This is Glob AI, not a chatbot, not a generic AI tool. AI native tech services. We're introducing a new delivery model through AI pods. It starts with a business challenge, a need, and Opportunity. Every workflow is executed by agents, while domain experts and clients supervise it in real time, resulting in enterprise-grade, governed AI services charged by outputs or consumption, not by hours or seats. Forget long procurement cycles. Forget long ramp-up processes. This is how tech services become services as software. This isn't division. It's already live, repeatable, and scaling.

Good afternoon and welcome to Globant's second quarter 2026 Earnings Conference Call. I am Arturo Langa, Investor Relations Officer at Globant. All participants on this call will be in listen-only mode. After today's presentation, there will be an opportunity to ask questions. Please note this event is being recorded and streamed live on YouTube. By now, you should have received a copy of the earnings release. If you have not, a copy is available on our website, investors.globant.com. We will begin with remarks by our Chief Executive Officer, Martín Migoya, our Chief Technology Officer, Diego Tartara, and our Chief Financial Officer, Juan Urthiague, followed by a Q&A. where they will be joined by our Chief Revenue Officer, Fernando Matzkin. Before we begin, I would like to remind you that some of the comments on our call today may be deemed forward-looking statements. This includes our business and financial outlook and the answers to some of your questions. Such statements are subject to the risks and uncertainties as described in the company's earnings release and other filings with the SEC. Please note that we follow IFRS accounting rules in our financial statements. During our call today, we will report non-IFRS or adjusted measures, which is how we track performance internally and the easiest way to compare Globant to our peers in the industry. You will find a reconciliation of IFRS and non-IFRS measures at the end of the press release we published on our investor relations website, announcing this quarter's results. I will now turn the call over to Martin Migoya.

