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Genpact Ltd
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Good day, ladies and gentlemen. Welcome to the 2023 First Quarter Genpact Limited Earnings Conference Call. My name is Gigi, and I will be your conference moderator for today. [Operator Instructions] As a reminder, this call is being recorded for replay purposes. The replay of the call will be archived and made available on the IR section of Genpact’s website.
I would now like to turn the call over to Roger Sachs, Head of Investor Relations at Genpact. Please proceed.
Thank you, Gigi, and good afternoon, everybody, and welcome to our first quarter earnings call to discuss results for the period ended March 31, 2023. We hope you had a chance to review our earnings release, which was posted to the IR section of our website, genpact.com.
Speakers on today’s call are Tiger Tyagarajan, our President and CEO; and Mike Weiner, our Chief Financial Officer. Today’s agenda will be as follows: Tiger will provide an overview of our results and an update on our strategic initiatives. Mike will then walk you through our financial performance for the quarter as well as provide our current thoughts and our outlook for the full year 2023. Tiger will then come back for some closing remarks, and then we will take your questions. We expect our call to last about an hour. Some of the matters we will discuss in today’s call are forward looking and involve a number of risks, uncertainties and other factors that could cause actual results to differ materially from those in such forward-looking statements. Such risks and uncertainties are set forth in our press release.
In addition, during today’s call, we will refer to certain non-GAAP financial measures that we believe provide additional information to enhance the understanding of the way management views the operating performance of our business. You can find the reconciliation of these measures to GAAP in today’s earnings release posted to the IR section of our website.
And with that, let me turn the call over to Tiger.
Thank you, Roger. Good afternoon, everyone and thank you for joining us today for our first quarter 2023 earnings call. Today, I’ll talk to you about our financial results for the first quarter of 2023 and about how we believe we are uniquely positioned to partner with our clients in leveraging generative AI, large language models or LLMs and broad AI and machine learning technologies.
So first, our results for the first quarter were solid and reinforce the powerful interlinkage between our Data-Tech-AI and Digital Operations services that leads to many new opportunities to continue to create value for clients and growth for us. One of the most exciting was the record level of first quarter bookings we signed in the quarter and near-record pipeline we entered the second quarter with.
In the first quarter of 2023, we delivered on a constant currency basis total revenue of $1.089 billion, up 4% year-over-year; Data-Tech-AI service revenue of $485 million, up 6% year-over-year; and Digital Operations services revenue of $604 million, up 3% year-over-year. Additionally, we delivered adjusted operating income margin of 16.4%, expanding 140 basis points year-over-year, and adjusted diluted earnings per share of $0.68, up 13% year-over-year.
Over the last 60 days, I have had the opportunity to speak to a 150-plus C-suite executives of large global enterprises, and I’m hearing a consistent set of themes. They all face the reality of having to do more with less, so they remain focused on cost takeout and cash flow improvements. At the same time, they want to allocate resources to their most critical long-term transformational programs, including starting to think about ways to leverage generative AI, LLM and, more broadly, AI in their business. It is this backdrop against which we set the net new record for our first quarter bookings, including 5 large deals, with total contract values greater than $50 million. While almost three-quarters of our bookings were from our priority accounts, we also added 17 new logos this quarter with an average contract value of about $6 million compared to the $3 million level for the full year 2022.
Let me give you some color on the 5 large deals because there are some consistent themes there. First, for a leading technology platform provider in the automotive industry, we are modernizing their application stack, moving it all to AWS cloud, redesigning their finance, sourcing, procurement and customer service operations to improve efficiency, and more importantly, access clean data that can be orchestrated to leverage AI-based solutions. The strategic objective is cost reduction, but also improvement in end-to-end dealer and customer experience that will drive growth.
For one of the largest global consumer goods companies, we will redesign, transform and run their full order-to-cash and source-to-pay services end-to-end across 100 plus countries to dramatically reduce costs and improve working capital. Even more importantly, using our AI and cloud-based Cora AP platform, we will standardize and automate master data management and contracting, which will then allow use of AI for better decisioning on buying supplier choices and sustainability as well as customer segmentation and customer credit.
For a large industrial equipment manufacturer with a very fragmented technology landscape across 50 plus countries, we will redesign and run their end-to-end global finance processes and thus enable them to fully separate into two listed entities. We will combine digital partner technologies with our domain depth and process expertise, which will then allow data to be leveraged for AI and generative AI, delivering savings and commercial benefits. A leading global med tech company changed its strategy of many years running their own global captives through now partnering with Genpact. Their motivation was to leverage the latest AI, generative AI and LLMs, for which they’d first need access to clean data consistently across 80-plus countries. We are taking over their global captives to not only drive costs down, but also deliver a commercially competitive advantage to drive their growth.
And finally, for a global beverage company, we are taking over their European capital operations and they, too, have decided to shift strategy to leverage our know-how in process and domain to be able to embed AI solutions by rapidly modernizing their processes that deliver AI-driven insights to improve cost, experience and outcomes. Many of these deals incorporate non-FTE-based commercial models, particularly transaction-based pricing that aligns all our goals to drive rapid leverage of new technologies, including generative AI, machine learning and LLMs.
Data-Tech-AI services, where we design and build solutions to transform our clients’ businesses, grew 6% on a constant currency basis. This was driven by ongoing momentum for our emerging services, including supply chain, sales and commercial and risk that collectively grew 9%, partially offset by a slowdown in the discretionary portion of our shorter-cycle advisory work, as we had expected. Digital Operations services, where we digitally transform and run our clients’ operations continued to deliver steady results in the first quarter, growing 3% on a constant currency basis.
We have made great progress on all 5 of our initiatives to deliver our long-term goal of 10% plus organic top line growth and expanding our AOI margin at a more meaningful pace than historical levels through 2026. First, revenue from our priority accounts grew 3% during the quarter and represented approximately 62% of total revenue. Despite seeing some near-term macro pressure, our investments in these clients are paying off as the majority of our first quarter bookings were from our priority accounts.
Second, we continue to deepen our relationships with our cloud technology partners with whom we co-innovate and create joint IP and AI solutions. For example, with ServiceNow, we have embedded our domain and data depth to help transform and automate manual processes in areas such as procurement, accounts payable and risk management. The strength of this partnership is evidenced by our co-sponsorship of ServiceNow’s flagship event, Knowledge ‘23, taking place next week, where we will present our procurement-as-a-service offering. And with Kinaxis, we have expanded our preferred partner status with capabilities in Europe and Asia to more effectively sell clients with critical supply chain solutions globally.
