Tech Insights Series on the CX Pod – LevelAI

Most companies are sitting on a mountain of customer insights they don’t know how to use. In the latest interview in the CX Pod’s Tech Insights series, LevelAI CEO Ashish Nagar breaks down how the world’s most efficient enterprises are using intelligent automation to bridge the gap between “good service” and “enterprise-wide growth.”

See more episodes at TheCXPod.com.

TRANSCRIPT:

Liz Glagowski: Hi, everyone, and welcome to the CX Pod. I’m Liz Glagowski of the Customer Strategist Journal. I’m your host for the podcast. I’m really excited today. We have another in our Tech Insights series, and I’m here today with LevelAI CEO, Ashish Nagar, to talk about just some CX trends and CX topics that are of importance to everyone in the industry. 

So thanks for– Thanks for having me. Appreciate it. Very excited. And we’re doing this one not only in audio in our typical CX pod, but also we’ve got this in video we can put on our TTEC YouTube channel and on social media as well. Thanks for being here and in person and not just being here. Thank you. So Level AI, it’s a leading provider of advanced AI applications in the contact center. I know you partner with TTech on end-to-end CX platforms. So before we get deeper into the conversation, can you give us a little intro on Level AI?

Ashish Nagar: Yes, absolutely. So thanks for having me again. Thank you. Level AI is an end-to-end AI platform. We build AI agents for the front office and the back office. Very simply, right? So for customer-facing conversations all the way to every part of CX operations, from quality to insights to voice with the customer. And we work with some of the largest organizations in the world to transform their CX operations with AI.

Liz Glagowski: Great. So it’s a new year, you know, and AI is the hot topic, of course.

Ashish Nagar: Is it?

Liz Glagowski: Have you heard about that, the artificial intelligence thing? There’s a lot of excitement and optimism, especially around some more agentic things, but there’s also a lot of trepidation, uncertainty. Some people just don’t know what to do. So where do you personally fit on that spectrum? And then where do you see your clients are fitting on that spectrum?

Ashish Nagar: Absolutely. So I think, look, when we started Level A, I’ll just take a 10 second detour. The problems in this space have been around for decades, right? Like how do we deliver better quality while controlling costs? How do we improve CSAT? How do we improve sales conversion? The problems have been around for decades, you know? And so this moment specifically is about can we now actually truly deliver against that promise? 

And I think in the last six to 12 months, to your question, we have a set of tools now where with large language model native technologies, which is the underlying technology behind generative AI, we have an ability to really deliver on that promise. 

So what we are seeing differently now over the last few years, and we have been LLM native from day one, what we are seeing differently now are customers getting more and more educated on what LLM native AI looks like, what real AI looks like versus other solutions or what bolt-on solutions look like or what, you know, single billing systems look like or whatever. I think that’s the one big difference. 

The two at a very high level, the expectations around automation are much higher. The expectations around intelligence are much higher. And then maintaining quality for both AI agents and human agents, consistency. Those are three big things you’re seeing.

Liz Glagowski: Yeah. Yeah, I mean, you are certainly Gen. AI experts in contact centers, intelligence, actionable insights.

Ashish Nagar: Yeah.

Liz Glagowski: So from your perspective, what are some of those biggest client pain points you’re helping solve and getting really deep into that insight?

Ashish Nagar: Absolutely. So I’ll call it threefold. One is voice of the customer. What is driving customer pain, right? Every Fortune 100 CEO all the way, every leader wants to be customer centric. So what’s number one? 

Number two? When designing a customer journey, how do we scale quality across all interactions, not just with the AI agents and human interactions? 

And third is, how can we automate, what can be automated on the front office side in terms of broad customer interactions, but equally importantly in our CX operations, right? 

So those are the three big problems we see. So going deeper and deeper on voice of the customer such that those insights can go to the product team to the marketing team, the compliance teams, because nobody calls customer service to talk about customer service, right? Like they call about a billing issue, a pricing issue, a product feature gap, troubleshooting. Those are all features, inputs to every part of the business. 

On quality, very interestingly for 2026, as AI agents take more and more of these conversations, Who’s checking those AI agents? Who’s making sure that the quality of those AI agents is great? And ultimately, the customer doesn’t care about talking to an AI agent or human agent. They care about all of us are users of this technology, having a wonderful end-to-end journey. 

