3551: AI That Delivers at Scale: Inside HGS and Real Business Transformation

[00:00:04] - [Speaker 0]
Welcome back to the Tech Talks Daily podcast. In today's episode, I'm joined by a very special guest. His name's Venk Kaula. He's a CEO of HGS. And together, we're gonna sit down and have a conversation that hopefully will cut through a lot of noise around AI and digital transformation.

[00:00:25] - [Speaker 0]
Because we're gonna talk about exactly what intelligent experiences, what they really look like in practice, and why so many organizations still get stuck in pilots and spend less time in production, And how AI, cloud, data, and human empathy, all these pieces have to work together if businesses want those elusive outcomes that they can really rely on. So we'll put the hype to one side. We'll focus on real world situations here, and I'd love to know what you think at the end. But before I get my guest on today, I wanna give a quick thank you to my friends at Denodo. The data world is louder than ever.

[00:01:04] - [Speaker 0]
AI hype, lakehouse complexity, and pressure to deliver more with less. These are things that I talk about every day on this show. But Denodo is helping businesses make sense of it all because they provide a unified data foundation for trustworthy AI, lakehouse optimization, and data products to finally bring service to life. So if you're ready to unlock real outcomes, simply visit denodo.com today. But right now, let me officially introduce you to my guest.

[00:01:35] - [Speaker 0]
So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do?

[00:01:42] - [Speaker 1]
Thanks for having me on the show. I'm the global CEO of HGS. We're a company that's focused on enabling intelligent experiences where AI, data, and human empathy all come together and converge. We are a company that serves over 200 plus global brands, supporting customers across board in 80 plus languages.

[00:02:05] - [Speaker 0]
Well, it's a pleasure to have you join me on the podcast today. There's so much I wanna talk about today because business transformation has become somewhat of a crowded space. So many bold claims and rapid technological shifts there from AI to agentic AI. We're looking at quantum in the future as well. So from your perspective, what truly separates intelligent experiences where operations and interactions seamlessly link from surface level AI adoption, the kind of things that we see on our news feeds.

[00:02:38] - [Speaker 0]
And are you able to give any examples of of what these intelligent experiences look like in practice? Because we're talking about a little bit more than a chatbot here, aren't we? So what do they look like?

[00:02:49] - [Speaker 1]
Yeah. So intelligent experiences really link the operations or what I call the fulfillment side of the story on any interactions that happen. So, typically, when you have an interaction, there's always a follow through fulfillment that a brand has to fulfill and execute on. And making that interaction to be seamless and from interaction all the way to operations and then making sure that you fulfill the end outcome that the customer is expecting on a consistent basis is what we call as intelligent experiences. So a classic example would be you know, all of us can relate to our flights getting canceled or rescheduled or delayed.

[00:03:41] - [Speaker 1]
And in those scenarios, assume a scenario where you you are at an airport, your flight you're it's late at night and your flight just get got canceled. Typically, the airline asks you to stand in line to get a voucher and a booking to a hotel. Instead, an intelligent experience would be where the airline actually books you into a hotel room or gives you three choices, texts you of those three choices, and you pick one of them. And that would that is something that's intelligent because it is contextual in nature. It understands the context in which your flight is getting canceled and when your new flight is and when it has been rescheduled to and how far away your hotel should be.

[00:04:26] - [Speaker 1]
So using data and intelligence to contextual intelligence to fulfill a need of a customer is what an intelligent experience is all about. Another example that I would use is, assume in the case of your schedule for a surgery. And typically, you're told that twenty four hours before your surgery, you need to stop eating and three to four hours before your surgery that you need to stop drinking any liquids. But what if and that's typically provided in a brochure or a piece of paper, and somebody in the doctor's office talks to you about it. But instead, what if if twenty six or twenty five hours before your surgery, you get a text message which says, in an hour from now, you'll not be able to eat anything, so we we would recommend get getting something really light for you to eat now so that you're not hungry for the next twenty four hours.

[00:05:25] - [Speaker 1]
Or four to five hours before surgery, you get a message saying, hey. It's time to drink some water or have a have a have a drink before you come for your surgery because you'll not be able to take anything anymore in the next hour or so. And post surgery, following through with messages talking about this is how you may be feeling, this is quite normal. Most people who go through the surgery will start experiencing this pain or will have these challenges, and it is quite normal, and it'll go away in a few days. Those follow through communications become very important.

[00:06:03] - [Speaker 1]
And that is what an intelligent experience is, to really turn the table in the conversation, communication, and delivery of the experience from a customer's perspective and making it contextual in nature.