Third, we are investing in new operating centers in Tier 3 cities, particularly in India, giving us access to great talent pools. We have 2 centers fully operational and a third one getting ready for quarter 3 launch. Fourth, we continue to drive outcome and transaction-based commercial models that now represent 13% of total revenue on our path towards 20% by 2026. In fact, 28% of our bookings in quarter 1 had non-FTE pricing.
And lastly, we’ve expanded our large deals team to take advantage of increasing number of large deal opportunities, and the results are showing in large deal bookings as well as pipeline strength in large deals. As expected, our attrition continues to drop, now at 24% during the first quarter. We saw this positive trend across the company at all levels, skill sets and geographies.
Adjusting for involuntary attrition and employees with less than 3 months of service, our attrition was even lower at 19%. During the quarter, we hired 8,000 new team members across the globe. It is clear that the opportunity to learn new skills and solutions and work on digital, generative AI and machine learning technologies in many of our client engagements is a talent attractor and is also driving attrition down.
Now, let me step back and talk about the rapid evolution in the last 5 months of generative AI and why we believe we are one of the best positioned in our industry to take advantage of this next wave of AI. While AI has been in our DNA for years, generative AI and the recent breakthrough is an exciting next wave, which will further leverage our capabilities.
As we look back over the last 5 years, it is important to emphasize how central a role AI has played in our success with clients. Over these years, we have developed and refined our AI capabilities, enabling us to create innovative, industry-specific solutions for our clients. We believe this has positioned us as one of the leaders in the AI space in our industry, giving us a competitive advantage for long-term growth and margins.
Some of the key milestones on this journey include the following. In March 2017, we made our big move into AI when we acquired RAGE Frameworks that allowed us to bring natural language processing and natural language generation technologies into our services. This helped us launch our AI-driven solutions for financial services using the technology to read financial statements that we then deployed in our loan processing operations and financial reporting services, delivering a 60% reduction in cycle time for loans and a 40% cost reduction.
We then built and deployed AI for demand forecasting and inventory optimization solutions for the CPG sector, reducing stock-outs and inventory holding costs. Three years back, we deployed AI-driven predictive maintenance solutions for a number of our large manufacturing clients where we run these operations, leading to a 30% reduction in maintenance costs and improved equipment effectiveness. We also created at that time our AI center of excellence, focused on driving innovation, talent development and building new solutions with scale and industry partners.
We then expanded our suite of AI solutions to include natural language understanding, NLU. This allowed us to have AI-powered customer service chatbot solutions for the banking, insurance and the high-tech industries as part of our end-to-end services. This improved customer experience drove up retention and renewal rates for our clients as well as cross-sell and upsell for them. In every one of my 150-plus C-suite client conversations that I referenced earlier, we talked about generative AI and how all our clients are challenged with where to start, how to prioritize, what steps to take and how to maximize value. The biggest realization for most enterprises is that their highly fragmented and distributed data sets and processes will prevent them from starting this journey.
We saw the exact same thing happen when RPA and low-code workflow on the cloud became prime time 7 years back. We embraced these technologies, built capabilities and incorporated all of them into our services and operations. We now have close to 8,000 bots running in our operations and deployed by us on client sites. We also have more than 250 clients whose operations run on Genpact’s Cora platform, our AI cloud-based digital platform that on the average, handles more than 20 million transactions a month.
We’re now seeing the same co-innovation journeys in AI to consolidate processes and data, clean them up, standardize them and then deliver services with these AI solutions built into them. This is clearly one of the big drivers for the surge in our inflows and bookings, the urgency all our clients have to get to the stage of being able to leverage these technologies to create value. They often need to fix their basics, their legacy technology, processes and data in order to be able to leverage AI.
Our differentiated value proposition as their transformation partner is built on five pillars of strength. First, domain expertise. Our deep understanding of various industries has allowed us to create industry-specific AI solutions that address unique challenges and understand all exceptions and edge cases. Second, scalable AI solutions. The AI solutions we develop can be easily adopted and scaled across different industries and functional areas. Third, continuous innovation. We believe that continuous and rapid innovation cycles are key to maintaining an AI and generating AI advantage. We invest consistently in R&D to ensure that we are constantly experimenting with AI and generative AI use cases with our clients.
Fourth, talent development. Our AI center of excellence has a talented team of data scientists, engineers and domain experts. And our 3-year-old data-abridged reskilling program had 70,000 people get certified last year. This provides the base talent pool to build our expertise in a talent-short market. And finally, strategic partnerships. We have established strategic partnerships with leading technology providers to enhance our AI capabilities. Our domain process and data expertise make us a uniquely differentiated strategic partner for many of them.
Let me share two specific examples of how these five pillars and our history have made us the partner of choice for finding ways to leverage AI. We built and deployed an AI-powered customer churn prediction model for a leading Software-as-a-Service company. This model uses machine learning algorithms to analyze customer behavior data, product usage patterns and other relevant factors to predict the likelihood of a customer churning. As a result, we identified at-risk customers and have now implemented targeted retention strategies in our operations, leading to a 30% reduction in churn and a significant increase in customer lifetime value for our client. For another client, we went back to 10 years of customer sentiment data that was being captured to build a very powerful Net Promotor Score prediction engine that we then used to drive specific, tailored marketing campaigns using generative AI to aid in customer service.
For a global manufacturing company, we used AI-driven predictive analytics to generate more accurate revenue and expense forecast as part of our FP&A services, considering various internal and external factors such as market trends, economic indicators and historical financial data. This improved accuracy enabled the company to make better informed strategic decisions, optimize resource allocation and ultimately achieve a 25% reduction in forecast tariffs, leading to increased operational efficiency and financial performance.
We are still in the early days of this current wave of use cases using generative AI and are in rapid prototype and experimentation mode with our clients. The initial wave of opportunities, are concentrated in help desks, customer service and research work particularly in unregulated industries. As we have demonstrated with technologies such as RPA, dynamic workflows and even earlier iterations of AI, every technology wave expands our total addressable market and allows us to do more complex work for our clients.
With that, let me turn the call over to Mike for a detailed review of our first quarter results.
Thank you, Tiger, and good afternoon, everybody. Today, I’ll review our first quarter results and provide you with an update for our full year 2023 financial outlook. Total revenue was $1.089 billion, up 2% year-over-year or 4% on a constant currency basis. Data-Tech-AI services revenue which represents 45% of total revenue, increased 4% year-over-year or 6% on a constant currency basis, largely driven by continued growth in our cloud-based data and analytics solutions across our focused areas, including supply chain, sales and commercial and risk services. Digital Operations services revenue, which represents 55% of total revenue, was flat year-over-year or up 3% on a constant currency basis, primarily due to deal ramps from existing and recent wins.
