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.
How can an entrepreneur in residence kickstart new AI-based businesses in a large incumbent organization? Find out on today's episode. I'm Shilpa Prasad from LG Nova, and you're listening to Me, Myself & AI. Welcome to Me, Myself & AI, a podcast on artificial intelligence and 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.
And I'm Sherwin Korubande, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, 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.
Hello, everyone. Today, Sam and I are excited to be joined by Shilpa Prasad, Entrepreneur-in-Residence at LG Nova. Hi, Shilpa. Welcome to the show. Thank you, Sherwin. Excited to be here. Tell us about LG Nova and your role there. I get asked this a lot. LG Nova is the innovation center of LG Electronics based here in Silicon Valley. Our mandate is very simple. We're tasked with providing
finding new business opportunities for LG Electronics that is not core to their bread and butter business. LG Electronics is a consumer electronics manufacturer, traditionally looking to make forays into solution-driven businesses. And so constantly identifying new technologies and areas that can be interesting for them to build upon and hopefully launch billion-dollar businesses out of it. That's what LG Nova is.
My role at LG Nova is entrepreneur in residence. And what that entails is I have many different hats that I wear, one of them being plain consultant for the corporation. And by consultant, I don't mean traditional consulting, but more in the context of what are some of the trends to look out for? What new technology areas could be interesting? How can we combine a multitude of technologies maybe for a business opportunity?
That's one part of it. The second part of it is very much focused on outside in innovation. Our roots are set in the fact that we don't want to do what somebody else has already done. And startups are a great example of how quickly they innovate. And we want to bring that innovation into the enterprise that we are and leverage some of that capability to chart out a path together. So collaboration with startups is key.
And that's where the entrepreneur side of me kicks in, where you've got to be able to understand the mindset of the entrepreneur, the speed at which they function, what drives them, what makes them do the stuff that they do, and convey that to the corporation in an effective way. And then, of course, there's a reverse role, right, where the entrepreneurs don't necessarily outside of the corporation understand that.
the workings of the big corporation and how do you bridge that gap, I think is very, very crucial to the role. It sounds very simple, but there is a lot of balls in the air in that sense. And you've got to be able to juggle all of that at the same time while keeping your eyes on the goal of building new business.
You're talking about a large, global, multi-billion dollar company that's operating at a given speed. And you also want this group to operate at a much faster, I would assume, entrepreneurial speed. So I could imagine why it's juggling a lot of balls. I wanted to ask you, is it identification of these technologies or innovation opportunities, disruptors, or is it in addition incubation and building of these as well?
Yeah, so there is the ideation piece, but there is also the venture building piece. And part of venture building is incubation. So you have maybe like six months to identify an opportunity, build towards something, and then demonstrate that capability both on the product side, on the business side, to senior stakeholders and investors wherever necessary, and then drive that to new business, right? So that definitely is a big part of what I do there.
Maybe it's just what Shervin and I hear a lot of, but there's a lot of artificial intelligence with startups. And you haven't mentioned artificial intelligence yet. So how does artificial intelligence intersect with your role?
AI is not intersecting. It's at the forefront of what I'm doing. If you remember like a year, year and a half ago, Sam, there was a big hype around the metaverse and technologies come and go. My focus is to identify real world problems that can be solved automatically.
and leveraging some of these technologies, right? So AI definitely at the forefront. There's a lot of AI-related work that goes on inside of the enterprise, which focuses on servicing the existing business units. And that's not what I'm put in place to do, right? My role is to look at AI as a technology and identify areas that could be interesting to solve as a problem in this ginormous world and build a business around it.
I can't speak too much of exactly what I'm building, but I can talk to the fact that there is an intersection of augmented reality and artificial intelligence that I personally am charting internally here, which will...
possibly change the way skill training, transference, upskilling, reskilling, and content around that is going to play out. I'm sure you already know this, that with AI in the mix now, so much of this can be so intuitive with a feedback loop that can be generated for both the trainee and the reviewer and essentially allow workforce experience
development or skill training in particular to move forward at a much faster pace. It seems like AI is going to be more of a key ingredient of the mix, not just an additive. And so, you know, keeping humans in the mix is going to be even more key. And I hear thematically in what you're talking about, focus on that. So tell us a bit about AI that's happening
You said you can't speak much, but I still want to tease some information out of you. So talk about AI that's happening within this innovation center or contrasted with AI that's happening at the enterprise.
I'm sure both of you are very familiar with a large enterprise and the channels one has to navigate in order to get anything to market. And what I mean by get anything to market, it's also about releasing something which impacts a lot more inside the enterprise, right? So when you look at the traditional sort of research that happens around AI within the enterprise, and particularly LG Electronics,
My understanding thus far has been that the appetite for risk taking is limited because it disrupts a lot of their existing work, right? Versus something like this that I'm charting out is leveraging some existing technologies that are out there. And when we launch something like this into the market, none of the existing pieces are
are impacted. In fact, they're benefited. So LG, for example, has 85,000 plus workers across the globe working in factory environments. Everybody understands today that there is a gap in the labor that's available in the factories from a frontline worker perspective. There is productivity-related asks constantly within the enterprise to make sure that the entire sort of capacity is being met. These are all real problems that
The AI research within the enterprise is not addressing. You asked about the time to market, right? My mandate is six months to figure out whether a business can go to market or not. I think on the enterprise side, it's a few years, if not more, right? Anywhere between two to five years. It has always been a topic of conversation saying corporate is slow, the startup is fast. So I'm essentially building a startup inside the corporate.