Good afternoon, everyone, and thank you for joining us. Today I want to talk about a change we are leading, a new way of creating value for our customers, delivering our work, a way of pricing it, which is already starting to compound. For more than 20 years, we have engineered the digital reinvention of the world's leading organizations, building the software, products, and platforms that run their businesses, delivered by dedicated high-performing teams and priced through on fixed-scope engagements or time and materials. That work remains the backbone of Globant. One year ago, I introduced you to AI pods, a new AI native revenue stream built on that foundation, but priced on the actual output and value we deliver or on consumption, rather than on the hours we bill. As AI pods deliver more work at higher margin for a similar price, our top line can understate the progress underneath it. As this grows, it will be relevant to assess annual recurring revenue, revenue per head, AI pod margins, and client penetration alongside the total revenue line. Before I go further, let me be precise about two names you will hear all call. Glob AI is the platform we opened to the market last week. AI pods are the service units that live on it, run by AI agent workflows and supervised by our experts. The revenue they create I will call Globe AI ARR. Hold those three together. The platform, the pods, and the number. This quarter's revenues grew roughly 60% to $52.8 million. And we estimate that Globe AI's ARR will surpass $110 million by year-end. Let me walk you through it in that order. The model, the number that measures it, where the growth is coming from, how to read our reported revenue while both models run side by side, and where it already shows results. The technology services industry as a whole is growing at roughly flat rates right now. But flat is an average, and averages hide the real story. Inside that flat industry, we have found a growth runway, AI-native services. And it is growing because it expresses what enterprises want from AI better than a traditional, hours-based approach does. Clients can tell the difference. and a growing number are moving budgets accordingly and more are choosing to work with us with this new model. As enterprises abstract away layer after layer of complexity, infrastructure, platform, software, some are now beginning to abstract away business services themselves. We think of this as service as software. Just like how the cloud transformed software infrastructure and provided predictable and recurring revenue, Glob AI does for professional services. You turn on the outcome and pay for what you consume, with Globant's experts built in. It opens budgets we have not had access to before. Annual spending of the global professional services industry is estimated at more than $6 trillion, roughly four times the size of the IT services market that Globant has been evaluated in. We are taking this deliberate decision to respect our current market, Thank you very much. Thank you for watching! Glob AI ARR reached $52.8 million as of June, up from $32.8 million in March, roughly 60% growth in a single quarter. We have seen plus 30% more productivity than with a typical engineer plus AI approach. Pipeline stands at $436.8 million, up from $352 million in Q1. Adoption has reached 45% of our top 20 accounts. Gross margins on this model run close to 10 percentage points above our traditional delivery. We now expect to exit 2026 at no less than $110 million in Glob AI ARR. That is the yardstick. AI native revenue becoming core to how we believe the market should value Globant, measured quarter after quarter. These changes have affected Globant as a whole as well. Globant's revenue per head reached $95,800 on a run rate basis, up 9.7% year over year. We are delivering more value with the same talent and capturing it. Glob AIARR captures three of the biggest waves of demand in our industry. They are core modernization, experience debt, and agentic process transformation. We have shared them with you on previous earnings calls. What changed is that all three now convert increasingly through AI pods. Let me go through each one with you. 1. Core Modernization There is a technical debt backlog between $1.5 and $2 trillion across the world's 2,000 largest public companies. It used to mean a large team billing hours over months. We can now deliver it as an outcome in less time. That is why clients are moving to the new model here first. Two, experience debt. Every customer facing surface that has to be rebuilt for an AI first world. Our Versal and Claude powered AI pods are turning multi-month rebuilds into same week releases. 3. Agentic Process Transformation The largest opportunity, redesigning how a business runs around agents. The value is in the transformed process, not the hours, so this is where outcome pricing fits best. Now, a word on our current position and how to interpret the top line while this shift is underway. For Q2, revenue was $614.4 million, within our guided range, up 1.2% sequentially. and back to slight year-over-year growth. AI pod revenue makes up roughly 2% of our total revenue today, and we expect it to reach 4% by the end of the year. We keep seeing the pocket of growth I mentioned earlier, demand for AI pods. We are choosing to accelerate these migrations, even if it means a short-term impact on revenue, because over time, it creates more value for the client with predictable, outcome-based consumption and more value for Globant with higher margins and access to more sophisticated projects. This quarter, 96% of our revenue came from repeat customers, and we grew our top 20 and top 50 clients by 6.6% and 6.9% year over year, respectively. This is concentrated where Glob AI and AI pod penetration is highest. Our data and AI studio is now our second largest studio by revenue, close to 11% of sales and growing close to 35% year over year. Our AI studios are increasingly selling AI native services alongside traditional staff augmentation and providing their depth. Since that top 50 growth is a sign they understand these clients' industries well. Our core business is acting as the distribution engine that carries Globe AI. and the AI pods that run on it into large enterprises on relationships built over two decades. Pipeline and bookings are at a healthy level. Its composition is shifting toward AI, data, cloud and integration work. Having said all this, we are operating in a tougher environment this quarter. Geopolitical pressure in our new markets, volatile oil prices weighing on travel, and longer decision cycles in North America. Juan will take you through a revised outlook for the full year. Last week we launched Glob AI, and with that, we are opening this same model to any enterprise through a single, self-service platform, so AI-native services, priced on output and value or on consumption, become available to more of the market, not just our largest accounts. Here is what that looks like in practice. Glob AI is a single destination where an enterprise can find, deploy, and start consuming an AI pod without a months-long discovery process and a long ramp-up time. A client can log in, explain their technological opportunity and plan language, and the platform draws on Globant's entire network of technological solutions, partnerships, recommends AI pods, and enables clients to start building the same day. It bridges the gap between mental throughput and making sound business decisions. Clients keep sovereignty over which models they use and where they run. These pods are built in cooperation with the companies defining this technology. Specific AI pods are engineered with name partners, secure code review with Anthropic, digital twin engineering on Nvidia Omniverse, prototype to product with Vercel, and enterprise integration with Salesforce and MuleSoft. The platform runs across the broader model ecosystem as well. Anthropic, OpenAI, Google, Azure, AWS, NVIDIA, Meta, among others. I am glad to announce that Saurabh Narang is joining us as Glob AI's CEO. Saurabh is an accomplished AI and technology executive with more than 23 years of experience. He joins us from ServiceNow, where he led the commercialization of its AI business. and previously held senior AI leadership roles at AWS, where he helped build and scale AI platforms, including Amazon SageMaker and Amazon Bedrock. Earlier in his career, he built KPMG's AI and machine learning practice, None of what I have discussed so far works without the right partners. In June, we announced a multi-year alliance with Anthropic, becoming a preferred services partner in the Claude Partner Network. Since signing, we have moved quickly. Several Claude-powered AI pods are already in production, and were showcased at our Globant Tech Summit in July. We are training thousands of Globers on anthropic tools and we have an active joint pipeline with several large financial institutions, airlines and e-commerce companies. One year after our initial partnership, OpenAI has named Globant a selected partner in its new partner network. And with Vercel, clients can ship AI-built applications natively in a single click. Turning multi-month projects into same-week deliveries. Together, we launched Vercel-powered AI pods, agentic units that design, develop, and modernize enterprise digital products on Next.js. FIFA is using AI pods powered by Glob AI to scale its digital ecosystem into a continuous, personalized experience for football fans worldwide. Its key platforms recognize fan preferences across competitions and, powered by AI pods, use real-time data to generate new experiences year-round. This quarter marked three years since the foundation of our partnership with British Airways, delivering a platform built for speed and continuous innovation. In June, British Airways reached an important milestone on this transformation journey with the launch of their new mobile app, following extensive testing to make every stage of the customer journey simpler and more intuitive, acting as a real-time travel companion. Positive customer feedback has highlighted the improved user experience, Particularly, the live flight notifications feature. And this is just the start. We're extending the partnership with new features powered by our AI pods model, accelerating what we can deliver next. In the Gulf region, we are working with one of its largest financial institutions by building its first agentic bank. Powered by our AI pods model, intelligent agents will act across acquisition, onboarding, servicing, and risk, Reshaping How the Bank Operates and How Customers Experience It Got delivered a solid Q2 2026, culminating in another standout performance at the Cannes Lions International Festival of Creativity in June. The network earned 22 Lions. including a third consecutive Grand Prix for long-standing client Mercado Libre, the first agency-client partnership to achieve this milestone at the festival. GUT also launched new work for Google Chrome and Ray-Ban Meta, created the world's first clay bar for Stella Artois at Roland Garros during the French Open, and introduced Rimowa's For a Lifetime of Lives platform, celebrating craftsmanship through stories of longevity and evolution. We are building a meaningfully different services business, deliberately, with a growing base of revenue underneath it, and with a number I have asked you to hold us to every quarter. This next chapter also means disciplined choices today, including decisions on our cost base to fund the transition and protect our margins. I do not take those lightly, and I am grateful to our teams for the resolve they are showing. Thank you to our clients, our partners, and our Globers around the world, building this alongside us every day. With that, Diego will show you the machine underneath. Thank you.