From a vertical perspective, financial services increased 9% year-over-year, largely due to continued strong demand in our risk management services, leveraging data, analytics and AI. Consumer and Health Care declined 4% year-over-year, largely driven by lengthening large deal cycles, lower Data-Tech-AI services revenue and the impact from a recent divestiture of a business we had previously classified as held for sale in 2Q 2022.
High Tech and Manufacturing increased 3%, primarily driven by new deal ramps, partly offset by a notable reduction in operational scope of a priority high-tech account. During the year, we grew the number of client relationships with annual revenue greater than $5 million from 150 to 175. Clients with more than $25 million in revenue increased from 31 to 36, including a recent expanded relationship that brings a number of clients with over $100 million in annual revenue from 3 to 5 in the same period last year.
Adjusted operating income margin expanded 140 basis points year-over-year to 16.4%. This better-than-expected performance was largely due to timing of sales and marketing investments that we expect to pick up during the remainder part of the year as well as general operating leverage. As a reminder, our adjusted operating income margin during the first quarter of 2022 included the impact of the business designated held for sale that was recently divested.
Gross margins in the quarter was 34%, down from 35.8% during the same period last year, primarily due to higher-than-normal severance costs related to workforce reductions related to our discretionary portion of our short-cycle advisory work, higher travel costs and investments supporting new deal activities. Excluding the severance charge I just mentioned, gross margin for the quarter would have been more in line with the level we reported during the fourth quarter of 2022.
SG&A as a percentage of revenue was 19.9%, down 230 basis points year-over-year, largely due to timing of investments that we expect to ramp up through the remainder of the year and overall G&A leverage. Adjusted EPS was $0.68, up 13% year-over-year, from $0.60 in the first quarter of last year. This $0.08 increase was primarily driven by higher adjusted operating income of $0.08 as well as the impact from lower outstanding share counts and lower net interest expense of $0.01, partially offset by a $0.02 impact of year-over-year changes in FX remeasurements. Our effective tax rate was 23.4%, in line with 23.5% rate last year.
Turning to cash flows and balance sheet. During the quarter, we utilized $34 million of cash from operations compared to utilizing $114 million during the same period last year that was in part driven by a significant expansion in DSOs in the first quarter of 2022, reflecting clients reverting to historical payments to take advantage of interest rates. Year-over-year, our DSOs expanded by 1 day to 83 days. We expect our DSOs to remain in the low 80-day range for the remainder of the year.
Cash and cash equivalents totaled $552 million compared to $647 million at the end of fourth quarter 2022, reflecting our annual incentive compensation payouts that occurred in the fourth quarter and the return of $55 million to shareholders. At the end of the quarter, our net debt-to-EBITDA ratio for the last 4 rolling quarters was 1.4x, in line with our preferred 1x to 2x range. With the undrawn debt capacity, the existing cash balances, we have ample flexibility to pursue growth opportunities and execute on our capital allocation strategy. During the quarter, we continued to execute on our program of more regular cadence of share repurchases and bought back 631,000 shares for a total cost of $30 million at an average price per share of $47.57. We also paid out a total of $25 million in dividends.
Capital expenditures as a percentage of revenues equated to approximately 1% in the quarter. We anticipate a higher level of investment activity throughout the remainder of the year related to new large deal signings as well as opening of new operational centers associated with our hybrid delivery model.
Finally, let me provide you with an update on our full year outlook. We continue to expect to have revenue between $4.64 billion and $4.71 billion, representing year-over-year growth of 6% to 7.5% and 6.5% to 8% on a constant currency basis. We continue to expect our full year 2023 adjusted operating income margin to be approximately 16.8%, aligned with our outlined strategy of driving margin expansion at a faster pace than we’ve done historically.
I want to take a moment to provide some additional color on our gross margin for 2023. We are expecting our underlying gross margin to improve approximately 30 basis points in 2023, primarily due to scaling of our Data-Tech-AI services as well as the impact of off-cycle pricing increases we obtained last year. However, this benefit will be offset by the impact of our recent large deal wins that have an onshore delivery that has inherently a lower gross margin in the early years of such contracts. Therefore, we are anticipating gross margin to be relatively to slightly down for 2023 compared to the 2022 level.
While these new large deals inherently have a lower gross margin than the company average, over time, as we digitally automate solutions, leverage resources, we expect their profitability to increase over the contract period. Over the deal terms for these agreements, we expect overall adjusted operating income margin to be in line with the total company level due to lower SG&A investment required to support delivery.
We now expect our full year 2023 effective tax rate to be at the higher end of our prior 24% to 25% range due to lower level of discretionary tax benefits available than initially anticipated in the overall jurisdictional earnings revenue mix. Given the outlook I just provided, we continue to expect adjusted operating income per share for the full year 2023 to be between $2.92 and $2.99.
Lastly, let me share some thoughts on the expected revenue and adjusted operating income margin cadence throughout the remainder of the year. We now expect revenue for the year to be more back-end loaded than we initially thought due to deal ramp activity related to new large engagements, offsetting the slowdown in advisory work that we anticipated will continue in the near-term. Therefore, we continue to look for low single-digit quarter-over-quarter growth for the second quarter, expanding to mid to high single-digit growth during the latter part of the year.
For a year-over-year perspective, we continue to anticipate growth in the back half of 2023 will be relative to the first half due to the ramp-up profile of the recent deal wins for an easier comparison. Given the strong adjusted operating income margin we generated in the first quarter, we believe we’re in a better position to expand our adjusted operating income margin to 16.8%. This outlook includes absorbing higher levels of both R&D and sales and marketing expenses throughout the balance of 2023 and investing the savings related to cost actions we took in the first quarter to support new deals.
In terms of progression through the year, we continue to expect adjusted operating income margin to flow through our typical pattern of expanding sequentially with revenue, however, at a less acute increase during the second half than in our previous expectations. Our full year outlook, informed by our visibility into the second half of 2023 with accelerated growth driven by large deal bookings as well as our robust pipeline, gives us confidence in our ability to achieve our multiyear strategy of driving sustainable 10% plus organic revenue growth and expanding adjusted operating income margin more meaningful pace than we have in the past throughout ‘22 through 2026.
With that said, let me turn the call back to Tiger.
Thank you, Mike. As we look at the way our 2023 has started and the momentum we see, it is clear that the capabilities we built organically and added inorganically over the years are even more relevant for our clients. Every new technology breakthrough that has become available has only increased the need for partners like us to help our clients leverage these technologies to add value to them at scale. We’ve already seen a greater urgency and desire in our clients and new targets to transition to new operating models, get their arms around their data that is clean, well defined and well understood, that then becomes capable of feeding these models to create outsized value.