So what I'm hearing is unshackled from a lot of the legacy scale with which comes inertia and much faster sort of as new ventures that are going out. And of course, some of that innovation then helps some of the existing enterprise AI. Yeah, absolutely. Absolutely. Wonderful. You talked about...
upskilling, reskilling, and reimagining possibly the role of human in this whole mix with AI in the Mix you talked about. Tell us about generative AI and where you see that play out in this.
I think the possibilities are humongous, just like with any other opportunity in AI, right? One of the aspects of, for example, intersecting augmented reality technology with AI means that we're not only focusing on
on voice and text with respect to AI, but also computer vision related data streams that are being captured and processed in order for skill transference to happen as effectively as possible. So think about a nurse training environment, right? Like we're not practicing on real patients, but on the dummy, if you have to teach students
trainee nurses, how to position the patient on the bed in order to do a specific procedure, then there is a multitude of value in capturing the data stream from the head trainer nurse in order to see how to do that and when you're actually practicing it to get real-time feedback, right? So we're looking at multiple data streams here. So not just NLP, but we would be looking at computer vision-based technologies, and we
and bringing in AI to sort of almost like superpower that process and that training and training experience on both sides, right? So that's an example of how we're doing it differently. Normally, I'm pretty excited about the applications of artificial intelligence we talk about, but I got a little threatened here in a second. I'm an educator here and I hear you talking about all sorts of different ways of reskilling and retraining applications.
where do i go you mentioned a model before with nurses where the experienced nurse would teach the less experienced nurse maybe a lot of that gets replaced what's the role for that
experienced nurse now in this new model? Yeah, well, actually, Sam, I think your fear is maybe misplaced here. And let me explain this, right? We're not trying to replace the trainer. What we're doing is actually empower the trainer with more opportunity to do the stuff that they should be doing. So in the training of the nurses example, a head nurse has to actually physically stand next to the trainee nurses, because
possibly multiple times during the day and teach them how they're doing their specific tasks. Now, imagine a scenario where we've eliminated at least two levels of feedback, review and assessment.
before the head trainer can actually come into the mix and take a look at what's going on. So the certification, I think, is still very, very important, right? Whether it be manufacturing, whether it be healthcare, the two examples that we're talking about, the certification is still important. And that certification comes from the trainer who is the expert. So we're not removing the expert. It's actually empowering the expert to also be able to train effectively and
and not have to monitor 100 screens of like trainee nurses that are doing their job. So you see the difference, hopefully. Yeah, Sam's a tenured professor. I don't think he's going anywhere. But Sam, it's a little bit like the full professor, i.e. yourself, sort of not being bogged down with the things that a graduate student can take care of. And like you only do the tough ones and the ones that really tease your brain. So that sounds glorious to me.
I think it's the assistant, right? You'd have like an AI assistant that could actually like do a lot of the grunt work that otherwise you have to sit and do. So I think there is value in it. At least we're seeing some inferences of value that we're able to generate in the very early conversations we're having.
Wonderful. Sherpa, tell us a little bit about your background, how you ended up in this role. My career is a zigzag line, Shervan, and it's not a straight line to what I'm doing today. Over the years, I think there's always been a lot of value and joy that I have found in creating something ground up. The energy, the momentum, and the rigor it requires to scoop something up from nothing and create something out of it.
has been at the core of everything I've done. So very early on in my career, I had the opportunity to partner with someone to start off a graphic design studio. And there I learned the mechanics of what it takes to actually create a business, right? It taught me a lot of the pieces that went into it.
From there, I've always landed myself in roles that were actually entrepreneurial in nature or servicing the entrepreneurial ecosystem. I like to mention a company called YouNoodle Inc. that I worked with here when I first came to Silicon Valley. And there I learned the power of
selecting the right startups or innovations to work with in order to move innovation forward, whether in the context of corporations, governments, universities even, and large-scale accelerator programs. That led me to my own entrepreneurial journey again. During COVID, it was really difficult. I launched a startup called Longplay.
and built a team of about 10 people. It was focused around changing the way corporate innovation happens. And one of the things I think there is, when you're outside of the corporate, there is just no way to understand corporate
all of the different moving parts that goes on. So we couldn't prove product market fit there, which is where Nova came into the mix. And I got the opportunity again to try to do a product market fit in an industry and in an environment that was a little different from what I was used to. But I think the nuts and bolts are the same. So that's just a quick background on me. It's super exciting, very multidisciplinary. And it brings me to my next question.
