Hello. Thank you all for being here. I'm Maggie Nolan. I'm the research analyst here at William Blair that covers IT services, including Genpact. I'm required to inform you that for a complete list of research disclosures or potential conflicts of interest, please visit our website at williamblair.com. Genpact is a global professional services, business process outsourcing, and solutions company, and we're excited to have here with us today, BK Kalra. He's the CEO of Genpact, so thank you for joining us, BK. We're gonna do a quick overview of the company. BK, I'll, I'll give that to you-
Mm-hmm
... to talk a few minutes and level set what the company is, who you are, clients you serve. And then we'll transition into a little bit more of a moderated fireside chat between you and I. Immediately after this session, we're going to be breaking out for additional Q&A in room Jenner B, so I encourage you all to join us there. And with that, I will turn it over to you.
Thank you, Maggie. Thank you, and, great to be here. Maybe I'll just do a quick introduction about the company. Genpact is a global professional services and a solutions firm, with about 100,000+ employees, in 30+ countries. We transform and partner with, global enterprises, many of them Fortune 500, and they engage us because of our deep domain expertise. We generate about $4.5 billion in revenues and about, $500 million in, operating cash flows. We approach the market, through two lenses. One is, Data-Tech-AI, where we intrinsically, design and build, solutions for our clients, leveraging leading technologies.
So think of in supply chain for a consumer goods company, how do we design a far more predictable supply chain and put that solution on the cloud? Or in another sector, take an example of insurance. In insurance, in Data-Tech-AI, there are many solutions, but one that bubbles to top in my head right now is building risk and underwriting solutions so that P&C underwriters in these large companies can assess the risk in a far more appropriate fashion. So we have routines and rituals around that and many assets that we have. And if I go to Digital Ops, and Data-Tech-AI represents about 45% of our business.
The 55% of the business, that's where we started, about 26 years ago, and that's now about 55% of our business. It is running and transforming operations for our clients. So running finance operations, running procurement operations, running supply chain operations, or core operations for a bank, or claims or underwriting. These are the large operations that we run for our clients, and that represents about 55% of our business. In anything that we engage with our clients, typically, Data-Tech-AI is at the spearhead of any solution that we bring in. In many times, we sign these 5, 7, sometimes 3, but a typical size, typical longevity of a contract is 5 years, 6 years.
They are not only digital operations or managed services contracts, but they have Data-Tech-AI as a solution where we design and build and then transform and run. And then there are many solutions where we are just doing design and building and handing it over to clients to run it, and but there's a lot of virtuous nature. So that's the characteristic of a business. Gen AI, and I'm pretty sure we will talk about it, is acting as a tailwind for our business and a total addressable market enhancer. And, really excited, as to where we are positioned and how we are pivoting on data and AI. I'll pause there.
Wonderful. Thank you, BK. So some of you in the room may not know that BK is relatively new to the CEO role. So I thought it would be, you know, interesting to talk a little bit about all the client conversations you've been having since coming into the role. What are you hearing from the client base in these early months in this role about what they need from Genpact, where you perform well, what they'd like you to evolve into? What sticks out in your mind?
Yeah, it's a great question, and yes, new in the role, but 25 years in making, 25 years in Genpact. I think, and you know, what made Genpact and many of us more relevant was always staying ahead with many of these client conversations, and that has continued even in this new role that I've taken. What we are hearing from clients is clearly how... You know, as you think of AI, Gen AI is certainly one portion of the conversation that they are constantly now talking about or are more agile to. But they are also, in all these large enterprises, know that it is not flick of fingers. It is not a magic wand.
A lot of everybody needs to understand flow of work, and that's where we come in. We understand the domain, and through running of these processes, we understand the end-to-end process, what goes in and how, certain exceptions are there. And therefore, really, working with them on infusing, not in just our current operations, in even in their operations, how we think of flow of work today, and what does that entail for tomorrow? And bringing all of those more newer edge solutions to table, so that they get transformed over a period of time.
And so maybe following up on that, dig into that a little bit more from a perspective of, you know, what's synergistic across those two segments that you were talking about, the two lenses as you referred to them. What's synergistic there for the company as well as for your clients?
