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. 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 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 generative 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-bridged 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 Promoter 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 variances, 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 at a more meaningful pace than we have in the past throughout 2022 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. 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?

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 five large deals. Good color provided there. In terms of the ramp, 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 impact on the P&L in the short-term as well as the risk that maybe comes with it. It sounds like great overall.

No, thank you, Tien-Tsin. And yes, speaking to 150 clients is a lot of engagement, but the environment is such that it’s important to get full insight and engage because everyone is trying to figure out answers. On the five deals, the deals are, in terms of structure, very similar to deals we’ve done before. A couple of them, specifically, do have material rebadge components. These are companies that have made strategic moves where they say that they need a partner to help them, even though earlier they thought they did not. We’ve done 25-plus such rebadge deals in our history, and they have gone really well. We taught ourselves out of GE many years ago, and that DNA remains. So we don’t see extraordinary risks associated with that. Obviously, these are complex deals and the ramps are complex, but we know this business. We know the way to structure these. We’ve done this before, so we expect a regular cadence to build up. None of these deals materially contributed to anything in the first quarter. We will start to ramp sometime in the second quarter and more into the second half of the year. Some of them will have tail ramps that continue into the first half of next year, given that these are large and complex. So there is nothing more to call out than that. We’re very excited about how quarter one went on that.

Okay, great. And on generative AI, how would you compare this opportunity relative to past waves, such as RPA, in terms of scope, opportunity and impact with enterprises so far?

It’s very exciting. I always believed RPA was never the ultimate answer; it was at the lower level of automation. AI sits at the top of the food chain. This is AI and the breakthrough in language has been significant. We started working on solutions with our clients in the fourth quarter because GPT-3 was available and our teams have been experimenting. It is different from RPA but there are similarities. The opportunity is huge and the value creation opportunity is real, but enterprises face challenges: where to start, how to prioritize, and what value can be obtained. That determines where to start. Secondly, whichever area you choose, you must have your data lined up. Typical large enterprises have fragmented data and processes; they must standardize and clean them to leverage AI. We saw this when RPA and low-code workflows on the cloud became mainstream. We embraced those technologies and integrated 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, and more than 250 clients on our Cora platform which handles more than 20 million transactions a month. We’re seeing co-innovation journeys in AI to consolidate processes and data, and this is a big driver of our inflows and bookings. The need to fix legacy technology, processes and data is a tailwind for companies like us. We believe generative AI and broader AI will expand TAM and create client value similarly to RPA.

Thank you for sharing that, Tiger.

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. Your thesis here on generative AI is essentially that, while it makes each of your employees more productive, it will create efficiencies that could reduce headcount but you’ll capture incremental work and still lead to total growth, right? That’s been true for your approach to AI for a while. I wanted to drill in: as generative AI comes more to the forefront and quality improves, is there a risk that pricing gets further eroded in a way that may be different than other parts of AI? Do you see the pressures as similar?

Great question. Two parts to the answer. One, we’ve seen this pattern not just with RPA but with cloud adoption: productivity gains can reduce labor needs but typically expand the overall addressable market because we add new services and take share. Clients where we’ve deployed bots and AI solutions often grow with us. Second, our commercial models have evolved: transaction-based and outcome-based models were low previously but are growing. Alternative commercial models are 13% of revenue now and we’ve targeted 20% by 2026; current bookings showed 28% TCV in quarter 1 had non-FTE pricing. Those models align incentives, create win-win outcomes, and can be margin-accretive over time. So while pricing pressure can exist, we believe the shift to value-based and transaction pricing, combined with our ability to expand TAM and co-create value, will offset those pressures and be accretive to margins in the long term.

Right. Just to clarify, might your project work as a percent of total revenue go up because of AI versus traditional time and materials?

I don’t think of it as project work per se. Most examples include AI solutions embedded into services we already deliver. Sometimes clients ask us to come in and do new work using AI technologies. We see similar patterns to RPA: some solutions are deployed with clients, others run within our operations. The ratio shouldn’t change dramatically; AI’s power is best realized when embedded into large deals and ongoing services, and that is how it will scale for us.

Got it. Thanks, Tiger.

Our next question is from David Koning of Baird.

Yes. Thanks so much. Nice bookings this quarter.

Thank you, David.

Can we go back to growth tempo? It sounds like you expect to exit this year back at a normal 10% plus run rate, but there is a four-quarter period of more mid-single digits. I know that’s driven by a little lower in the Data-Tech-AI that started in Q4. Can you bridge between the normal 10% plus growth and why there is this mid-single digit period? Is it just one or two clients? I want to ensure nothing disruptive is happening.