[00:06:18] - [Speaker 0]
And earlier this year, I have spoken to other people from the team at HGS, and I was made aware of a term realized AI, which refers to the practical real world examples and applications of AI. But for people listening and hearing about you guys for the first time, tell me how HGS define realized AI in practice, and what does a real time individual AI experience look like for a global organization that could be listening? And and where do you see most companies struggling to achieve it? Because I think there's been a lot of bandwagon jumping over the last few years, and businesses have been struggling to find ROI, etcetera. It's well documented, but you're doing something different here.

[00:06:58] - [Speaker 0]
Tell me about more about that.

[00:07:00] - [Speaker 1]
We have seen a lot of our customers starting AI pilots with various other organizations, whether it's their internal teams or other organizations, and they and they're forever stuck in a pilot or a proof of concept. That is where this idea came from, where we have been working with customers where we pick specific business problems. And we don't use AI for the sake of using AI. We use AI as one of the components of embedding into the workflow process that is there to execute something or execute a process or full conduct a fulfillment for a process and use AI to deliver the next week's best action, personalize at scale, as well as deliver a consistent outcome for the customer. And we've been able to actually execute those projects and realize the value of using AI through either 100% compliance or being able to get a 30% plus faster processing speed or improved customer satisfaction of around 20% plus.

[00:08:16] - [Speaker 1]
And all these things have led us to believe that there is a recipe of how to move from idea to pilot to actually getting the benefit and value out of AI. And that is not by using AI for the sake of using AI, but actually embedding people who understand the business problems and challenges, having technologists who can actually use it, the traditional workflow implementation. In addition to having a representative in the technology team who's representing the idea of using AI models in the right inflection points and delivering this as a value for our customers. So one of the approaches we are taking with the customers is we will provide proof of value in the first ninety days of picking a specific process that can be automatable with AI and enabling it with AI and drive it to provide the most value for our customers. And that's where we coined the term realized AI.

[00:09:20] - [Speaker 0]
I love that. And I don't wanna focus just on AI because cloud is an increasingly important topic as well because traditionally, many businesses feel that they invested heavily in the cloud and only to achieve mixed results. So when you talk about reunlocking the cloud, which I know is something that you're passionate about, What's gone off track here over the years, and how should leaders be rethinking their cloud strategy to recover or or recover real operational and and financial value?

[00:09:52] - [Speaker 1]
Yeah. So one of the, big things that happened on in the last decade was that a lot of customers moved to the cloud because, number one, they did not have to spend as much CapEx, and instead, the cloud spend turned into operational expense. And you were supposed to be only paying for what you use. But if you don't fundamentally redesign your systems and applications to take advantage of the elasticity of the cloud and the modularity of the cloud infrastructure that's available, you're really shifting what is their on premises in the data centers as physical infrastructure, and they move them to the cloud. So what happened was they took a physical server and they replaced it with a cloud server.

[00:10:46] - [Speaker 1]
But that doesn't really get you the benefit other than just moving from cap capital expense to operating expense. In fact, eventually, you'll it'll cost you more because you sometimes forget to turn off servers or you turn on more servers than needed. So you're not realizing the value out of moving to the cloud and the elasticity of the cloud and the ability to turn on or off based on the demand on the application that you're running on the cloud. So the key is to do have a disciplined approach and do what we call as cloud FinOps or financial operations on the cloud, where you analyze your utilization of your servers against the footprint of servers that you have, map it to the application architecture, and rearchitect the applications to be able to scale up and down elastically based on the demand on the application and the utilization of the server's processing powers. And in addition to that, we should also move to some of the newer technologies where there is serverless compute, where we can actually have the cloud infrastructure run your functions that you call on to be executed on demand and only pay for the execution time.

[00:12:09] - [Speaker 1]
And that is when you unlock the value of the cloud. As an example, we have helped a customer where we have been able to move from a deployment of 10,000 servers in their data center over to the cloud and then gain 500% more of our improvement in load speed of their applications and also drive reduce cost at the same time. And that can be only achieved if you rearchitect the application and modernize how your systems architecture is deployed on the cloud and not just simply lifting and shifting it and moving into the cloud. The interesting aspect is with the whole new demand on the workloads for AI, there is going to be even more and more demand on the cloud infrastructure and the need for elasticity. So I think it's more important for organizations to go through the FinOps discipline and execute the process of analyzing, understanding, and rearchitecting their applications to take advantage of cloud infrastructure.

[00:13:23] - [Speaker 0]
And there are so many different pieces to transformation that we're talking about here. We've got AI. We've got cloud. And AI readiness is often reduced to tools in the minds of teams, yet the deeper barriers, of course, involve data quality, governance, and ethical use. And with your focus at HGS on human centered intelligence, what what foundational work is required to apply AI with empathy at scale?