Yeah. It's virtuous in nature, Maggie. What we see is, typically a lot of our conversations begin with data tech AI. A number of them begin from digital ops too, but we certainly bring in data tech AI in there. So when we have to think of a data tech AI solution, so client is having a particular set of a problem statement, so how do we think about. So take an example. I was giving an example of supply chain, and maybe for a CPG company. So the problem statement could be, hey, the products are not available on the shelf as frequently as they need to be, and the term they use is on-time, in-full.
The on-time, in-full rate is not as high as they like it to be. And while they have a lot of their products sitting in inventory in various factories, but they don't get to reach the shelves in time. And that could be a simple problem statement, which is a revenue enhancer for them, because in case the products are available on the shelf at an appropriate time, you know, whatever be the CPG product, you know, that can fly off the shelf. So how do we think about that kind of a issue? And that's where we have, again, understanding of the domain of a CPG industry, understanding of domain of supply chain, and look at the demand planning side and the supply planning side.
That's where we bring in a lot of our assets to first diagnose, then build a solution, and then propose a solution. A lot of time that converts into Digital Ops, where they are saying, "Okay, why don't you run this for us?" Like for a large CPG company, global CPG company, one of the majors, among top five, we run in most of their developed markets, we run their supply planning, demand planning process. And it started... These are again long-term contracts, but it started from a simple consulting exercise, I would say three years ago, four years ago. And now it is all of the demand planning, supply planning, logistics, transportation for this particular company is run by Genpact.
So it started with a Data-Tech-AI kind of an approach and then morphed into Digital Ops for us.
You've been entrenched in this for a long time, and, you know, we've seen more recently the segments being defined in the way that you just described, but this has been, you know, Genpact's approach for a long time to the business, and you know that very well. But I'm sure, you know, as you were coming up into your new role, you had plenty of time to think about how you want to change the business as well. And in these early days here, you've talked about your three plus one initiative. So can you talk to the audience a little bit about what that means, an overview, and then we can get into kind of how you're progressing against them?
Absolutely. So how we have framed our 3+1 as a strategic pillar for us is to improve the execution and agility of the entire Genpact team. Three of these initiatives are more client-facing, and +1, internally, we call it Client Zero, but how do we become our best credential so that we are inspiration to our clients? So the three client-facing initiatives, the first one is more focused on partnerships holistically, most importantly, technology partnerships, but other partners too. And how do we build data and IP solutions on top of partner products that we can use in a repeatable fashion is our first initiative around partners.
In each of these initiatives, we have built some lead indicators to see, are we steering the ship in the right direction, and, or do we need to do any corrections on the way? So as an example, I'll give you one of the lead indicators here, just more from an inflow standpoint. In first quarter of this year, we had 2.5-3x higher number of inflows from technology partners relative to the corresponding period last year. Early days, but I believe we are progressing well in that direction. The second client-facing initiative is data technology and AI, we call it, and we have very strong data and AI and tech capabilities, building AI capabilities furthermore. And how do we bring in all of those solution sets?...
into most of our client conversations. I do not know of a single client, and we work with many Fortune 500 clients, who says, "Oh, our data sets are pretty rationalized." Everywhere, data needs a lot of cleanup. Data engineering is always a big, big, big program. You cannot unleash any AI or machine learning or anything unless until you have harnessed the data pipelines. Genpact historically has been very, very strong. However, we are increasing our pivot on that and including working with partners here, with people like Snowflake or Databricks or many others, including Microsoft Fabric and many others that exist. So that's the second one. The third one is truly around simplification, and how do we interact and how do we engage with our clients in a far more simplified fashion?
For a $4.5 billion franchise, I think we were possibly going to become a little bit more complex, and what we are driving is many simplification initiatives. The first one that has taken hold is truly just approaching the clients as they buy it from us through a finite number of units and truly cutting the PNL in that fashion, and that is driving far more agility, far more accountability in our teams. And there are series of other simplification initiatives that will follow.
And then the plus one is, as I mentioned, Client Zero, where we are driving the same data and AI inspiration into our internal processes of finance, of HR, of technology, internal process of sales, and how do we become far more efficient and become an inspiration for our clients? There are already north of 50 use cases that we are engaged in internally that we are pushing the agenda on.
I definitely wanna get back to that, and I think we'll probably tie that into a broader discussion around AI too. But on the first three, maybe starting with the partnerships, are there partnerships that you feel are driving the majority of those inflows that you talked about? Are there partners that stick out, you know, in your mind as you know, must-haves that maybe aren't on the list yet, that you would like to look to add your capabilities there?