I’ll start and then Mike can add more. We set expectations for some difference between first and second half because the comparisons to prior year vary and we expected discretionary advisory spend to be down, which has rolled through. Mike called out a reduction in operational scope for one of the larger high-tech clients which affected the first quarter. Some large deals were expected to sign at the end of last year but pushed into the first quarter; they will ramp into the second half. Overall, these are timing and scope factors we planned for. Mike?

Yes. The reduction in scope of that client was included in our outlook. When we look at the business on a 12- to 24-month rolling basis, plus the large deal wins, we feel confident about achieving a blended near-10% rate over the coming years. Some years will be better, some slightly lower, but the trajectory remains intact.

Great. And the divestiture you mentioned in prepared remarks — can you say how much revenue impact it has?

We’re not disclosing the exact amount, but it makes up a substantial portion of the delta between our normal trajectory and where we’re forecasting this year. Timing and large deal ramps also factor in, so in the end it washes through.

Got it. Thank you.

Your next question comes from the line of Maggie Nolan from William Blair.

Thanks. You’ve had traction with large deals, but you also mentioned elongated large deal decision cycles. What are your expectations for the remainder of the year for deal sizes?

We haven’t seen a downward trend in large deal cycles; they have been elevated for a couple of quarters and remain elevated but aren’t increasing further. I wouldn’t expect cycle times to get longer; as things stabilize, cycle times should come down. We’ve assumed elevated cycle times continue through the year and made no other assumptions.

On outcome-based contracts, you set a target for 2026. What does it take internally to drive toward that target, and will those models be margin-accretive?

Historically, when we enter contracts with outcome-based elements, they create win-win outcomes — clients benefit and we benefit. Talent working on these sees real value creation, which is motivating. Getting clients to agree to these models has taken time, but momentum is building, including driven by AI and generative AI where outsized value creation is sought. These arrangements require clients and us to be true partners, combining client data and our domain and technology expertise. We’ve seen success in insurance claims, fraud, sourcing and procurement, order-to-cash, financial planning, and customer service. We see real opportunity to expand outcome-based models and believe they can be margin-accretive over time.

Thank you, Tiger.

Our next question comes from the line of Ashwin Shirvaikar from Citi.

Thank you. Let me start with bookings. Good bookings. My question is about revenue conversion and confidence that the timing will hold. In the industry some companies report large bookings that don’t ramp at historical pace. How confident are you these bookings will convert as expected, and what type of work is flowing more smoothly versus what gets pushed out functionally?

We haven’t seen the issue of large bookings not converting into revenue materially in our business. Our deals are different; many are service-oriented and require global coordination and Board or CEO-level approvals, so they are harder to stop once agreed. Some timing variance of weeks or months occurs, but we have high confidence in ramp patterns. Delivery is crucial; if execution stumbles, delays can happen, but with our focus on priority accounts and resource allocation, we work to prevent execution slips.

We don’t have structural pauses on nice-to-have projects that could be delayed. Large transformational deals are material and require commitment, so pauses are not common. Our priority account framework helps ensure necessary attention and resources.

Thanks. Second question on operating margin cadence. Mike, you mentioned margins should improve through the year and you’re above consensus in 1Q. Is the cadence slower now? Are the gross margin factors you mentioned the main drivers? Is 16.4% in 1Q moving to around 16.8% for the year as you outlined?

Yes. Margins will continue to expand through the year and average to roughly 16.8% as outlined. There are lumpy items, such as severance in 1Q. Underlying gross margin should improve about 30 basis points, but new large deals with higher onshore delivery will dilute gross margin in the near term, keeping reported gross margin relatively flat for the year. SG&A leverage will help expand adjusted operating margin despite that gross margin effect. This dynamic sets us up favorably for 2024.

Okay. Thank you, guys.

Thanks Ashwin.

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. Could you talk more about the 17 new logo wins and break them down into how many of those were Digital Ops focused versus in the Data-Tech-AI bucket? Also, what verticals were the majority of those wins in?

Good question. I don’t have exact numbers on hand, but a majority of the 17 would be Data-Tech-AI engagements because that is a common entry point for new clients. For the five large deals, we had earlier entries through Data-Tech-AI and they grew over time. The 17 logos averaged about $6 million TCV, compared to $3 million last year. Winning these entries sets us up for significant conversions: historically about 40% convert into meaningful relationships within two years and 55% within four years. The wins are distributed across our verticals; they do not concentrate in any single vertical. Compared to last year, the new logos do not include many fast-growth smaller companies in the tech space.

Thanks. One follow-up: you mentioned past AI-related acquisitions. Would you consider acquisitions now or do you feel capabilities are solid?