In summary, every one of the 150 plus clients I’ve met in the last 60 days expressed interest and intention to have Genpact be their partner in using and implementing AI and generative AI. We started our journey in AI with the acquisition of Rage in 2017, and that spurred a number of AI and machine learning services and solutions. Three, we view AI as both an opportunity for internal efficiency and margin enhancement and expansion of services to clients with an increasing TAM. Since the announcement of ChatGPT and other generative AI, our pipeline intensity have gone up. We’re already working with a number of real use cases with clients in our operations and their operations as well. That is why, despite some near-term pressures in the small portion of our advisory business that is more discretionary, overall demand for our services could not be more robust.
I’m pleased to share that we recently published our 2022 Sustainability Report, which is available on our website, highlighting the ongoing progress we have made across our ESG initiatives that support our long-term financial targets. And at the same time, we are helping many of our clients make progress on their ESG goals using our solutions.
With that, let me turn the call back to Roger.
Thank you, Tiger. We’re now ready to take your questions. Gigi, can I please ask you to give the instructions?
[Operator Instructions] Our first question comes from the line of Tien-Tsin Huang from JPMorgan.
Hey, thanks. Good afternoon to all. Tiger, it sounds like you’ve been talking to a lot of executives, which is great.
Thank you.
I wanted to – on the – yes, it’s a lot of talking. I think the – good for business. I think on the five large deals, I have to ask on that. So good color provided there. In terms of the ramp, I mean, it sounds like some of this will ramp in 2023. How long will it take to get to full ramp? Is there risk involved in some of these transitions? It sounds like there could be some rebadging and taking over some of the captives. So just trying to better understand the – both the impact on the P&L in the short-term as well as the risk that maybe comes with it. It sounds like great obviously overall.
Yes. No, thank you, Tien-Tsin. And yes, just a reaction to the 150 clients, that’s a lot of talking. I know. But the environment is such that it’s good to talk to get full insight as well as engage because everyone is trying to figure out answers. So to your question on the five deals, the deals are, in terms of structure, very similar to deals we’ve done before. A couple of them, specifically, I called out, do have material rebadge components. These are companies that have made strategic moves, where they say that, actually, we need a partner to help us, even though, earlier, we thought we don’t – we didn’t. We’ve done 25 plus of such rebadge deals in our history. And I would say, bar none, they have gone really, really well. No surprise because we taught ourselves out of GE. I guess, now, 18 years back, but that DNA still remains. So we don’t see risks associated with that. Obviously, these are complex deals, the ramps are complex, but we know this business. We know the way these use our structure. We’ve done this before. So there is nothing extraordinary in all of these. So we expect regular cadence to build up. Unfortunately, none of these deals really materially contributed to anything in the first quarter. We will start to ramp sometime in the second quarter, and actually, more into the second half of the year. In fact, some of them will tail up – I mean, the tail ramp will continue into the first half of next year, given that these are large and complex. So no, I mean, I think more to call out than that. We’re obviously very excited at the way quarter one went on that.
Okay. Great. And I think – I’m glad you went to the generative AI because I know we and my peers are all getting questions on it. How would you – Tiger, we talked about a lot of different tech trends and waves over the years. I know you mentioned RPA. Anything you would compare this to, this generative AI opportunity, to – relative to past waves, either in terms of scope or opportunity or just impact with enterprises as you see it so far?
I’ll start by saying it’s very, very exciting. I’m going to say something which I’ve said before in reasonably public forums, which is I always believe RPA was never going to be the ultimate answer. And I already believe that RPA is a very simple, easy solution at kind of at the bottom of the food chain. And we’ve always said that AI is at the top of the food chain. So this is AI. So this is what all of us in the industry, all of our clients and everyone who studied, worked, and knowledge building and the use of services, have been asking for and saying, one day, we will get there. Well, the breakthrough finally happened on language, so we are very excited. And actually, we started working on solutions with our clients in the fourth quarter because GPT 3.0 I know is available, and our people and our teams have been working on those. Our AI center of excellence worked on those. So it is different than RPA, but there are lots of similarities. I think it is important to understand that while the opportunity is huge and the value-creation opportunity is real, the challenge that every enterprise faces is, first of all, where do I start? What do I prioritize? And what value can I get? Which then determines where do I start.
The second is whichever area you choose to start, because that’s the best value and you can get it fast, you better have your data lined up. And if you look at typical large enterprises that have been around for many, many years and are hugely successful, it’s the one thing that they have been on a journey on, and that journey continues. Every one of the five deals that we signed in the first quarter, every one of the deals in our pipeline that we are very thrilled about are all around helping our clients get their arms around data, which means standardize, consolidate, bring it all together, clean up the data. And that’s the journey. And who’s best positioned to do that? Companies like us. So I think this is a real opportunity and a tailwind for us, exactly what we saw in RPA. I remember 2016, late 2015, 2016, I think we all read really expert reports that said RPA will destroy BPO. And we – I remember we saying, no, RPA is going to expand TAM. That’s exactly what happened. We believe generative AI and broader AI is just a real opportunity to expand TAM and create value for our clients.
Thank you for share that, Tiger.
Thanks, Tien-Tsin.
Thank you. [Operator Instructions] Our next question comes from the line of Keith Bachman from BMO.
Hi, thank you. I’m going to follow Tien-Tsin. Tiger, you’ve always had thoughtful perspective over years and years and years. And your thesis here on generative AI is essentially that, while it makes each of your employees more productive, i.e., more efficient and based on the team FTE model or P x Q model on the people, it will hurt revenue growth, but you’ll capture incremental work to still lead to total growth, right? That’s just – and that’s been true for your whole approach to AI for a while. I wanted to drill in, though. As you think about that though, is – there is a couple of variables that I want to try to tease out as generative AI comes more to the forefront and the quality improves. But is there a risk of – that pricing gets further eroded in this process that may be different than other parts of AI are? Do you see it all – all the pressures as being similar?
So actually, Keith, great question. And by the way, the way you started the question, you made me feel old, but...
Well, that’s just me because I’m old, too, because I’ve listened to you for a while.
No, no, thank you for that. So good question, by the way. The way we think – two parts to the answer because there are two parts to the question. One, exactly to your point, we see – and we’ve seen this is not just with RPA, but it’s even moving to the cloud. And actually, if I go back, 15 years back, there was no RPA. We saw that with Lean and Six Sigma. You drive productivity, you take labor out, you therefore reduce the amount of work that needs to be done, you get more work. And we’ve always said that this industry, our industry and our services, continue to be underpenetrated. Now in the meanwhile, we’ve added new services in the same industry verticals and across our clients.