So uniquely looking at the whole journey together, the quality of the AI agent and human agent together. That’s the second and third I talked about automation. We had a LevelAI voice agent talk to our technology customers and customer for 15 minutes on a complex troubleshooting topic, where a 20 minute step-by-step solution, they were able to solve it. That’s the level we are moving into. And AI agents are not just stopping there, but how can we use them for operations? That’s something we are uniquely, we call them AI workers. We are uniquely innovating on.

Liz Glagowski: So obviously you’re in the middle of this AI revolution then. What’s changing this year? What do you see that’s different with client priorities and even their AI maturity? Where’s everyone at?

Ashish Nagar: I think from 2025, the things which will stay are a lot of top-down interest, and rightly so, from board CEOs to adopt more automation, more voice agents, more of this VOC and QA technology. That’s same. What will change is that the early messaging has played out. Now, show me the money, show me the results, show me the outcomes, the ROI, and number one. Number two, it would also paint a focus to how hard is it to deliver the change within the enterprise, because AI doesn’t act alone. It acts with the ERP system, the CRM system, the ordering system, multiple APIs, And I think a lot of new AI vendors, which we just came back in the last six or eight months, 12 months, will realize that this is as much about AI as it about the API. And so I think that that will change this year.

Liz Glagowski: So that’s another key challenge that I know we’re finding is this tech stack integration, trying to figure out everything. What’s your perspective on how… Is there a different way to approach it? Is there some good ideas to think about?

Ashish Nagar: Yeah.

Liz Glagowski: Stack integration, what are yours?

Ashish Nagar: That is good. I would look, I would work backwards from ROI. If I’m advising a CX leader or a CEO or COO, which are the beginnings, what initiatives of automation in your organization create the highest impact? Let’s take an e-commerce company, it’s like, where is my order? right? And creating the IT environment for that instead of trying to solve all problems in one go and then attack at it in a phase wise way. The bigger the organization, the more tech debt there is in these systems, right? And so that is what, that’s how I would approach it. That’s number one. 

Number two, I think forward deployed engineers are really popular these days in Silicon Valley. We live in Silicon Valley. It’s professional services, you know, it’s professional services with another name, but I do think there’s a little bit of agentic sort of, configuration work, but I frankly don’t see that to be very different how it played out over the last four or five, 10 years. That’s how we would advise approaching this. 

Liz Glagowski: So just to wrap it up then, what are you most excited about for this coming year?

Ashish Nagar: Two or three things. One is, you know, customers realizing that they need one integrated end-to-end system. One of the problems with customer service is that One customer journey is surfaced by a voice agent, then by, let’s say, a copilot, AI copilot solution, then let’s say an AI QA tool, and then an AI view C2. That’s messed up. Four different AIs for one journey.

Liz Glagowski: Yeah.

Ashish Nagar: Customers will realize, nah, I don’t want that. We want one connected system, and also one AI across all of them. Like a lot of legacy vendors have four different acquisitions. or something, but there are essentially four different AIs, right? So I think customers will start recognizing, which leads to four different data silos. We are really excited about customers realizing that one system is better. 

The other, which, you know, is playing out with Google and OpenAI a little bit, full stack AI. We think full stack AI is the way to like integrating all the way from GPUs to models to data to the apps, right? With an AI trusted in between, that is the stack of the enterprise for the next foreseeable future. put a wrapper, a thin wrapper on top of technology. Those two things I think I’m very excited about because they will show early signs this year, but in my view, they’ll be the trends for the next, this whole wave, you know?

Liz Glagowski: Right, right, right. And then I’m asking everyone for their Tech Insights series, if you and I were to talk again a year from now, what kind of things would we be talking about?

Ashish Nagar: I think we would be talking about all the projects which got underway now and the, tremendous ROI. And I know it’s like a salesy answer, but the tremendous ROI, most of them were able to deliver and the learnings from some of them, I really feel there will be, it will become really differentiating for brands to have these kind of voice agents be front and center for customers. It’ll become really differentiating for brands to have one integrated customer journey. And those, stories are still not prominent, but they will become front and center by next year at large scale.

Liz Glagowski: Yeah. And that will help the overall experience for consumers as well.

Ashish Nagar: That’s the biggest thing. For you and I, it makes it, yeah, ultimately we are all consumers of this tech.

Liz Glagowski: Yeah. Excellent. All right. Well, thank you so much, Ashish. Thank you. And I look forward to our conversation next year.

Ashish Nagar: Thank you. Same here.

There is one CX trend so new, it didn’t even make it into our 2026 CX Trends report: AI agents calling contact centers on behalf of customers.