We continually evaluate acquisition opportunities. However, the bottleneck today is less about acquiring core AI technology, which is becoming more commoditized, and more about data orchestration, cloud migration, workflow orchestration, and solutions that enable clients to standardize and clean data so AI can be applied effectively. Acquisitions that strengthen those capabilities and are accretive to our clients would be attractive. Talent acquisition and reskilling programs also remain important.

Got it. Thanks guys.

Our next question comes from the line of Bryan Keane from Deutsche Bank.

Hi guys. This is Nate Svensson on for Bryan. Great to hear about the large deal wins. How soon do you reasonably believe a large mix of clients will have generative AI models in production, based on your experience with prior technology waves like RPA? At what rate can a third or more of your clients actually get there with models materially impacting operations?

Great question. Initial pilots for generative AI are concentrated in help desks, customer service and research work, where data plumbing is lighter and where progress can be faster, especially in unregulated industries. For regulated industries and core finance or accounting processes, it’s a longer multi-year journey because you must standardize and consolidate data across many countries and systems before models can be put into production. We have experience doing these multi-year journeys for payables, receivables, claims and underwriting. Adoption will be phased: Phase 1 — pilots in areas like help desk; Phase 2 — deeper, multi-country deployments across core finance and operations. Language breakthroughs accelerate opportunities in areas that rely on language or coding. Overall, expect pilots and early production in certain functions relatively soon, with broader, material operational impact on a multi-year horizon as data platforming and standardization efforts are completed.

Thanks for that detail. And a follow-up: can you quantify service line composition? For example, how much of your revenue is in customer service or content-related buckets where generative AI is most applicable versus other areas?

We called out emerging services at 25% last year and noted that because they grow faster than the company average, that percentage should increase; emerging services grew 9% in the first quarter. Customer service and phone-based help desk is less than 10% of our business, which is why we see a significant opportunity to penetrate that market. Finance and accounting remains our largest area, and those are longer journeys because of the scale, geography and data complexity. We expect composition to continue to evolve as emerging and AI-enabled services grow.

Appreciate the color.

Our next question comes from the line of Surinder Thind from Jefferies LLC.

Good morning guys. Good afternoon. Thank you. I wanted to follow up on generative AI. How soon do you believe a large mix of clients will have generative AI models in production and materially impacting operations?

I addressed this earlier: pilots and early production in help desks, customer service and research can happen relatively quickly, especially in unregulated industries. For core finance and accounting, multi-year journeys are typical because of data consolidation and the need for careful testing before flipping models into production. The language breakthrough makes adoption faster in areas dependent on language or coding. Overall, expect meaningful production use cases in some functions soon and broader operational impact over multiple years.

Okay. Thank you for that detail. And a follow-up: regarding your business mix and exposure to language- and content-related buckets, how does that map to vendors in the space?

We already discussed that customer service is underpenetrated and represents an opportunity. Emerging services continue to grow above company rates. We focus on data orchestration and domain expertise and partner with technology providers to build scalable solutions. The vendors in language and content are increasingly commoditized, and our differentiation is in domain, data, and the ability to operationalize AI at scale for clients.

Our next question comes from Surinder Thind from Jefferies LLC. We already discussed that customer service is underpenetrated and represents an opportunity. Emerging services continue to grow above company rates. We focus on data orchestration and domain expertise and partner with technology providers to build scalable solutions. Vendors in language and content are increasingly commoditized, and our differentiation is in domain, data, and the ability to operationalize AI at scale for clients.

Mike, when putting together your guide, can you talk about the puts and takes given the 150-plus client conversations Tiger has had and marketplace volatility? Where are risks in the guide? How much of the work needed to hit your guide has already been won, given long deal ramps?

Good question. We typically need to win roughly 30% — about $1.4 billion — of new business on a rolling basis. That remains the case. We had a strong start to the year, and we risk-weight opportunities in our bottoms-up guidance process. With the bookings and pipeline we have, we feel more comfortable than earlier in the year, but the process and risk-weighting remains disciplined.

Thanks. Related: there’s downward pressure on FTE usage in the industry. Can you talk about your exposure and risk if employment weakens globally? Some competitors have seen more sensitivity.

Great question. We haven’t seen major impact because much of our work is mission-critical operational work that must continue. Processes like closing books, customer transactions, supplier transactions — unless the underlying business shrinks — these activities continue. We have had examples historically where certain industries saw a reduction in activity, such as hospitality during COVID, but by and large our services support core transactions and ongoing operations. One example of reduced scope was the high-tech client Mike mentioned earlier. Otherwise, we haven’t seen material sensitivity tied to reductions in client headcounts.

Got it. Thank you. That’s very helpful.

Thank you, Surinder.

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.