So RPA, same story, while we have 8,000 bots that are running, the reality is that those same clients, in fact, I think if I do a regression analysis, which we’ve done, of where are those bots most effective? The most effective clients are those where we have the bots and we’ve grown with the client. So it’s like a – the two happen almost together because you’re clearly adding value to the client and then you are expanding the total addressable market and capturing more of that. When you do that, you get better pricing. And what is interesting, today, in our business model versus 2016 when the RPA wave started, is that our alternate commercial models, commercial models that are built on transaction-based or outcome-based or value-based pricing, which was very, very low in those days, is still only 13%, but is on a growth trajectory. We called out 20% for 2026. I wouldn’t be surprised if we actually break through that much earlier. The bookings for this quarter itself, indicates a 28% total TCV being alternate commercial models that – we’re going to get that. And when we get alternate commercial models, it’s a win-win. The client wins big time, and we do win as part of that, because that’s how total win-win gets created. So we expect, in this journey, margin to be accretive, which is part of the reason why Mike and I said in the long-term plan that that’s going to happen.
Right. Right. And so sorry, Tiger, just to drill down. This, you think, changes the nature – I mean, might your project work, so to speak, go up as a percent of your total revenue because of AI versus just doing time and materials?
I don’t think, Keith, I would describe it as project work. In fact, probably almost all the examples I gave, both in the deals as well as in the solutions, almost all of them are AI solutions that we’ve embedded into the services that we deliver. In some cases, it’s services that we’re already delivering. In which case, we go to the client, we put together a proposal and a solution, then we work together with the clients because sometimes we need data from them. Sometimes we already have the data because we run the operations. And then we embed that into our operations, deliver the value and get a piece of that value and then get more growth.
However, at the same time, there are real opportunities that we do encounter where the client says, why don’t you also come in and do X, Y and Z in my operations using your AI technologies? We did that with RPA. Out of the 8,000 bots, I think about 1,500 of them are actually deployed with our clients. The other 6,500 are with us. So we see that happening in AI as well. I don’t think the ratio is going to change. I think the ratio is going to continue to be what it is. Because the real power of AI for us is when we go and make a big proposal on a big deal, it includes AI-driven solutions in it, just as today, it already includes RPA in it.
Got it. Got it. Okay, well, I respect my colleagues. I am going to see the floor. Many thanks, Tiger.
Thank you, Keith.
Thank you. [Operator Instructions] Our next question comes from the line of David Koning from Baird.
Yes. Thanks so much. Nice bookings this quarter.
Thank you, David. Thank you.
Yes, yes. And – can we just kind of go back through to – I know it sounds like you’re going to exit this year back at kind of the normal 10% plus run rate, but there is kind of this four-quarter period of more like mid-single digits. And I know that’s driven by a little lower in the Data-Tech-AI that started kind of Q4. So can you just kind of bridge between like the normal 10% plus growth in the business to why for four quarters as a mid-single digit? I mean, is it just one or two clients? Just – because really, the genesis of the question, just so we don’t worry that there is something getting a little bit disruptive in the business.
No. So I’ll start off, and then Mike can answer it. But I’ll just say even as we entered the year, when we started talking about the year in the first quarter this year, when we guided for the year, I think Mike and I have both said that the year is going to see a little difference between first half and second half because we expected first half to be higher growth – second half to be higher growth than the first half, given that the prior year, the comparison was going to be – comparison of the first half of 2022 was in the first second half of 2022. We had set that expectation. And as we entered the year, we also saw discretionary spend in advisory work, particularly related to things like digital marketing or experience and so on, come down in the fourth quarter, and we expected that to continue. And we planned for that, and that has rolled through.
Mike also called out in his remarks the reduction in operations scope for one of the larger high-tech clients. That’s also got factored in, and that comes through fully – actually, most of it comes through in the first quarter, so it obviously comes through in the first half. The deals, the large deals, we actually expected, if you remember, to sign some of them in the last few days of fourth quarter. They got pushed out in the first quarter – into the first quarter. We did sign them, but we signed them towards the end of the first quarter. So now the ramp is really, for lack of any other word, a pretty significant ramp as we go through the year into the third and the fourth quarter.
I’m going to have Mike answer that.
Yes. The only thing I’d add to that, as you alluded to, that reduction in scope of that client, which we did have in the forecast on our outlook that we provided earlier. What I would say is that when we do look at our business, we will continue just to look at it just on a 12-month, 18-month and 24-month rolling average, right? And then when you take into account these large deal wins, feel very comfortable. When all is said and done, we will be at that blended 10% rate over these 2 years. Again, we’ve always said some years will be a little better. Last year, it was 11% plus. This year, we’re estimating it to be a little shy of that. I think when you wait all of this out, it’s going to be back on trajectory to do even better than that.
Well, nice. So no, that’s great to hear. Good. And then, I guess, just the second question. It looks like just – there was a cash flow item just that you did divest that business, I think you mentioned in the prepared remarks. Can you say again how much – how big of an impact it is to revenue when you divested the business?
I’m sorry, one more time, I couldn’t hear you. Cash flow and?
Yes, I’m sorry. The divestiture, how much revenue impact does that have?
How much revenue? Yes, we’re not disclosing that, but it makes up a substantial amount of the delta between what our normal trajectory is in our growth rate and where we’re forecasting this year, if you want to kind of back your way into it.
Got it. Thank you.
So again, the other big put and take is, as Tiger alluded to, the timing of all this, right? Particularly, these large deal wins literally came at the end of the quarter. We anticipate them a little sooner. That large deal ramp down, so to speak, happened in the earlier part of the quarter. So in the end, it all washes through.
Got it. Thanks, guys. Appreciate it.
Thanks, David.
Thank you. [Operator Instructions] Your next question comes from the line of Maggie Nolan from William Blair.
Thanks. So you’ve obviously had some tracks in the large deals, but you also mentioned that you’re still seeing elongated large deal decision cycle. So I’m curious what your expectations are for the remainder of the year for deal sizes?
Yes. So Maggie, great question. We haven’t seen any downward trend in the large deal cycles. They have been elevated for a couple of quarters. They continue to be elevated at the moment. They are not increasing further. That’s the good news. I don’t want to crystal ball guess, so I don’t think I would expect it to rise up any further. Right now, I wouldn’t say it’s going to go down. But as everything settles down and people get into a cadence, as hopefully, things like inflation and supply chain gets a little bit more stable, I would expect some of that elevated cycle time comes down a little bit because it is elevated and has been elevated. We’ve assumed that, that continues through the year. We’ve not made any other assumptions.
Great. Thank you. And then on the outcome base, you put a target out there, I think, for 2026. What does it take internally in the business to drive towards that target? And then it sounds like maybe the opportunity there is that it would also be margin-accretive. Is that accurate?