[00:13:49] - [Speaker 0]
Because, again, another critical part or another piece of the puzzle, isn't it?

[00:13:53] - [Speaker 1]
Today, if you if we look at the hype and if we believe in the hype, we we we all would have to believe that by just turning on and using the AI tool and giving it simply a simple prompt, you can get the outcome that you need out of it. But the fact is AI can hallucinate, and your AI's output is only as good as the training data that it has used. And a lot of it depends on the quality of the data, the governance of the of the data. And then there's question of ethics. Do we have an actual ethical framework with a human in the loop design to make sure that every decision that AI is taking is actually overseen and approved by a human from an empathy standpoint, from a human relationship standpoint.

[00:14:47] - [Speaker 1]
From our perspective, you know, most customer experiences and experience customer experience leaders are demanding for closed loop feedback and pre or post baseline of for attribution of the content that's being used in delivering the information. And not just that, it's also that with a human in the loop design, you ensure trust of the end consumer of the output that you're generating using the AI tooling that you're putting in place. So data quality and governance and human in the loop are the key to maintain an ethical, empathetic AI stance for a for an organization.

[00:15:30] - [Speaker 0]
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[00:16:16] - [Speaker 0]
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[00:16:59] - [Speaker 0]
So how are you helping shape AGS' strategy to ensure that technology investments translate into very real practical outcomes, not not just impressive demos? Because I think expectations are rising as we as the technology matures and we hit 2026.

[00:17:16] - [Speaker 1]
Yeah. The expectations are raising very fast, and they're being very unreasonable. Let's put it that way. And the the the yes. I have I'm playing this role, but based on our conversation so far, you can tell I'm also a technologist at heart, and I've grown through the world of technology.

[00:17:36] - [Speaker 1]
And that keeps me grounded every day to understand and recognize that not everything is automatable, not everything is going to be AI driven. So we are trying to anchor every initiative that we have to a measurable experience outcome Yeah. Specifically looking at growth and efficiency metrics for the company, and focusing on ethical ethics and governance, data governance first more than anything else. Some of the scorecards we track are, you know, first call resolution, customer satisfaction, what is the cost to serve a customer, the the wait time. We also look at sentiment not just about the consumer side, but we also look at the sentiment of the individuals or our team members interacting with the AI systems or with the customers, and how do we make sure that you're measuring from all aspects to not just focus on efficiency, but also focus on the experience itself.

[00:18:49] - [Speaker 1]
And that's what we are focused on.

[00:18:51] - [Speaker 0]
And I think there's also an increasing pressure to create smarter experiences while maybe unrealistically reducing costs at the same time. So how do you at HCS use your expertise in Agentic AI to offer software as a service model that that delivers real AI driven engagement, but without sacrificing control and and handing the keys over to somebody else. Again, maybe unrealistic expectations, but this is one of the things that you do here.

[00:19:20] - [Speaker 1]
Yeah. So traditionally, HGS played in the contact center and business process management space for a long time. So we know how to take a process, break it down into smaller components or a conversation, train team members, bring them up to speed very quickly, and execute them on a repeatable manner and a predictable manner on an efficient basis. And we're applying that same expertise to train AI models and build workflows and process reengineering to create agent AI processes and then turning around and making sure that there's a human in the loop on a command center style style model where there's somebody watching the entire process execution, making sure it is going as per plan, collecting the metrics, and observing the metrics on a constant basis. And we are starting to offer this as service as a software model.

[00:20:21] - [Speaker 1]
Essentially, the customer our customer, the brand, gets charged per interaction or gets charged per outcome, meaning only if a case is resolved or only if a case is not escalated to a person does the AI centric operating system gets, the the execution of that interaction is charged to the customer. If not, then we fall back to a human being available to help resolve the issue, answer the question, or execute the process. So we're kind of using a hybrid model of I almost call it a ballet between agentic AI systems and the human talent working hand in hand to deliver the best outcome for the customer as possible.

[00:21:10] - [Speaker 0]
And although there is a lot of hype around AI, I think it's often easy to forget that we've been here many times before. We've seen similar hype around mobile, cloud, digital transformation, digital disruption, hybrid working, AI, agentic AI. The themes continue again and again. And I'm curious, looking at your two decades in digital services and tech enabled CX, Are there any themes that you see keep resurfacing, and and what differentiates the organizations that turn potential into intelligent experiences from those that struggle to keep up, especially around linking data, automation, and operational know how? You must have seen a few trends here over the years.