Thank you, Martin, and hello, everyone. Martin just laid out our strategic vision for Globe AI and how it fundamentally transforms the way clients acquire our services. My focus today is on the underlying engine, the technology architecture, the operational mechanics, and the first mover advantage that makes this delivery model perform at scale. The legacy professional services model trades human hours or custom solutions, forcing engineers to solve the same foundational problems repeatedly. Globe AI breaks that cycle by operating as an asset-based engine. Within the platform, we have codified over two decades of enterprise engineering and industry domain knowledge into curated, battle-tested playbooks. These are deterministic, documented, agentic workflows designed for production-grade reliability. Because these agentic assets are modular and validated, we achieve extraordinary cross-industry compounding value. An IT root cause analysis workflow built for an airline client, for example, can be replatformed into a pharma supply chain or media distribution pipeline in a matter of weeks rather than months. AI Pods serve as our direct vehicle for monetizing this compounding IP, taking clients from a natural language challenge to production-ready deployment without the traditional friction. As we set out in introducing Glob AI, raw LLM prompting produces significant token waste, hallucinated logic, and expensive retry loops that includes additional time for human supervision. Glob AI solves this through structured, deterministic process optimization. Before an AI agent executes work, our platform automates context assembly, architecture mapping, and data preparation, enforcing automated quality gates at every step. By optimizing the orchestration layer, we ensure that every token consumed yields verifiable production-ready output. At the same time, enterprise adoption hinges on control. Globe AI's architecture routes intelligently across more than 140 LLMs, providing complete model independence so clients are never locked into a single provider. Crucially, every transaction is locked within the client's dedicated token vault. This guarantees absolute token sovereignty. No client data is ever exposed or used to train third-party models, allowing organizations to compound their own institutional intelligence safely over time. We are seeing the impact of this platform model directly in our operational performance. By integrating specialized AI agents into continuous delivery workflows supervised by our experts, we are restructuring the software engineering lifecycle. This leverage enables us to decouple enhancements in output, velocity, and delivery from headcount growth. Furthermore, this enables us to layer on non-linear and recurring revenue to the mix with structurally better unit economics that will over time transform the business. This is the structural signature of a business moving up the value chain. Now, I would rather show than tell. Let me take you on a short Globe AI tour.

It starts simply. Once you have selected the plan and the AI pods from the catalog or through chat with the Globe AI agent, you would be able to access the projects module. From that moment on, you are ready to begin. Globe AI works at two levels, your organization and your projects. Your organization is the foundation where you configure the tools and settings that apply to all your projects. Your projects are where the work happens, each with its own goals and one or more AI pods running simultaneously at different prices, which always include not only the tokens consumed, but also the human supervision required for the project. Let's start at the organization level. The first thing your organization needs is context, and you can give it directly from the tools your organization already uses. Connect your GitHub to read, write, and open pull requests directly on your repositories. Google Workspace to pull signals from Gmail, Calendar, and Drive. Atlassian to sync with your JIRA tickets and Confluence pages. You can also provide context through a document or a description. Once you're inside your project, everything is in one place. At the top, you see your project at a glance, resuming the totals of your project, how many AI pods are active, your goals, your token consumption, your current spend with your limit, and the start button that sets everything in motion. Right below, your customized dashboard also gives you four key metrics. Selecting the period, you can see the spend, tokens used, tasks total, and artifacts produced. Beneath that, a quick summary, your active goals, the units of work in progress, always visible at a glance. And finally, your AI pod fleet, the specialized teams running your delivery. On the right is your communication channel, the Globe AI agent. Just describe what you need in plain language, or start with one of the suggestions below. You can also mention your forward-deployed engineer, the Globin expert assigned to your project, and your direct line to the human overseeing every step. Whenever you need to communicate, you're one message away from your expert. Goals are the units of work your AI pods will execute, and you create them by simply telling the agent what you want to achieve. You can connect a repository and let the agent analyze your codebase directly. You can describe your needs in plain language, or you can attach a document and let the agent extract the requirements from there. Either way, the agent proposes the goals, you review them, you approve them, and if something needs to change, you tell it directly or bring in your expert, always available in the chat with full context on your project. When you're ready, you say start. That single action locks in your project and hands it to the forward-deployed engineer, who supervises every step of what comes next. Artifacts is your shared workspace for project files. Here, you'll find every document, spec, ABR, or implementation summary produced by your AI pods, ready to preview with a single click. You and your FDE can all upload files here, keeping everything in one place. Some artifacts will also be delivered directly to your GitHub repository as commits or pull requests. Clicking on your name gives you access to your notification settings. You choose how GlobeAI reaches you – email, WhatsApp, or Telegram – and you decide which events trigger each channel, approvals waiting on you, actions required, new deliverables, or run updates. Clicking on your organization gives you a complete control panel with four tabs. Overview, with all the general information. Members, where you manage your team. Billing, your full financial picture. And last, usage, a breakdown of consumption for the current cycle.

The capability behind Glob AI is not built on theory. It reflects what we have been proving on the ground. In our previous calls, we shared how early AI pods deployments drove milestone efficiency gains, whether accelerating drug discovery research at Pharmamar 15-fold, compressing supply chain contract cycles by 40% at YPF, or reducing legacy migration timelines from 14 months down to two. What makes Globe AI so significant today is that those custom high-impact successes are no longer bespoke projects. We have productized those learnings into our standard catalog. Backed by our deep co-engineering alliances with hyperscalers and model providers, Globe AI turns those proven enterprise outcomes into an on-demand, repeatable capability accessible to every client from day one. We have built the underlying platform, secured the governance framework, and proven the economics at scale. Everything I just described is what compounds behind one number. Globe AI ARR. We look forward to driving this next chapter together. Thank you very much.