So, number of partners, you know, are quite intuitive, you know, that we all can think of, the hyperscalers or, people like ServiceNow, Salesforce, Snowflake, I mentioned, a few others. And there are then many, SaaS solutions, you know, including few in, finance, few in supply chain, people like Kinaxis or BlackLine or HighRadius. There are many of them that are part of our ecosystem. And then there are others which are, newer ones, you know, maybe from just data or technology or AI, that we are onboarding as well. So it's a whole program, that we are running.
And in each of these programs, we are also thinking about what are our own, data or IP assets that we are putting in on top of the partner products so that there is higher degree of repeatability, that, that we are, pushing for. And that's a little bit of a different approach from the past. But, clearly, I think we are in the domains that we are in, in the verticals we are in, be it in banking or insurance, there are many, many such partners that are becoming part of the ecosystem.
And then one more on kind of the changes that you're driving in the business, simplifying the sales team. Can you talk a little bit more about the go-to-market and whether or not enough time has passed that you are starting to see some benefit from this, and where you think it can go from here?
Yeah, I would say in each of these, it's still early days.
Mm-hmm.
But I'll give you one example, you know, apart from simplifying how we go to market and in a far more agile fashion, we also morphed a little bit of our variable incentive compensation for sales team and made it more even keeled between booking and revenues. It was possibly overindexed in one direction, so that, you know, we have far more even keeled process. So as Genpact, we have always done many times large deals or longer contracts, because that's where we came from. And as we are becoming more and more prominent force in data technology and AI, a number of those deals are smaller in nature, or... but they earn much faster.
We changed, doing certain of those changes so that they take hold, but they are still early days.
Very good. On the demand environment, you know, spending across the space has been a little bit muted, but you were able to update your outlook last quarter. We felt that some of that was tied to great execution on your part, but I'm curious what you're hearing from clients, how much of that is starting to materialize in the numbers? You know, is there an improved willingness to spend and engage in some of these interesting projects that are gonna be drivers for the years to come?
So maybe, maybe two-part answer, maybe. Point number 1, if I look at macro, I don't think macro has changed in any significant manner relative to what we saw at back half of last year or even first 6 months of this year. And macro is also driven a lot by where the inflation is, where the interest rates are, the expectation of X number of cuts, and I don't know how many cuts will happen, but geopolitics will, a number of those pieces stay on mind of our clients, stay on the minds of our clients. But going back to what is more in our control is our own execution, and that 3+1 is also guiding us as an execution framework.
With the lead indicators in each one of them, we saw a better progress in that. I think as we progress, as we continue to improve execution, hopefully we continue to make superior progress.
Very good. So now we'll, we'll get to the AI portion of the-
Okay.
-the questioning. Has to come up eventually, right?
Absolutely.
How are you thinking about automation, the application of AI, how that, you know, impacts some of the core work that you do in finance and accounting or supply chain, for instance, and what kind of tangential opportunity that creates for Genpact to strengthen the client relationship and seek more business with them?
Yeah. So let me first say that it is certainly not a magic wand. And both our clients and we deeply understand it, and it does take a far longer and a responsible way to have something like AI inserted in the workflow of any process. And our clients clearly understand that that one needs to understand the end-to-end process, end-to-end domain to truly engage AI and relevant applications into the flow of work. And it is not a simple asking a prompt or doing certain prompt engineering, and now you have this magic come out of it.
But having said that, a lot of our clients, at least this time round, given it is more pervasive, we can all see what can happen with a ChatGPT-4 or 4o. It provides a far more democratic way in which people are learning that, and therefore, it is providing opportunity for our clients to do certain use cases, certain proof of concepts, and also bring in certain proof of concept into production. And I'll give you one example for a large medical distributor. We signed a pretty large deal with them. It's of another Fortune 50 client of ours, and we signed a pretty large deal with them about this time last year.
And, as we were one of the portions of those deals is, so they are in a medical products business. So they distribute their products into various hospitals globally, be it in here or in Europe. So these products are certified by FDA or European Medicines Agency or various regulators across the globe. So, whenever this product is not working, then a 1-800 number call comes in. Now, we can call it as a call center, but there it's manned by various engineers. So if I call them call center employees, you know, these employees will walk out. So they are pretty astute people who understand these products in detail. But if they are so think of them as these products as FDA-approved products.