Yes. The second part of the answer is a simple one, Maggie. Our history shows that every time we enter into a contract that has that component, and it could take various forms. It depends on the type of service, etcetera, we tend to really create win-win-win across the board. Everyone wins. In fact, the third win I talked about, if a client wins, we win. But actually, the talent that works on it, they get really excited because they actually see real value being created, etcetera. So it’s a real opportunity. We think, as we continue down that path, it’s always been a challenge to get our clients to agree to those types of commercial models. It’s taken time over the years to actually get a number of clients there. But these things have a way of gathering momentum. We believe that, that momentum is beginning to gather. And one of the drivers of that momentum are things like AI and generative AI. Because what happens when you do that is you are really looking for outsized value creation. And in that outsized value creation journey, you really need a true partnership between the client organization, the access to that data they have, the standardization of the data, and our domain expertise and process expertise and technology and digital expertise and analytical insights to bring all of that together to operationalize it and deliver value. We’ve seen that in insurance claims. We’ve seen that in insurance fraud. We’ve seen that in sourcing and procurement. We’ve seen that in order-to-cash. We’ve seen that in financial planning. We see that again and again and again.
Customer service. We think there is a real opportunity in customer service. And one of the most interesting things about customer service is that we’re not a big player in customer service. So think about what we could do. We could disrupt the customer service market by actually building solutions and taking them to our clients across financial services, high tech and a number of other customer service arenas.
Thank you, Tiger.
Thanks, Maggie.
Thank you. [Operator Instructions] Our next question comes from the line of Ashwin Shirvaikar from Citi.
Thank you. Hi, Tiger, Mike. Good to speak with you guys. Let me start with bookings. Obviously, good bookings there has been mentioned a few times on the call here. The question I want to ask is with regards to revenue conversion, and not so much the proposed timing, but the confidence that, that timing will hold. Because what we’ve kind of seen in the industry is a number of companies are coming up with tiers of big crash bookings number and then it doesn’t really ramp at a historical or normal pace. So in terms of just sort of confidence levels with regards to that. And then the flip side of that question is, in general, what’s sort of flowing more smoothly in terms of revenue conversion for you? What type of things are getting pushed out from a functional perspective?
Yes. So the first part of the question, Ashwin – great to hear you, the first part of the question that you asked, I know the reference to large deal bookings and not converting into revenue that we’ve also heard based on public commentary in the industry. We haven’t seen that in our business. In fact, I have to go back into history to think about where it happened, and I would struggle to really come up with a meaningful material answer. And the reason for that is because our business is a little different. Our business is a little different. In fact, I guess, on this topic, it’s very different. When we talk about the kind of services we talk about, it is not something that you can go back and forth on. It is not something that you can take likely in terms of a decision. It is not, I am going to sign up a $200 million TCV deal, why don’t you start with $50 million, deliver it, then the next $50 million comes, then the next $50 million comes. Technology projects sometimes have that cadence. A number of our deals have technology embedded in the services, for sure. In fact, they have data tech AI, they have AI embedded, etcetera. But the deal is the service, and it includes announcing to the global team that the client has that the service ownership is changing. Part of the reason why some of these deals really take long is because you need CFO to obviously agree, then you need a supply chain leader, then you need the procurement leader. Most often, in fact, all five of these deals go to the Board of Directors, go to the CEO. And in fact, it requires a CEO meeting with me, for sure, if not, more people in my team. So, this is very different. So, I have – we have very high confidence level on the way these deals ramp. There will be a, give or take, a few weeks here, or months here. That’s not the issue. The issue is, is it going to materially change, it doesn’t materially change in our business.
We don’t have any really structural pauses on a nice to have, maybe we should just wait a quarter or two quarters and pause this project. That doesn’t happen just because of the materiality of what we are talking about.
Obviously, our trend, it goes without saying, delivery is important. If the midway through the journey we don’t deliver, our execution really stumbles, then, obviously, the client says, hey, wait. But that’s not something that – on these types of deals, we have not done – we haven’t allowed that to happen, and we wouldn’t allow that to happen, which is one of the good things about our journey, particularly in the last 1.5 years when we announced in June of last year our focus on priority accounts and the fact that these large deals are – majority of them are from the priority accounts. I think it makes a big difference because resource allocation, the right people, the leaders’ attention, all of that goes towards these. So, we don’t allow the bar to drop to the point where the customer delays. Of course, there are bumps on the road, but we don’t expect that to happen.
Right. No, that’s good to discuss and have out there that the buy-in from the client side is at very high level, which in and of itself also helps. My second question is on operating margin cadence. I think Mike, you had mentioned last time operating margins should improve through the course of the year, at least in 1Q, relative to our model. And I think relative to consensus, you are much better than expectations. So, is that a slower cadence upward now? And is it the same factors that you mentioned with regards to gross margins that sort of drive down to operating. It will kind of 16.4% on the low end, and it goes up to like 17%-something, but they average out at 16.8%, is that the idea? I know it’s sort of too long of a question, yes.
Yes. Let me answer the latter one first, absolutely. It will continues to expand throughout the year and average out to that 16.8% that we talked about. Keep in mind, there are some – always some lumpy items that particularly come through. We had some in the first quarter related to severance from that perspective. The interesting thing is our underlying gross margin will improve. However, when you kind of normalize for – we know most of these recent large deal wins, it’s going to be somewhat dilutive against that and get us basically flat for the year. So, if you kind of think about it, we will have the normal SG&A operating leverage against that, which will allow our margin to expand for that, but our reported gross margin should be relatively constant throughout the year. And again, that’s the kind of good gross margin dilution we are looking for. I have always talked about that because it’s all driven by these large deal wins that are dilutive to gross margin when you get them on. So, it’s kind of best case scenario for us and really sets us up really nicely for 2024.
Okay. Thank you, guys.
Thanks Ashwin.
Thank you. [Operator Instructions] Our next question comes from the line of Sam Salvas from Needham & Company.
Hi guys. It’s Sam on for Mayank. Thanks for taking the questions. Just a couple of quick ones for me. Could you guys talk a little bit more about those 17 new logo wins and maybe just break those down into how many of those were digital Ops focused versus in the data tech and AI bucket? And maybe what some of the verticals the majority of those wins were in?
