Ashish Nagar Podcast Transcript
Ashish Nagar joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, home of the Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive. Today’s guest is Ashish Nagar. Ashish Nagar is the CEO and founder of Level AI, taking his experience at Amazon on the Alexa team to use artificial intelligence to transform contact center operations. With a strong background in technology and entrepreneurship, Ashish has been instrumental in driving the company’s mission to enhance the efficiency and effectiveness of customer service interactions through advanced AI solutions.
Under his leadership. Level AI has become a key player in the AI driven contact center space, known for its cutting-edge products and superior implementation of artificial intelligence.
Well, good afternoon, Ashish. Welcome to the show!
Ashish Nagar: Thank you for having me.
Brian Thomas: This is awesome. Love you jumping on a podcast with me today. Have a conversation about you and your company and what you’re doing and hailing out of the Bay Area there in California. I really appreciate that. I get a lot of guests out of there.
So again, Ashish, I appreciate your time and jumping into your first question, after your tenure with Amazon’s Alexa team, what motivated you to establish Level AI and how did your experiences at Amazon shape your vision for the company?
Ashish Nagar: Yeah. Again, thanks for having me. And the Amazon Alexa experience was really interesting and formative from a technology perspective with the genesis of Level AI.
So at Amazon Alexa, I was working on a project for almost a Star Trek computer where Jeff Bezos wanted us to build Alexa such that we could, Alexa could talk to any human. on any social topic for 20 minutes, like just having a chit chat conversation as if you know, you walk into a bar and you find a new person and you could chat with them.
And it was an Amazon team as well as 10 research groups from the best universities in the world who are trying to solve this kind of like grand challenge. And I was the product leader in the middle of all this. And what I realized was How much change was going to come in this space of language AI or generative AI, which we call these days.
So there was a massive opportunity to build a company which could use these kinds of technologies for specific applications to make. And the vision was using AI to make human productivity better. Right, augmenting human productivity with AI, and that’s what we set out to do. The Alexa experience gave me that unique background to build a differentiated architecture, say no to the old sort of rules based, non-generative AI architecture.
So with Level AI, we started with that foundation of an LLM native product, which has shown Increased benefits for our customers and sort of a continued path for us to build a great company.
Brian Thomas: That’s amazing. You know, being at the ground floor, building such a great product like that must’ve been amazing.
Obviously, you’ve got some of the brightest minds from around the world and you’re in the middle of that as product development. So, I think that’s amazing. And it certainly has shaped your vision for your company, which is awesome. So, Ashish, what specific pain points in customer service did you aim to tackle with Level AI’s technology, and how does your platform uniquely address these issues?
Ashish Nagar: Absolutely. So Level AI is a customer experience, intelligence and service automation product. And what I mean by that is every brand in the world from Fortune 1 to a small mom and pop store has a relationship with their customer. And it gets manifested in a contact center call or a social media post or a survey or some kind of a response.
And Level AI is an intelligence layer on top of that data. Right. So we do experience intelligence on top of that, which essentially is what are these customers reaching out about? What are their pain points? What are the service issues? What are the bottlenecks? Where can compliance and service and sales help and so on and so forth, right?
And the other part is what part of these workflows. can be automated in direct service to the customer or while as part of their operations to be able to improve the service experience. So right, those are the two things which we are working on experience intelligence and service automation. And how do we uniquely address this?
So we have two unique characteristics in our business. First, we have built the broadest bundle of AI and CX solutions. Right. So we are a one stop shop for five or six different A. I. N. C. X. Use cases like quality automation, customer experience, intelligence, real time copilot for service agent screen monitoring.
But that’s the one unique advantage we have. And the second one is. We have an LLM native architecture right from the GPU layer all the way to the app layer and we control every part of our AI stack or we have built every part of our AI stack so we don’t depend on OpenAI or any Google or any other third party for LLMs or any part of our AI stack that gives us unmatched security, cost and performance.
Brian Thomas: That’s amazing. Building stuff from the ground up sometimes, obviously, you know your, your architecture inside and out, but keeping that in house, obviously, for a lot of reasons, whether it’s trade secrets, proprietary information, or just again, as you mentioned, um, to reduce costs. I think that’s pretty cool, but customer service is always near and dear to my heart.