Hello and good afternoon everyone. During Q2, we delivered on our revenue guidance, accelerated our AI pods adoption, launched Glob AI, grew our top line sequentially, and maintained a prudent balance sheet position. We grew on a quarter-over-quarter basis in 5 out of our 8 verticals, and importantly, we grew markedly above company average in our top 50 and top 20 cohorts. Also, in response to observed market volatility, we took actions on our cost structure. I will review our results and then walk you through our updated outlook. Revenue was $614.4 million, within our guided range, slightly up year-over-year, up 1.2% sequentially. On a year-over-year basis, Q2 revenues included 80 basis points of FX tailwind. From a geographical standpoint, compared to the prior year period, Europe and Latin America expanded by 6.8% and 5.9%, respectively. Conversely, North America experienced a 2.4% contraction and new markets saw a 17.7% decrease. The new market segment represented a consolidated drag of roughly 115 basis points to the year-over-year revenue growth figure. Due to the ongoing conflict, this specific geography suffered unexpected project delays over the course of the second quarter. Our cohort performance remained the highlight. Top 50 clients grew 6.9% on a year-over-year basis, top 20 at 6.6%, and top 10 at 4.4%. All well above company average, in line with our 100-squared strategy. 16 out of our top 20 relationships are showing positive year-over-year growth, and we continue to scale recently signed large deals. Adjusted gross margin was 36.5%, slightly down as USD weakness accelerated, primarily impacting our largest delivery center, Colombia, and our utilization remained below our targets. Adjusted SG&A accounted for 18.6% of sales. While adjusted operating margin was 13.2%, below our guided range and driven by the impact on margins. In response to these conditions and to optimally align for subsequent expansion, we initiated a business optimization initiative in Q2. Through this initiative, we ensure the acquisition and retention of the capabilities required for our AI-focused strategy while simultaneously right-sizing our cost baseline to the prevailing market landscape. The main actions under this plan included a comprehensive review of our workforce to align skills and size with our strategic priorities, a consolidation of our global office footprint based on an analysis of our facilities and lease contracts, A strategic prioritization of our delivery centers to support future expansion. In connection with these actions, we recorded a one-time charge of $32.3 million in the second quarter, and we expect some actions to flow into Q3, which will be critical in protecting our profitability in the short term given the current FX headwinds we are facing and will be reinvested to fuel our growth engines, specifically our AI platform development and our people. Despite FX headwinds, we plan to improve margins with the additional efficiencies planned for Q3 and increasing our AI pods in the mix, which operate with margins above company average. Adjusted net income came in at $60.3 million with a 9.8% adjusted net income margin. Adjusted diluted EPS ended at $1.40. Our balance sheet remains a source of strength. We ended the quarter with $168.8 million in cash and short-term investments and $253.1 million in net debt. Free cash flow for the quarter was $12.6 million, and free cash flow for the first half of 2026 reached $48.7 million, a record for the company. On capital allocation, the share repurchase program our board authorized in May, up to $125 million over six quarters, is active. At today's valuation, buying Globant remains one of the highest return investments available to us as the market is pricing Globant as a legacy services company in a soft cycle when, what we are, is the fastest scaling AI native delivery platform in our industry. At the current valuation, the company is trading at double-digit free cash flow yield on a normalized free cash flow basis. Now let me turn to our outlook. Three external factors have primarily impacted our May expectations, and our revised guidance incorporates all three. In May, the lower end of our guidance contemplated a significant deterioration in our new markets region that at the time was not reflected in our forecast. That scenario materialized, and our expectations for the second half of the year have now changed in the region. Our commitment to the region is long-term, and important relationships there continue to grow. But the prudent assumption today is that this environment persists in the short term. Second, we have seen some of the knock-on effects from oil prices, pressuring the travel ecosystem. Some of our travel clients have slowed the pace of their transformation programs to protect their own P&Ls. Even as others in the same industry accelerate with us. We believe this is a deferral of ramps, and we expect this revenue to return as industry volatility dissipates. Finally, we continue to observe protracted cycles in discretionary decision making. As a result of the above, we are revising our expectations for the second half of the year. For the third quarter of 2026, we now expect revenue to be between $607 million and $615 million. We expect a non-IFRS adjusted operating margin between 13.5% and 14.5% and the IFRS effective income tax rate in the 21% to 23% range. Adjusted diluted EPS is expected to be between $1.43 and $1.53 per share, Assuming an average of 43.2 million diluted shares outstanding. With respect to the full year, we are revising our revenue guidance to a range of $2,428,000,000 to $2,462,000,000 from $2,462,000,000 to $2,508,000,000 previously. In terms of profitability, we now expect our adjusted operating margin for the full year to be between 13.5% and 14.5%, driven by the increasing USD weakness. The IFRS effective income tax rate is expected in the 21% to 23% range. We now expect adjusted diluted EPS of $5.75 to $6.15, Assuming 43.6 million average diluted shares. We expect strong free cash flow generation in the second half consistent with our seasonality and our capital allocation priorities are unchanged. The repurchase program and the continued build out of AI pods. The business optimization initiative we carried out this quarter will be visible in our margins as we exit the year, positioning us to enter 2027 with a leaner cost base, record revenue per glober, and our highest margin delivery model, AI pods, approaching by year end close to 4% of revenue on a run rate basis. To conclude, the transition to AI pods accelerated, we achieved record productivity, and we performed strongly within our top clients. The strong demand we see in AI pods validates our industry view, one we feel will transform in a positive way. We will be laser-focused on this transition of our delivery model in order to accelerate these trends. Thank you for your continued support.