There are user manuals, service manuals, so it's a pretty structured data that exists. What we did in this particular instance is inserted AI assistance in the flow of work, so that when a call is coming in from a particular hospital, we know that, oh, these are the 7 products that are sold by this particular company of the repertoire of 50 products that they may have in this space. If this call is coming in from this particular hospital, must be for this particular product. As the call progresses, AI is learning and listening and providing assistance to this engineer on how to do the first-level resolution for the call so that the field service representative is not sent.
These are, again, now use cases that are getting into production, into the flow of work. Over a period of time, we are rethinking as to how the end-to-end process can work. But it'll be a gradual transition into a newer way of working, and I think both our client and us are learning as we are executing it. The other question you had is, hey, therefore, is it opening up newer vistas for you? There are so an example like this one is also therefore pushing us and pulling us into other conversations with this client or other clients like these. As well as, you know, there are clients which are setting up responsible centers of excellence. There wasn't a responsible RPA setup....
But given the nature of this particular turn of technology, many clients are engaging us in setting up these centers of excellence. You know, sometimes we are managing on for them, or sometimes we are just setting it up for them, and they are managing.
Have you found that clients, maybe think or have even tried to do some of this on their own first before they engage you? Or are they coming to you from the beginning saying, "This is a huge transformational trend, and we don't even know where to start?
Typically, it is more latter where our clients are engaging us from the word go. A lot of times we provoke that conversation; a lot of times they ask us to. And there's also an element of talent. You know, in this world, and people like us have over period of time built talent and are also known as a talent machine. So there are many ways in which clients engage us, but a lot more times it is more latter in your question where we get engaged upfront. And then there are instances where we are handing on certain proof of concept to them, but there are others where we are getting engaged in a more long-term fashion. But still early days, I would say that.
Sure. Like you said earlier, you're Client Zero, so they know that you're doing this internally as well. What has the success been so far doing this internally? We haven't quite yet jumped... Mike, the CFO, is in the audience. We haven't quite yet jumped to, "Wow, this is gonna impact the guidance and how we're thinking about margins." But the point is to create some level of efficiency. How are you monitoring that and determining whether or not you're successful for the long term in these endeavors?
Well, it's a great question, and I think our internal clear thesis is that how do we, in a short period of time, become an inspiration for ourselves and for our clients? And I'll enumerate one or two use cases. One of the use cases in our, again, procure-to-pay process, where a lot of times our suppliers, you know, it's not a massive base, but our suppliers come in, "Hey, where's my payment?" And typically we need to look into our system of record, ERP or Oracle or whatever. So we have already inserted AI assistant and GenAI, where, say, you know, it goes and checks based on either the call or an email received, instead of a person checking it. Somebody checks it: "Hey, where is...
You know, "Why is this payment not made?" And builds a, generates a content, generates a reply. Right now there's a human in the loop who checks it, "Yeah, this seems like great," and the machine is learning out of that. Or if there is a change that is happening, machine is learning again through that. So there are many such use cases that we are doing, including one on predictability of our, financials. So, so there are many use cases in play at this point.
Very good. So is that changing who you want to hire internally for your own back office? And is all of this changing who you're looking to hire for the client-facing roles? And if so, where are you finding this newer talent that's more focused on data and AI?
So both at both the places, and our thesis is that in any and every function, data engineers will become part of the ecosystem, and that's what our thesis. Internally, even in Mike's team now, there are X number of data engineers that have been hired, which didn't exist earlier. And we are both building and buying. Genpact is known for also building the talent. Right now, over 100,000 people are enrolled on our Genome platform on data and AI courses, 60,000 in more advanced courses. Even top 100 people are going through a structured applied AI program through places like MIT and Stanford. So we are changing the current DNA, as well as we are constantly hiring from different companies, ranging from Amazon to Google to others.
Very exciting stuff happening at Genpact. Welcome to the call. Thank you for being here today. We'd love to continue the conversation. You're all welcome to join. The breakout for additional Q&A will be in room Jenner B. Thank you, BK, and thank you all for attending.
Thank you so much. Thank you, Maggie. I appreciate it.