TTEC experts Alfredo Rizzo and JB Bednar discuss real-world examples that sound impossible until you hear the actual call recordings and reveal why CIOs and business leaders are responding with “immediate attention and a sense of urgency” when confronted with this rapidly evolving challenge.

Listen to the CX Pod episode, which is an excerpt of the full LinkedIn Live webinar, “Say goodbye to old-school CX in 2026.”

LISTEN ON SOUNDCLOUD:

TRANSCRIPT:

Liz Glagowski:

Hey, everyone. Welcome to the CX Pod. I’m your host, Liz Glagowski of the Customer Strategist Journal. Now, we at the Customer Strategist Journal recently released a CX Trends report with TTEC and TTEC Digital that focused on five trends that are really impacting customer experience for 2026.

As part of that, I just hosted a LinkedIn Live with our two experts from TTEC and TTEC Digital, Alfredo Rizzo and JB Bednar, about some of the trends and what they’re seeing out of the market. So for this podcast, I wanted to take an excerpt where they focused on a really key part of agentic AI that actually is too new to even appear in the paper that got released back in November.

AI is calling into the contact center and acting on behalf of customers in a lot of different ways that maybe some people aren’t prepared for. So we had a great conversation around that topic, and it’s excerpted here. Like I mentioned, this is just one excerpt of a larger conversation, so head on over to ttec.com or ttecdigital.com to check out the full LinkedIn Live event, as well as download the CX Trends report. Okay, everyone, happy listening.

Liz Glagowski:

So one final question. I know we’ve had a great conversation so far, just to wrap it up. we published this paper in November, and things move so quickly that I’m sure the landscape’s changed even since then. So can you tell us one thing that has popped up on your radar that’s a new hot topic, maybe since we even went live with the report in November? JB, I think you can start with this one.

JB Bednar:

Oh, yeah. There’s definitely a couple of hot ones in. Boy, I mean, I don’t think the shelf life on any any research is good past a month or two these days. There’s an important one, I think, is a trend that we’re starting to see that’s pretty material. If you Google, you know, AI is calling and tech, you’ll see some of the white papers and work we’re doing on this now. But it’s this idea that you know, customers are using AI as well, right? Google is promoting tools, you know, and customers have access to ChatGPT and Claude and other technologies just the same.

We’re starting to see consumers use these AI technologies to engage with brands so that, you know, our contact center associates are engaging with AI agents that represent the consumers. This is a real trend. We are seeing this today.

We’ve got clients today in the healthcare space where, you know, in B2B scenarios, healthcare payers are using AI agents to, you know, confirm benefits for a patient or to get, you know, a pre-authentic procedure, things like that, where our agents are now being faced with you know, an inbound call, they may be dealing with customers, but now they’re also dealing with AI agents.

And I think that’s definitely going to be an important one, especially around areas where maybe, maybe you can’t control that. I think it’s going to be, it’s really an area where I think it’s going to throw a lot of companies for a loop, because now it means how do we, how do we prepare our agents for working with bots? How do we validate customers? How do we get consent? How do we make our knowledge base material or our transaction, our transactional systems available to external agents?

I mean, that’s a that’s a complete 360. Nobody saw it coming change for a lot of brands and a lot of contact centers. You know, there’s even the fraud potential of that is so high. So You know, we’ve all thought that, hey, we’re gonna replace the contact center agents with AI, and customers are gonna talk to AI, but we’re also seeing the opposite, right? We’re not talking to customers anymore, we’re talking to a customer’s AI agent. So, you know, I don’t know, Alfredo, do you have, I’m sure you’ve got a pretty good perspective. You’ve been working on this one for a long time as well.

Alfredo Rizzo:

We have like a year, I mean, this is such a hot topic, and I’ll say that the reaction we get when we start talking about this with CIOs and line of business leaders at our clients, is just like immediate attention and sense of urgency understanding. They say, whoa, this can snowball so quickly. It’s just like, their next question to us is, what are you going to do about it for me? Right? Like, How are you going to help me with this? is going to be big. You know, we’ve seen, you can probably just Google this out there, but there are people talking about the next version of the internet.

It’s not going to be built for humans. It’s going to be built for machines, meaning AI assistants. So if you’re a brand that wants to be found, it’s not going to be a human finding you. It’s going to be an AI assistant working on behalf of a human consumer. to do something.