Thank you, Juan. And hi, everyone. So as we go through the Q&A section of this call, I will first announce your name. At that point, please unmute your line and then ask your question. Please mute your line after the question is done. And I will also ask you, please, to limit yourself to one question and one follow up. So thank you very much. And with that in mind, we will take the first question from the line of Brian Bergen from TD Cologne. Brian, please go ahead. Your line is open.

All right, thank you. Hi, guys. I wanted to ask on the business transition. So you're showing strong sequential growth in pods, now targeted at 110 million. I think that's up from 60 to 100 million before. Based on what you're seeing here, just how long are you anticipating this transition period to be as Globe AI and the pod model scales, whereby it can drive a reacceleration in the overall company trajectory? And I guess as it relates to your revised revenue outlook for 26, I think the midpoint of the constant currency forecast is down just under 2%. How much of that is intentional impact as you move under this engagement model versus macro headwinds on the business?

Okay, let me tackle the first one. That's a very important question. I think that the transition to the new model is something that we are doing it, you know, in a deliberate way. And it's something that we will keep on executing quarter over quarter. Honestly, the demand that we have seen and the acceptance of the model and the positive signs we're seeing from the market are very encouraging. Still, it's a small percentage, but we think that we will keep on accelerating this. Now, if you ask me, I mean, if the revenue from the new markets wouldn't be affected, we would be already in the positive growth side without the need of reviewing the whole forecast for the year. So I think overall it's a very positive moment and it will accelerate a lot the growth probably by the end of next year. We will see a pretty strong effect of that kicking in. Now, I cannot say right now, I don't think it's a piece of information we can describe right now in a very exact way. So with that caveat, I will let the second part to Juan.

Thank you. Hi, Brian. So the guidance for the year stands now at 2.445 at the midpoint. That is minus 0.4%. And the FX tailwind there is about 70 basis points. So organic constant currencies, you would be talking about 1.1%. Now, when we look at the guidance change, the majority of it is explained by a reduction in the forecast for the new market business. You know, the week after we reported back in May, there was all this news from Saudi, you know, reducing budgets, delaying projects and things like that. And as you know, it's a market that we have been expanding Thank you very much. It's also somehow related to what is happening there because the increase in oil prices impacted some of our businesses in travel and hospitality. And that implied a reduction in the second part of the year forecast for some of those customers. And then the rest is a little bit of a mix between some assumptions we're doing on certain migrations plus the overall business environment in where we are right now. But again, I think, and it's important to also look at, you know, how the new business and the part of the business that we are pushing very, very hard is evolving. Yes, it is still small, but when you start to compound, you know, at 40, 50% quarter over quarter rates very quick, you know, it starts to become more relevant. And as you pointed out, you know, we have been talking about $60 to $100 million for this year. Now we are already over 52 million dollars and with very good visibility of the second half of the year because we are kind of passing through the first initial stage of trying and testing and and, you know, understanding what it means to work with an AI pod and many of our top customers, actually 45% of the top 20 are already using it. And what that means is that those customers are starting to scale. So we feel confident about the ability to scale this business, you know, to over $110 million by the end of the year.

Okay, thank you for all that detail. Just to follow up here on the optimization you took, just talk about the savings you anticipate from those programs.

Yeah, basically what we're doing here is we have been, you know, We are reviewing our workforce and aligning that to the current level of demand and also to the current needs of the business with the new models that we have in front of us and also with the skill sets that are required with a new way of delivering services that we have established. And because of that, We had to make some changes in the organization. Also, in terms of delivery centers, we optimized again our delivery centers. The impact in the second quarter of that was roughly $32 million. We're expecting around $20 to $25 for the third quarter, and that will finalize the program for the year. We think that that is going to help us first save money, because otherwise we would have had Thank you very much. In the last, since the election of the new president, it appreciated almost 15%. And that is a massive, massive impact on our numbers. So we are going to offset that. We are going to invest more as we were discussing in the call, you know, we just announced a new CEO for our global AI business, and we have to invest in that business because we believe that's the future of the company. So we will be using the money for that, and that will save us costs that we would have had otherwise.

Okay, thank you.

You're welcome.

Welcome.

Thank you very much, Brian. The next question comes from the line of Tin Singh Pong from JP Morgan. Tin Singh, please go ahead.

Thank you, Arturo. I think I want to ask on the optimization. I just want to make sure I understand just what, like you said, Martin, you're taking this decision very seriously. What areas were impacted exactly? How much of it was influenced by what you saw in surprising you in May versus the shift to the new model? Or is it really more about the delivery centers and better aligning yourself with some of the effects and inflation trends like you talked about with Colombia? I just want to better understand that.