I talk about a lot here on the podcast, and I know we need to improve that because that’s always a pain point in the consumer world. Obviously, as you know that, especially when humans are doing the job sometimes. So thank you for sharing all that. And Ashish, what are the primary challenges enterprises face when integrating AI into their contact centers and how does level AI assist in overcoming these obstacles?
Ashish Nagar: Great question. So like, you know, in the classic world, there are two or three primary challenges. One is. Step one for any AI capability is pulling together all the data across the enterprise. So these could be, in our case, simply customer service contacts or voice calls, emails, chats, and, you know, Salesforce and survey data and so on.
So that’s one primary challenge. The second primary challenge is training the AI models For the enterprise environment and thirdly, I would say is organizational transformation, right? Like there are hundreds of people, thousands of people who get on board with this service and how do we make them better, right?
So on the first one, the data piece, Level AI has built out of the box data connectors for almost 50, 60 different customer experience services where folks can plug the data as easily as possible. The second one is. The AI training capability. So you might remember in the old AI paradigm of even three or four years ago in enterprise AI, you give a bunch of data and then twiddle your thumbs for six months for the models to train and like to for for customers to start value, seeing value out of it.
But with Level AI’s sort of LLM native architecture, once the data is hooked up into our system, We can start providing data value from next day, from day one, day two, and that is because these LLMs are like set, are pre trained on customer service data, are pre trained on functional data. So fast time to value, little time needed for AI training.
Which also means little time for professional services and sort of data labeling and data mining and so on. And then finally, in terms of organizational transformation and training, we have done a lot of work in that area to make the product self serve in terms of AI enabled coaching, smart walkthroughs, and we work closely with our customers to build a tailored onboarding program for them.
So those are three areas where we help out to overcome these obstacles.
Brian Thomas: That’s awesome. And I can only see that customer experience getting better and better over time. And I really like you delving a little bit into that right now. As I mentioned earlier, the customer experience is so, so important and near and dear to my heart.
So thank you for that. And Ashish, last question of the day. How do you envision the evolution of AI in contact centers over the next five years? And what role will AI play in this transformation?
Ashish Nagar: Look, I strongly feel against popular opinion that AI will be a significant player in the contact center or customer service space, but it will not, we are not looking at it In five years, an army of robots taking over all human jobs.
We are not looking at fully automated customer service teams and so on. I think we are looking at 30, 40 percent automation on an average across all industries. And then the other 60, 70 percent is AI powered service agents, right? Or AI, AI powered service agents, human beings who are 10X more productive.
using AI technology. And the reason I believe that is, look, when we think of service and we think of brands engaging with customers, they’re not just answering questions. They’re also salespeople. They’re also consultants. They’re also helping you select a life insurance policy. They’re also helping out with, you know, very complex technical troubleshooting.
In all those roles, A human’s touch, a human’s presence, a human’s intelligence, planning, knowledge is required and will be required, AI will augment it. In roles where it is simple question answering or simple status checks and so on, those will be automated by AI. And so, and what role will level AI play in this transformation?
We will lead this transformation. Like, one of the things which motivates us in doing what we do. is customer service is the ground zero of how AI will play out within the enterprise, right? And it is, you know, our life’s work to play a small role finding this balance of AI and the enterprise, right? So we will play a role in being the intelligence layer on top of all of these conversations, whether they are happening with the bot or with the human.
And then we’ll play a role in automating whatever can be automated in terms of service delivery. So we’ll play a central role in this transformation.
Brian Thomas: That’s amazing. And I like your prediction over the next five years of where we’ll be as far as that balance between human and machine. I predict it might be a little bit further, but you’re, you’re probably right.
You’re in the middle of this and you would probably know best, but I’m really looking forward at improving that customer service level. And again, taking the fatigue out of it for the humans that are constantly battling eight day shift of talking with customers that may not be very happy at the time.
So I really appreciate your insights. And Ashish, it was such a pleasure having you on today. And I look forward to speaking with you real soon.
Ashish Nagar: Thank you so much for having me. Really appreciate it.
Ashish Nagar Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