There's no one size fits all when it comes to AI. And it's, of course, a full ecosystem of engineers and software engineers and prompt engineers and AI engineers and data scientists, etc. But I've been noticing a...
gradual evolution of the skills over the last, I would say, 10, 12, 15 years or so from, you know, hardcore technical only to also more multidisciplinary. And that multidisciplinary could be in a variety of areas. It could be in social sciences or it could be in, you know, human AI interaction or it could be in philosophy or whatever. But
Are you seeing the same thing? And what is your perspective on the talent of the future in this space of AI? It's an interesting question. I have to say I'm not seeing the same thing. I'm not seeing the width as much as I'm seeing the depth. There is still...
I think a tremendous gap in the workforce with respect to being able to be multidisciplinary. There's a lot of, to your point, deep tech work that happens, especially around AI. I think that's over the next few years, a lot of the folks like myself that are responsible for how this technology unfolds.
are going to have to carry with them. That comes to measurement too. I mean, and then Shervin, I know that's a hot topic for you right now, but when you're talking about impact, you've got to figure out how to measure it. There's going to have to be some measure of, is this working? Is this not working? And
These seem like some difficult problems, particularly with the newness of the technology, the freshness of the timelines that you're talking about. Seems very difficult to figure out if this is working or not working in your role. Yeah, I mean, I can give it a little bit of a personal take.
My style of building projects has always alluded to testing it as much as possible with the customer who has the problem. And that's where the product focus sort of is very front and center for me. And that's why venture building is exciting. When we talk about measurement, there is definitely different ways in which the impact of what we're creating or what we're
trying to create can be measured. There is a market validation aspect here, right? There is definitely an investor validation aspect, meaning our investors, it's not just about my project, right? It's not just about the project that I'm creating or building. It's actually more about where the money is going. And of course, a little bit of it is hype, but a little bit of it is also taking the chance on the next thing that is likely to change the way
AI is going to be used in day-to-day environments or contexts. I think the third part here is also just about empowering the users, ultimately, of whatever solution we're creating and whatever we are building in order for
The business opportunity ultimately has to add value to the humans, that is you and I, who are actually going to use that technology to make our lives better. Today, we're doing the work in the exact same way that we are taught or we're used to or we have learned how to, and we're going to have to change that or adapt that and pivot to a new way of doing things. And is AI enabling you to have
better data and information on that? Is AI enabling you to know something that maybe 20 years ago would have taken you a lot longer to know?
Absolutely. But I think is AI enabling me only to do that, I think is perhaps a lot of weight on AI and less relevance to what has been done in the past like 10, 15 years in a specific industry. I think AI also has a flip side to it, which is sometimes it's putting too much in front of you where then you have to actually like go through the top layers to
really figure out where this data can be used and how this data can be applied. So there's the pro and the con of AI as well. - You mentioned cons, what kind of cons are you seeing from these startups you're working with? I mean, what are people struggling with here? - Well, I think the startups in particular are still looking for ways in which whatever they have developed can be applied. The application of AI
in effective ways is still to be determined. And so startups are creating all these LLMs that can be relevant to the project, but they don't know how it's going to actually translate. So I'd say if
there's still a little bit of a journey there for the startup to be able to like understand it. And in the context of the role that I have today, for example, I'm also enabling the startup to understand how they can use the technology that they've created and translate it over to a meaningful problem, right? But like, that's why I said consulting before, like it's consulting for the startup as well, like to give them some real case
scenarios where you can apply something that we've built together and then see how it translates into value. So I think the startups are still learning for sure, Sam. So those things actually tie back to maybe some of Shervin's comment about breadth and breadth of knowledge and multidisciplinary because you didn't specifically mention that they were struggling with technology. I heard lots of things there that were not particularly technology-oriented.
Let's transition here. One of the things that we do at the end of the episode is we ask you five questions. We're going to ask them fast. We just want your first reaction from them. What do you see as the biggest opportunity for AI right now? Healthcare. Okay. What's the biggest misconception that people have about artificial intelligence? That it's going to replace them, that it's going to replace us as humans. That's not going to happen, I think. We're ways away from that. Ways away. Good. What's the first career that you wanted?
To be an entrepreneur. I think it was always in my DNA. Yeah, do you have a lemonade stand when you're growing up? When is there too much artificial intelligence? Where are we trying to make a square pig fit around a hole? I don't think I have a concrete answer for you, Sam, there. So maybe you've got to switch the question up. Ask it this way. Can it be too much? Can you put too much artificial intelligence into a product?
Yeah, absolutely. I think an area that comes to mind is cobots, like collaborative robots. It could work. It may not work. And then it's very heavily AI driven. So automation is key there. But how much of that could be relevant, I think, is questionable for me personally. If you're fantasizing, what's one thing you wish artificial intelligence could do right now that it can't currently do?
Make me sound as good as I possibly can. Shilpa, thank you so much. This has been illuminating and wonderful. Thanks for being on the show. Thanks for joining us. Thank you. Thanks for listening today. On our next episode, we'll hear how Harvard Business School professor Ayelet Israeli suggests augmenting corporate market research with generative AI. Please join us.
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