Well, hi, Dean Jane, how are you? The whole program has different reasons, right? And you see that on one side, we are migrating to this new model that requires A certain type of, you know, forward deploy engineers and certain type of AI engineers that are slightly different from what we used to have. So there's a transition on the talent that we are seeing that is causing one of the reasons of the optimization. Also, we are seeing like also a transition on the demand of the traditional business. The demand of the traditional business is moving away from Web UI testing into more data and more cloud and implementation. And that transition also created some demand of new profiles that we didn't have that much before and we needed to start training and retraining. So the effect has that. Also, it has been the impact of several programs and things to run more efficiently the company. As you saw, the increase on the revenue per head has a big message inside that as we're becoming more efficient to deliver our revenue. So it's an effort. that has like many different components inside of it. I don't know, Juan, if you want to add anything to that.

No, I think, I mean, it's basically, you know, it's a kind of a reshuffle, not a reshuffle, but maybe reskilling our workforce to the new type of demand, also to the new type of services that we are seeing that we are providing to our customers, you know, through the AI pods. You definitely require different skill set. And also we need to protect our margins. We need to make sure that, you know, we offset all the US dollar weakness that impacts our Latin America business. And, you know, also make space for the investments that will be required in Global AI.

How much was the FX impact in the last year?

If we were to look at all the currencies in Latin America for the last year and a half to two years, we are talking an overall impact just for the FX of about four percentage points. Now, we have, of course, we have been able to increase our revenue per head. That helped us offset part of that. And we also made some efficiencies last year that also helped us. But the magnitude Thank you very much.

Looking back over Globan's history, I always think of the 50 squared account approach as being really, really important. So thinking about Globea and how this ramps, can you just give us an idea of What the revenue per could be from a client perspective as you penetrate your top 10, 20, 30 as you learn? Is there any analogy or parallel that we can draw back to how Globant grew under the prior model and assign that to the new model? Just trying to better understand how this can ramp beyond some of the metrics you gave for this year.

Yeah, we're seeing very good traction on the 100 Square program. Indeed, it grew, you know, that group of customers grew, the top 50 grew like 7% or something like that, which is extremely encouraging, right? And it is... where we are delivering these new things and the first experiences during these last nine months of execution or a year of execution of our AI pods. So this is very encouraging by itself. In terms of amount of revenue, I would say Let's say that we maintain the gross margin as we have a much higher gross margin on the AI bots. And we have maybe, you know, let's say $1.2 to $1 on the new service line. So overall, but not in all accounts. I mean, it depends a lot on every single account. And remember, this is not, it cannot be traced back to the original model because the original model was headcount. This new model carries tokens plus token supervision in a single price, either per million token or per output, and that creates a totally whole different math. And this new math is You know, a place in which you can optimize margins, you can improve supervision, you can do it more with less, or maybe, you know, in some accounts we need to put more supervision for certain specific projects, but it's managed in a totally different manner from before. And that's why, you know, I like to say that this transition is not just, you know, like a playground that we started. I think it's the future of a company moving, you know, to output, to consumption, to value from a totally different model of before. I'm not saying that this old model or traditional model will disappear. But yes, I'm saying this is a transition. Step by step, you are seeing us gaining momentum on this new way of delivering, on this new way of charging our customers that is absolutely decoupled from the traditional way. So making a parallel between those two things sometimes becomes difficult. And honestly, we have not much story. I mean, we have one year implementing this. We already have some signals. It's enough for us to put a pricing on those million tokens or these outputs that we are charging. But we need to see many more things happening to be able to take the kind of conclusion you want. But what I can say is, listen, we're evolving in a very nice way, growing in a very nice way. It surpasses my own expectations. I said it, you know, 60 to 100 now is at least 110. And I think it will keep on compounding because it makes a lot of sense for our customers. And many of the new projects that I refer to in past earnings calls about changing interfaces, automating processes, making sense out of massive amount of information. All these things require a totally different way of delivering. And this new service is not just for those 100 square customers. and this is a beautiful part because this is also to serve other segments which may be smaller and we are still learning how to do it but we will keep on expanding on this as we progress. I don't know Juan or Fer, if you want to add something.

No, like the last thing you said, it's quite important. With Globe AI, we're also thinking of how to widen our base of clients, how to serve clients with different kind of scale. that we couldn't do it before in different segments to accelerate our revenues. You know, we've proven the success of the 100 square model, like you said, and it is very well reflected on the growth of at least our 50 top customers. And now the challenge is like sustaining the growth of a segment of client that has a different dynamic, right? And needs to be served differently. And we believe that with Global AI, we also have a way to reach and much wider base of customers with a simpler way and a more sustainable way.

Thank you for the thoughts. Nice to see you all.

Thank you.

Thank you, Tianjin.

Thank you, Tianjin. Nice to see you. The next question comes from the line of Maggie Nolan from William Blair. Maggie, please go ahead.

Hi, thank you. Nice to see you. Maybe I wanted to... to build on um you know one of one of those past questions you um let's see okay yeah so you had talked about in the comments that you thought that globe and ai and having a focus on outcomes was opening up new budgets to you that you hadn't had access to before so maybe can you elaborate on Where that growth is coming from? Who are the new buyers? Is that growing? Or do you view a total addressable market as growing? And kind of what's changing your ability maybe to go deeper in clients?

