [we’d] love to hear your thoughts on that.
Yeah, so, this whole API economy is built upon the notion that we have to unshackle the closed data and knowledge ecosystems, and bring it out in the open. I think any API economy is built on the notion of this democratization. So, we have to open up things for ourselves first, because a lot of companies are sitting on tons of insights and tons of data that they themselves are not able to unlock, so that’s the democratization at the home front. We need to have every enterprise, every business, be able to have full access and realize the full potential of all the insights that they have—so, a lot of silos to break there. [There are] a lot of great debates going around on how to do it within closed systems, how to push it into a CDP, how do we manage all of that stuff. So, those are great debates, but eventually, it doesn’t matter how we address it, unless we are exposing it for applications to really tap into—in ways that are natural for those applications. Any amount of that data and intelligence is not really going to be helpful, so making sure that that data is not only available, discoverable, but available in real time or near real-time, is more critical today than it was before. So I think using all of that context that we have around a consumer, or even the venue in which they are operating, can provide us so much leverage in how we apply that to AI and machine learning.
Got it, great. You know, you’ve got the experience in building marketplaces. Tell us some of the work that you were doing there, in terms of access to data and making sure that data is available at the right time, to be able to do the decisioning and intelligence-based decisioning.
Yeah, so, marketplaces are an interesting thing. Some people relate it to the obvious notion of e-commerce enablers, but it is much more than that. Marketplaces are really innovation drivers, in terms of the true, I would say, platform opportunities, or the platform network effects that these marketplaces can create. So, marketplaces are complex ecosystems by design, and that means there are providers, and there are consumers, that have to be matched up by the marketplace. Marketplaces evolve, and they are basically doing this hard job of matching what someone has to offer to what someone wants to consume. And I think the marketplace becomes stronger as this ecosystem evolves. More producers, more value, driving more consumers and more value seekers around, and I think it’s a great enabling cyclic effect that we have. So I think that charter remains constant: how do we bring innovation into focus, and how do we make it easy and scalable, to drive a lot of innovative efforts at the edge of the companies? Because the edge is where interesting stuff happens, and “How do we unleash all that the enterprise or the business has to offer to these innovators at the edge?” is the secret sauce there.
Awesome. At ZineOne, we think about the next generation of capabilities for real-time and intelligent decisioning, to involve the synthesis, the analysis of consumer data, as they are perhaps interacting with the marketplace. So, lots to really unpack there, in terms of the dynamics of data and how streaming and event-driven data analysis can really be leveraged in those situations. So Nikhil, let’s focus on some functions, such as marketing and digital commerce, and we’d love to hear your thoughts about some of the developments that excite you in the use of intelligence—AI and ML—in terms of unlocking the value of data. I think we talk a lot about context, we talk a lot about real-time…how do you really make it happen?
Yeah, and I think what is happening is our interactions are getting more and more complex. So, things that I’m working on at Viasat right now involve detecting the venue where someone is interacting, so we don’t even know where they are. They might be on a plane 30,000 feet above the ground, or they might be somewhere in a remote national park, or they might be in your neighborhood coffee shop. And we have to detect the venue, we have to detect their intent, and then we have to match them up with the right kind of providers that can bring in a lot of value, in whatever they are trying to do at that moment. In trying to detect this whole venue-aware context that a customer is operating within, we have to seek out a lot of insights, or a lot of signals around that context that they are in. And that could include things like their location, their elevation, the kind of business that they are interacting with, or the kind of weather that they are being exposed to. Sometimes there [are] a lot of predictive capabilities that we might want to put in. [Say] someone is flying on a plane, and is planning to finish off some work that they had planned—it’s a six hour journey. What if we can already tell them that there is going to be turbulence, and there is going to be issues with the weather that would knock out their connectivity for these two hours out of the six hours. [Then], they can plan their activities better. So I think a lot of nuanced use of this contextual awareness can be put to practice.
That’s a wonderful example, Nikhil, and it really gives a different dimension to thinking about being helpful as being the goal, for a lot of these engagements and experiences that we’re trying to design and really go back to the drawing board with.
Yeah, certainly, and I think a lot of focus has been on improving the customer’s experience, and I think the nuance here is: it’s not only about that, but it’s also [about] taking away any unnecessary experiences out of the way, and then making sure that any negative experiences don’t even surface. So I think a lot of proactive reactions to all of these situations can really, really be good.
That is really, really interesting, and at ZineOne, we do think about these areas where AI and ML can actually lead by providing, as you said, the predictions that can govern the timeliness of providing help to a consumer, to a customer—that can essentially aid them in the process, in their journey. I think companies are focused on: how do I grab attention, and how do I really convey the most important aspects that are going to govern the experience? And the attention then becomes a byproduct of that engagement, of that process itself. So Nikhil, could you tell us something that isn’t generally known about what it takes to be successful in the area of marketplaces, payments, e-commerce?
We are moving to a very different era and different set of expectations, so that seamless, frictionless, faceless interaction is what I think is going to be the future. And I think things are just going to happen around us, as soon as the environment or the ecosystem understands our intent well enough that they would just let it happen. [The] other thing I have noticed is, because things are so hyper-connected now, all businesses are in an extreme collaboration with one another in getting things done. That has made a big change in how we do things. The power has been taken away from producers of goods and services, and has been flipped over into the hands of the consumer. So it is not about what we provide to them, and how successfully we can push it onto them. It is all about them trying to dictate what they want, how they want it, and it all has to happen in the moment. We have to be able to respond to it dynamically. Those are going to be the challenges that would really pressure our AI and machine learning ecosystems, and really push them to the next big thing.
That is amazing, Nikhil: the notion of faceless interactions in a hyper-connected world. I love it.
-Nikhil Kolekar, VP of Platform Technology at Viasat
We look forward to getting to know your business!