People are not going to be necessarily looking at your website and downloading your app. They are going to be going to ChatGPT and Gemini and Claude and Grok and saying, you do it for me. You book my travel. You go on this travel site where I have an account, pick one, and I need to go to these places with these people and have these experiences, and I want to fly this way and do this, and I need cars, whatever, and just do it all for me. I’ve got your credentials with this travel site, so it’s all in your shopping cart. Please review and tell me if you like. Oh, I like it. Okay, if you want to give me your credit card, I’ll buy it for you. Or else you can log into the site and do it yourself. So that is happening right now. And the Center for Human Consumers to Action is going to shift. right, into these AI assistants.

And so it’s not going to stop with the example I just gave. It’s going to continue very much into, well, there is no digital way to do this, call them. Call the brand and do it. So we are seeing clients reach out to us. In the summer, we had a major financial services client that reached out to us and said, hey, have you been seeing this? And we’re like, yep, we’ve been seeing it.

We just had an AI assistant call us to negotiate that settlement on behalf of a customer. And at first we thought it might be fraud, but nope, every authentication question we asked for, you know, 2 minutes, the AI is infinitely patient and answered them all. It had all the goods. Maybe there was a human behind it because we even asked it out of wallet questions and it had the authentication. So then we were like, the human agent was confused.

Should I service this or not? OK, we decided to go ahead and service it. So we actually worked with the AI assistant for 20 minutes to negotiate a payment plan on this loan. And this was an expert negotiator. They were able to lower the settlement amount from what was owed, keep the company from referring the customer to credit bureaus, and so on and so forth. This had been expertly trained to do that negotiation task on behalf of human consumers by calling into companies, building a strategy for who we’re going to call first, how much disposable income you have, and so on and so forth.

And that was certainly an eye-opener because it’s no longer just Google calling for dinner table reservations. It’s now moving up the value chain of use cases, right? Think about an expert negotiator working on your behalf. So this is real. This is going to come fast. And we need to have a strategy for do we even service it? How do we determine good and bad AI that’s calling us? What do we do about it? Are these decisions that we have with AI on behalf of human customers legally binding? And do they hold water long-term? What is our position? So we need to start thinking about this. So yeah.

JB Bednar:

I’ve got one more to share on that point because you shared that example and I would not have believed it if I did not listen to this actual call myself. but was listening to one of these AI calls and the agent didn’t break character.

At about the midpoint of the call, the agent started to get a bit cautious, right? Because it was clearly not quite a person. Maybe it was the first time one of these agents had taken one of these calls and they started to push back on some of the AI questions.

And the AI asked to speak to a supervisor. I’m not kidding you.

Now, look, somebody programmed this thing to do it. Literally, the AI saying, OK, I’m not achieving the goal that I was developed to achieve. This agent is putting up a roadblock. I’m going to ask for a supervisor. It’s crazy. But imagine how agent training has to change now.

There was a question in here around fraud from Catherine. You know, now we’re expecting a lot of agents to be able to identify a deep fake or a fake voice or to suss out an interaction that might not quite seem perfect and then do that. That’s challenging to do at scale. It’s not easy to identify these voices if you don’t listen to them all day long as being AI generated.

Alfredo Rizzo:

Yeah, I think we’re going to have to shift, JB, from not just thinking about customer experience. I’m going to coin a term here. We’re going to have to think about machine experience. What machine experience are we building for the AI agents that are going to act on behalf of our customers? How are we going to say they can do everything that a customer is authorized to do with their bank account, for example, or insurance policy? right? Or healthcare appointments and records and so on. Or maybe not.

Maybe the human customer has to delegate permissions to the AI agent. There has to be a framework to tell the company, the service provider, the brand, you know, this permission has been delegated. You can deal with my AI agent for this, but not everything that I can do as a human, right? So This is going to be rapidly evolving. I think this is gonna be something we’re gonna be focusing on quite a bit this year.

There was another great example I had with one of our clients where they were struggling because customers were calling and saying, ChatGPT gave me this instruction. of how to fix my problem, which was wrong. And it turns out that ChatGPT had, you know, crawled the database, the knowledge base of this client a year ago. And its knowledge, its corpus of information was a year old knowledge base data. But customers expected that if I’m going to ask how to fix this product problem, I’m going to get the perfect answer from ChatGPT, not a year old answer.

This is not a problem any of us have ever had to deal with historically, right? We would expect, well, a customer’s going to come to the knowledge base. It’s going to be up to date. Now we’re seeing people use, you know, Google, AI search, Claude, ChatGPT in lieu of, or as the first line for customer experience. How do we make sure that AI agents that we don’t even control are giving our customers the right information? That’s a different paradigm entirely.