Well, look, that paragraph that you mentioned refers to the following. For years, as I described at the very beginning of the Ernest Call, we have been creating experiences and software products and, you know, we have been very close to using technology to create experiences that engage in an emotional way with our customers. And that is the core. That's what defines us. Now, as AI came, there are many other places in which it's not just creating those experiences but also operating part of those backends and processes that before were not an opportunity for us. So, as I described that the process automation and the change on the landscape of how to use AI for pretty much everything, including automating every process, creating new org charts that reflect that automation, we believe that our AI pods, which are now the AI pod software that what you see in Globe AI if you go, will evolve into operations and will include that same concept of having an agent operating something for you and having humans being able to and many more. I think that this Globe AI concept is opening up is much larger than the software development lifecycle that we have right now on the Globe AI. So what I'm saying is with this new idea, with this new concept, with this new definition of how services and AI native services will be rendered, We are able to tackle much more than just the original software development lifecycle, and we can expand our presence into AI pods for operations, right? So that's what I'm referring about when tackling new budgets. And also, There's a market share game too, because when you present this new way of doing things, a new way of charging things, for every dollar you sell on this new model, you are able to capture maybe another dollar of that same budget because you are doing things that other vendors were doing. So we're extremely excited about those two things happening at the same time, expanding into other places and capturing more dollars In many cases it was the reason why we win and we won. I have many examples, several examples. Otherwise, we would have lost. And I think that this new definition of how to do things and how to charge for things is a really new approach to our customers. And we launched it last Thursday. And the impact, the amount of people that called us, the amount of people that are interested in understanding more about how this Globe AI model works, In essence, it's a huge effort and a huge intellectual effort in creating something that didn't exist. And we are the first providing it. So I'm extremely proud about the whole team that is developing this. I'm extremely proud that we are being able to take it to the market. and we are being able to convince our customers and not just you know a couple of cases now it will be more than a hundred million dollars in ARR that is an absolute success and I think that this is something that you will see us insisting more and more and deliberately entering to asking our customers to change the model and That will be a process. That will be a process itself. It reminds me to some other companies changing how they do business. But I think overall, it will be very exciting to see that transition and to run it.

Thank you, Martin. That's super helpful. Maybe then, obviously, AI Pods is the growth driver here and Globant AI. But EMEA was expected to be kind of a future growth driver for the company and has been an important region in the past couple of years. So you were pretty clear that you were conservative or prudent in your outlook for that region as the dynamics there have changed. I'm wondering where from an end market perspective, whether it be vertical or geography, you're turning your attention to as a potential growth driver over the next kind of 12 to 18 months and what we should look for for success metrics there.

Excellent question, Maggie. Listen, the ocean of our industry is pretty flat. I mean, you see other companies, you know, they are pretty much all of us in the same kind of level of growth. What we found is that It's not just the old model with new tools, the success, but a totally different delivery system. When I see that we found a place that is growing fast and we want to build our company around it, it's exactly what we want to do. Now, there are some industries that are taking this faster than others. We are seeing a lot of success in financial services, in media and entertainment. We're seeing a lot of success in airlines. This thing makes things more efficiently and faster, 30% faster. As we see the game evolving from cost reduction, that has been the main focus of everybody with AI today, to revenue generation, which is what I believe is the smart way of using AI, then we will see a lot of industries coming into this space. The message I want to convey is not just a region or an industry, but it's a new way of delivering what you should pay attention to. And that's why I ask you to hold us accountable to that number of transition as we evolve this company. This is where we are putting our energy on how to deliver in a much more efficient way. It's not just adding people plus AI. People plus AI means a lot of slope, a lot of time used to supervise that slope, a lot of rework over and over and over. And when you put order in that process, like what we do with our Globe AI way of delivering, then things become much more efficient and things become independent on the model that you want to use. and things become scalable. And then, you know, this is just the beginning of Globe AI. Globe AI is much broader than that. Globe AI is the initiative we will use to transform Globan into an AI-native company. Thank you Maggie. The next question comes from the line of Brian King from Citi. Brian, please go ahead, your line is open.

Hi, guys. Just wanted to ask, Martin, when you talked about you're making a choice to push more work to AI pods, and that seems like it's costing your existing business or hurts the core revenue of that business. Can you just talk about that deflationary pressure and why that doesn't last longer as we go for this transition over the next couple of years? Are we going to have to kind of run in this negative revenue territory due to the deflationary pressure that maybe pushing work to the AI pod model is going to cause?

Well, look, I mean, in essence, a lot of our customers has been spending the same amount of money getting more productivity, right? And that has been the case in the vast majority of the things. I think in the future that could evolve to First, you know, we need to convince procurement, we need to convince more people, and that process could be slower than just running the traditional game, but we're ready to pay that. And I think that the margin overall will be much better. and the capabilities for us to improve the margins even further from where we are today are still better. So I think that that deflationary scenario is something that we're not seeing right now. If we need to pay for it, we will pay it. I want to do that transition. And that's why I said, listen, we are migrating this Even understanding that in some accounts, this will be, you know, some demand, softened demand, but I believe that overall the picture will be totally different. I don't know.

I was going to say, Brian, that sometimes what we've seen is that It may take a lot longer to convince, to persuade the procurement teams and persuade our customer to transition and keeping the same level of revenues with more productivity. So sometimes we're seeing that we know that we want to migrate. And sometimes we can do it faster if we are willing to provide some efficiencies immediately to the customer in terms of price. But we believe that because the model is so much more efficient and it makes so much more sense for them that we should be able either to expand on other areas of the organization, to win market share from other vendors. Because what we believe is that this is the model in which we need to deliver services. Now, if we can do it faster, we will do it. And I think that's a key message that we are giving here.