[00:21:52] - [Speaker 1]
Of course. So it's very interesting. Right? When we moved from client server systems to web or when we moved from, you know, on prem systems to the cloud or when we when we when we try to do RPA and automation, in all these cases, anytime the fundamental shift in technology was treated as though it was a bolt on to an existing method of operating, it typically doesn't derive the value that you need to extract out of it. But when you see it as a completely new way of working and approach it from an organizational change management perspective and connect data, automation, and human insight together, you get the most value out of it.

[00:22:40] - [Speaker 1]
We are starting to see in cases where we have been able to do things like this, where we are able to deliver integrated automation built ground up where we rethink the process end to end from the conversation to the execution and fulfillment of the promise through the conversation. If it is looked at it in in a holistic manner and re you reengineer the process with the idea that there is an AI coworker along with a human coworker and that they're gonna work hand in hand, we are starting to see a significant cost savings to the tune of 60 plus percent, improved satisfy customer satisfactions of 80 to 90% for because of faster resolution or faster execution or fulfillment. But most importantly, the process becomes and the outcome of the process becomes extremely predictable and reliable, which is what the customers expect at the end of the day. And that requires a fundamental shift in thinking that AI is not a bolt on, but it is just a new way of working. And we need to redesign our way of working to include that coworker into the mix.

[00:23:55] - [Speaker 0]
Love that. And, of course, for I always try and give everyone listening a valuable takeaway. So if we have a listener listening anywhere in the world now who wants to move forward confidently but might feel somewhat overwhelmed by the the pace of change right now, is there a a mindset or strategic shift that you'd recommend that would help them turn good intentions into more predictable, meaningful outcomes and really hit the new year running?

[00:24:21] - [Speaker 1]
Yeah. Don't use AI for the sake of saying that I'm using AI or move away from being the tech first to the I would move towards outcome first model where figure out what the expected outcome is and look at it as how can I make it more predictable, more consistent, and then design a process to support that with keeping in mind that AI is an integral part of your operating system of how you do get that work done and the process executed and build the systems accordingly? From an HGS standpoint perspective, what we are doing for the customers is we are saying we'll do a ninety day proof of value, and that's what we call as realize AI, where we will come in, we'll pick two or three KPIs, take an existing process, redesign it, prove that you can execute the process with a consistent delivery capability and get a higher throughput, which is what is going to drive accelerated adoption and return on investment.

[00:25:27] - [Speaker 0]
Well, we've covered so much in just thirty minutes today. And for anybody listening who wants to dig a little bit deeper on this, learn more about how HGS will be able to help them create an almost boundaryless AI ready future, but not use AI for the sake of using AI, of course. Where do you like to point everyone listening?

[00:25:47] - [Speaker 1]
Oh, they can go to hgs.com, our website. They can definitely go to LinkedIn. Reach feel free to reach out to me on LinkedIn. I'm you can find me as Venk Corla on LinkedIn. And, also, you know, there's a big visibility program that we are focused on on LinkedIn, trying to educate our customers and put the audience on how you actually use AI to realize its most value and what is the approach you need to take for it.

[00:26:18] - [Speaker 0]
Well, I'll add links to everything so people listening can click on any of those in the show notes. And we covered a lot today in our conversation around applied AI led business transformation from hyper personalization, reunlocking the cloud, and AI readiness, and how companies can overcome some data challenges and so many other challenges that they're facing right now. But more than anything, thank you for you for sparing a little of your time today to share this story with people around the world. Invaluable advice and over two decades of experience as well. So thank you for your time today.

[00:26:51] - [Speaker 1]
Thank you for having me on your show.

[00:26:53] - [Speaker 0]
This is one of those conversations that reminds you that AI progress is rarely about the tools themselves. It's about outcomes, discipline, and redesigning how work gets done from end to end. And my guest shared clear examples of realized AI, rethinking cloud value, and why human oversight still matters as systems become more autonomous. I would say arguably, it matters even more now. So I'll include links into the show notes for anyone who wants to explore HGS's work or continue the conversation with them.

[00:27:28] - [Speaker 0]
And if this episode sparks any ideas or any bigger questions for you, don't just sit there letting it marinate. I love to hear what stood out for you, and I'd love to hear more about your own transformation journey and the lessons that you've learned along the way. You can get me at techtalksnetwork.com. You can leave a voice message. You can find out how to work with me.

[00:27:48] - [Speaker 0]
You can direct message me on any social channel at neil c hues. I'm the easiest guy in the world to find. But I have left you with lots to think about, so I'm gonna walk off into the frosty sunset today, and I'll be back again tomorrow with another conversation. Thank you for listening as always. I'll speak with you then.

[00:28:07] - [Speaker 0]
Bye for now.