Sam, how are you? Great question. I don’t have the exact numbers, but suffice to say that a majority of the 17 would be generative AI because that’s been the history of the last 3 years or 4 years, and that’s a great way to enter a client. If I look at the five large deal wins, all of them we had entered in the past through data tech AI. So, we already had entered through data tech AI. Those relationships were small at that time. We had classified them as priority accounts because we knew that we will find a way to grow them, and that’s exactly what happened. So, these 17 logos, predominantly data tech AI, which is why the average deal size is $6 million. Yes, it’s upwards of $3 million last year, but it’s still only $6 million TCV. The great thing about these kind of entries is that it sets us up. And if I remember the numbers I shared last quarter in the earnings call, 40% conversion into a meaningful relationship in a 2-year time frameand 55% conversion into a meaningful relationship in our 4-year timeframe. So, continuously winning these 15 to 20 new logos really fits us nicely. These are very distributed across our industry verticals, so I wouldn’t say that there is anything unique about it. The one unique thing, which is no surprise, is the 17 logos as compared to last year does not have as many or probably it doesn’t have any, fast-growth smaller companies that are growing at 100%, 200% a year. And you and I can both name many such enterprises in the technology space, in the high-tech space, etcetera. We probably don’t have many of those in those 17.
Got it. Yes. That’s helpful. Thanks for all that color. And then just a quick follow-up. Tiger, you mentioned a couple kind of AI-centric acquisitions you guys have made in the past. Is that something you guys would consider nowadays, or do you feel like your capabilities there are pretty solid right now?
No, Sam. We are always looking for the right acquisition. We have actually – we probably end up looking at almost every potential acquisition opportunity out there, including ones that our clients talk to us about, companies they are working with, whom they are very like, etcetera, etcetera. I think the – if you look at the AI journey and if you look at a number of examples I gave, I would argue that the challenge is not the technology itself. I think these technologies are going to be more and more available, almost democratized completely, everyone is going to have access to it. So, that’s not the issue. The real issue is not going to be if we acquire more technology. The issue is going to be, can clients really attack that data, so data engineering skills, data cleansing, those types of – data orchestration on the cloud. Technologies that help that, technologies that take loads on to the cloud and orchestrate workflow on the cloud that then have AI solutions that you can bolt on and build into them. Those are the type of acquisitions that I think continue to be on our radar screens. Those are the type of acquisitions that will be very accretive for our capabilities. And those that are acquisitions that we can take to our clients and add value. And of course, in terms of talent, I think our ability to continue to attract talent in the data science, AI, machine learning and engineering space, as well as training our existing large talent population with the Genome data bridge and tech bridge kind of programs that we have, we are very excited about being able to funnel the talent through both those forums.
Got it. Yes, that’s very helpful. Thanks guys.
Thanks Sam.
Thank you. [Operator Instructions] Our next question comes from the line of Bryan Keane from Deutsche Bank.
Hi guys. This is Nate Svensson on for Bryan. Obviously, great to hear about the large deal wins, but I wanted to ask a little bit about the shorter term advisory work that started to come down in 4Q and well through into the beginning of this year. So, I believe on the earnings call last quarter, talked about the potential for that sort of work to pick up in the back half of 2023. So, kind of wanted to square that with the severance costs from this quarter, which I believe I have heard that those are related to sort of headcount in the advisory space. So, I just want to see how well positioned do you think you are for a potential recovery in the shorter-cycle advisory work and sort of any help trying to size that potential to revenue acceleration once that deal work starts to come back and the macro starts to work a little better? Thanks.
Sure. We are able to pivot very quickly if and when that work does come back. In the guidance we put forth, we are not assuming any of that comes back. Still cautiously optimistic for the second half of the year that, that type of “discretionary work” that’s either in pause or on hold will come back. But again, in terms of our ability to ramp up to meet that demand, we will be there and we are in good shape to do that. We are redeploying those savings associated with the severance associated with that to continuously grow and support our other businesses.
Got it. That’s helpful. And then just my follow-up question, I wanted to ask about financial services. So, very strong revenue, up 9% in the quarter, so maybe you can talk about the profile of your financial services clients and the type of works that you are winning and completing there? And I guess, any change in the tenor of client conversations within financial services since we have started to see a bit of the regional banking crisis on hold starting back in March?
Yes. I will start off, and then, Mike, jump in. Our financial services client base is reasonably well diversified. Having said that, it doesn’t go down much at all to the smaller sub-regional type banks. We haven’t had a relationship with any of the banks that have had challenges in the recent past, and we don’t have much of a relationship to be material at all at the next year of smaller regional banks. Most of our relationships are with large money center banks, global, U.S., Canada, Europe, Australia, and very strong over multi-years, which is why we haven’t seen a direct flow-through impact from any of that in our business. What we have seen are two things. One, the discretionary slowdown in advisory work, etcetera, one of the places where that happened was in some of our banking clients, where the prioritization that the banks are doing is even sharper than ever before, including some of them actually pivoting the conversation to having to deal with higher volume of deposits. So, some of the work that we started getting recently were some of the larger banks getting more deposit flow, for obvious reasons, and then the fact that they have now got to deal with that larger volume and then orchestrating the capability quickly to be able to do that. And then the other thing that we are clearly seeing is the whole large deal environment in the banking space driven by, again, a desire to actually further standardize and simplify in order to then bring in technologies in areas where the bank have said, you guys focus on that, but I am going to focus on A, B and C, because those are also important to do, which is something that is important to call out because you – no enterprise can actually leverage these technologies as quickly as they want to across their footprint. So, the reason why they need partners is because they say they will do X portion of the work on something to do with customer marketing and dealing with their customer, segmentation, new product. And then we end up doing a bunch of other things, including customer service, maybe, maybe collections work, maybe receivables management for them, maybe fraud. And of course, risk is the big service line that has been on a growth path for us in the last 3 years, and that continues – that momentum continues.
Appreciate the color.
Thank you.
[Operator Instructions] Our next question comes from the line of Bryan Bergin from TD Cowen.
Good morning guys. Good afternoon. Thank you. I wanted to follow-up on the topic here with Gen AI. And just curious, Tiger, how soon do you reasonably believe a large mix of clients will actually have Gen AI models in production, just based on your prior experience with other ways of tech like RPA? Like how long does all this take? And we are all trying to cut through the hype, but trying to understand at what rate, let’s say, a third or more of your clients can actually get there with these models materially impacting their operations?
Yes. So, Bryan, thank you and great question. I called out help desk, customer service and research work as three examples where we are seeing, and it’s obvious, because we actually identified those and started talking to clients very, very early, and we are seeing conversations begin. Because those are areas where you do not necessarily, in all those cases, have to get into the deep data plumbing. You have got to get into some element, but not too much, in many cases, and you can start the journey, particularly in unregulated industries. The moment you get in regulated industries, customer service, you have to walk the path very carefully. You have got to create pilots. You have got to test those. InfoSec, Cyber, I mean, there is a whole lot of things that have to be done. So, the first thing is data. Therefore, to answer your question, we would expect many of the initial pilots to be broadly in, for example, those three areas. And I am talking about specifically generative AI. Remember, we are already using AI in many, many other places where we actually trawl through data and then build models that become prediction for the next best action. But those are database and machine learning-based AI technologies that actually we have deployed in many, many solutions. The second way I would answer the question is there is a Phase 2, where it takes much longer because you actually have to work with the client. And if you think about accounts payables, we do a lot of accounts payable work for a lot of our clients, supplier payments of bills. If you think about the way that gets orchestrated in a client environment, it happens in 100 countries, it happens across the business, it happens in the factories, it happens across 1,000 people, and there are hundreds of policies and often hundreds of technologies on which it is done. And by the way, there are hundreds of definitions of each of the data. You have got to find a way to bring that together to rationalize it, to simplify it, to standardize it so that we can then say, now, I have enough of data to start building models that can feed both machine learning engines as well as generative AI engines. We see that journey. We have done that in payables. We have done that in receivables. We have done that in claims. We have done that in underwriting and insurance. We have done that in loan processing, the example I gave. They tend to be multi-year journeys, because one of the challenges in AI is that when you flip the switch of the model, the model better work because if it doesn’t, then you really could create chaos. So, everyone who is making a decision is very cautious about flipping the switch. So, they do it in country one, in business one, in product cycle one, then two, then three. So, it’s a journey. And we have seen that play out in technology-after-technology, exactly the way it plays out. We have seen that in machine learning. We don’t see it any differently. There is no question that this is a breakthrough in language. So, where language is used is where we think is the biggest opportunity and where coding is used. So, if you have lots of people who are sitting and writing code, I think that is a real opportunity to actually disrupt that more quickly because you are not putting it into production, you are actually sitting and doing coding. So, that’s a real opportunity. Help desk, as I have said, is an opportunity. Research is an opportunity.
Okay. Thank you for that detail. And my follow-up is on service line composition. So, I know in the past, you have offered certain business mix disposal, I believe with IA that you called that emerging services, roughly a quarter of the revenue. Can you provide any further quantification of mix across areas like F&A or customer service or content mod? And the other question is with certain types of services that Gen AI is more applicable to, we have been getting that question about the rough mix across vendors that would fall in these areas in language and content-related buckets versus other areas?
Yes. Emerging services, we called out last year at 25%, and we said that since that’s growing much faster than company rates, that percentage will keep increasing. And that is true because, as we said, emerging services grew up 9% in the first quarter, well above company average. So, we expect that 25% to have gone up and will continue to go up. The broad distribution of our services, we don’t have that cut out there. What I would say is, if I look at, let’s say, customer service and help desk on the phone, which we think is actually one of the biggest opportunities where you can have a co-pilot, you can have generative AI really help in those client – customer conversations. That’s less than 10% of our business. And which is why I said there is a real opportunity to actually create innovative solutions, working with our clients, and penetrate that space where we don’t have much penetration, which is a real opportunity. And if you look at finance and accounting, which is our largest and no surprise, that’s how we started. Those are long journeys given they are, as I said, hundreds of systems, hundreds of – thousands of locations, 100 countries. And often, data is the bottleneck. You have to get a hold of data.
Okay. Appreciate the color. Thanks guys.
Thank you, Bryan.
Thank you. [Operator Instructions] Our next question comes from the line of Surinder Thind from Jefferies LLC.
Thank you. So Mike, I would like to start with a question of where – since it started at the first question, which is – it’s around guidance here. So, when you were putting together your guide, the forecast here, can you talk about all the puts and takes in light of the conversations that the 150-plus that Tiger has had, the volatility that you have seen in the marketplace, where would there be risk in the guide at this point? In – is most of the work that you need to generate your guide or the revenues been already won at this point, given the long deal ramps and so forth, or how much more work do you need to actually win to get to hit your guide numbers?
Yes. So, it’s a good point. We talked about at the beginning of the quarter that we have to win roughly 30% or about $1.4 billion of new business, right, in any given period of time on a rolling basis. That’s still the case, right, from our perspective. We had a very good start to the year from a first quarter perspective. But what we do is the same process we do when we do our guidance for the year. We risk weight each one of the opportunities that we have, and we build it up from the bottoms up. So listen, it’s probably a little bit less risk than we had going into the year, particularly with the volume of bookings that we had and large deal wins. But we feel really good about it. And then we all feel really good about the extrapolation of what we – the pipeline and the bookings we have in terms of where they are going to carry us through 2024. So, not 100% [Technical Difficulty] Surinder, you may have two devices open maybe.
I am just on one single line here, so I am not sure where the echo is coming from.
And now it’s – now it’s gone. Thanks. Sorry about that.
Okay. I guess fair enough. And then I guess a related question in terms of just the numbers here. When we look across the industry, obviously, there has been some downward pressure on the usage of FTE resources. Can you talk about your exposure there? And in terms of the risk there, is it, there is further deterioration in employment within the U.S. or globally? And how you think about that risk? It just seems like you guys haven’t been impacted the way that maybe some others have that have been forced to kind of reduce their FTE usage at existing clients.
If don’t think what – so if you could just repeat the specific – impacted by what?
Employment, right? In terms of like – for example, as you provide customer support, right, at any given entity, if there was layoffs internally and they have a 20% less workforce, you would expect them to ask for fewer support resources, right? So, a reduction in the FTE usage from you guys. Any color there? It doesn’t seem like you guys have had a lot of sensitivity there, whereas maybe some of the other competitors...
No, we don’t. We think – actually, it’s a great question. I didn’t understand the question. We both now understood the question. It’s a fantastic question because it further – we haven’t had an impact. Because if you think about the kind of work we do, we do work where either you have to do it, there is no option. It doesn’t matter how big your company is. I am thinking about closing the books. It doesn’t matter as you grow, you still have to close the books, or you think about customer transactions, you think about supplier transactions, unless the business itself shrinks, which can happen, in which case, those types of transactions will shrink. And we have seen that before in the global financial crisis. We saw that with some clients in the pandemic, whether it was the hospitality client or an airline client. But our – we have – we don’t have – I guess we don’t have too many services at all that internally serve employees. And as the employee base reduces, our service reduces. We haven’t seen that. The one example that Mike called out, and I called out as well, is when the customer decides to actually reduce the scope of work itself, saying this is work we do for our customers, we actually don’t want to do this work for our customers anymore given the environment. And we saw the impact of that in one of the technology companies. Other than that, no, we haven’t seen the impact of that because I wouldn’t expect to see that in our business.
Got it. Thank you. That’s very helpful. That’s it for me.
Thank you, Surinder.
Thank you. I would now like to turn the conference back over to Roger Sachs for closing remarks.
Thanks everybody for joining us on our call today, and we look forward to speaking to you again next quarter.
Thank you. This concludes today’s conference call. Thank you for participating. You may now disconnect.