No, that's really helpful. And then just as a follow up, Juan, the revenue per head, it jumped to almost double digits. How much of that is like for like pricing or are you guys getting a little better pricing right now in the market? Just trying to understand that number.

It's a combination of different things. The market is competitive, right? There are some occasions where we are being able to deliver with less headcount because we are being more efficient with our delivery model right now. And if you look at the total headcount, it's down roughly almost 10% year over year with revenue per head going up almost 10% year over year. So we are being more efficient. In some cases, we have been able to get some additional pricing, but I wouldn't take that as the norm because the market is very competitive right now. But we have been able to increase our revenue per employee because we are delivering in a more efficient manner. In some cases, we are charging that with a new model, with the AI pod revenue or the global AI revenue model. In other cases, it may be a fixed price where we are able to deliver You know, more efficiently and hence increasing the revenue per head. So it's not that we are charging like Globe AI because it may be a fixed price, but we're still getting more revenue per employee because we are delivering more efficiently. And I think that explains the sharp increase. If you look at the revenue per head three, four, five years ago, it was around 60, $65,000 per employee. Now we're getting close to, you know, over 90 and getting close to 100.

Yeah, okay. Thank you so much.

You're welcome.

Thank you very much, Brian. The next question comes from the line of Arvind Ramani from Trust Securities. Arvind, please go ahead.

Hey, thanks, everyone, and good afternoon. I just had a couple of questions on Globe AI. Martin, you said that's kind of where you focused your efforts on. So just a couple of questions over there, right? How does the workload split across these OpenAI and Anthropic and Thank you for joining us. What is your unique value proposition? Is it around enterprise context? Is it around routing logic? What's proprietary to your firm?

I will start with the first and then the last and I will let Diego to complete. I see that we just announced a partnership with Anthropic. We are extremely excited with the things we can do together. We are already seeing some impact from that pipeline coming into our scope of work, which is very, very exciting. I see models that will be evolving, and our customers will decide, kind of what to use. When we see Globe AI, we see something that it could use pretty much any model on the core when developing the software or creating the software, including open-weight models, if that's the case. We're processing a big chunk of our tokens with our own infrastructure and our own models, using open-weight models in many occasions. And some of our customers are requesting that. Some of our customers are saying, we'll go full-fledged with Anthropic or with OpenAI. So we have pretty much full independence on that specific thing. And of course, Tools will keep on evolving and keep on becoming more and more sophisticated. To the specific mode that I would like to describe about Globe AI, Globe AI is a play of services. Basically, we are mixing in an absolute frictionless way the creation of the experience and the creation of the software and the definitions that you need With AI and with humans and putting, unpacking everything into a single price and a single price per consumption or per output. So basically what you saw, the demo, the video that Diego showed to us is a video that It makes it very clear that Globe AI helps you with the definition, helps you with the creation of a specification, and then a set of agents get triggered, a set of loops get triggered, or a workflow gets triggered. Depending on what you need to do, those things are different. And this playlist has been curated and evolved with time. And depending on which playlists you are using, they will require different levels of supervision for humans that are watching what those stations are creating, but we charge you in an extremely transparent way, either per million token or per output in case of a user story or in case of a... What I'm saying is that this coordination, that elimination of the friction to buy services is the real thing that we are providing. And that's independent on any model that you may choose, right? So that's why we are saying this is real AI-native services, because it's playing the same role that in the past when Cloud didn't exist and we needed to create compute. You buy the servers, you buy the connectivity, you used to hire the people to manage those servers and then suddenly someone coordinated everything in a beautiful way and it was so easy to ramp up infrastructure, right? Well, professional services is in that old era and with Globe AI we're creating the AWS of the services. So it's extremely easy to go, explain your project, create the context, connect with your Jira or with your GitHub or with your whatever repository you want, and then execute the mission. And that mission will be played in an extremely professional manner, supervised by the best people that can lead you to the right enterprise result you are looking for. that concept it seems too easy but it's extremely sophisticated is the evolution of services and it's not just a platform and it can play with codecs with with our own coda it can play with a cloud code it can play with pretty much any of the ai tools that is that are out there and uh And then it can be played into any type of infrastructure. But the thing is, it always gets coordinated in a pretty efficient manner with a human. So no more ramp up of teams, no more long time of procurement for something, no more not understanding how much something will cost. It's a totally different thing, and that's why Diego showed the video. But I don't want to screw your speech, Patricio.

Sorry, I think it's good and it's a totally valid question. One of the things that we do find on every single enterprise project has a ton to do with accountability and repeatability. You will not get accountability from a frontier model. You use it, you get a result. Is it good? Is it bad? It's up to you. You implement. That's what you get. Globan brings you accountability. That's why we have humans. And the second most important aspect is that The model is not repeatable. When you go to a frontier model, it does, I mean, generating output is super good. It's amazing. And we are capturing 100% of the value there. But when it comes to planning, how do I execute something, it's a combination of how you're prompted, How much information you gave it? What's the decision and thought process on the model? It consumes a ton of tokens for solving sometimes easy tasks that could have been solved with probably 10% of the spend, as an example. and we actually moved away from that and what you see there as battle cards are actually the formulas of how's the proper what's the proper way of delivering this type of value what's a proper way of doing a migration a re-platforming SAP for HANA migration as an example that blueprint It has a ton of very well-defined steps. Every step, it has a required input, not less, not more than that, and a required output, and a supervision for that, which is called a quality gate. So you get a repeatable system, a system that you can feed over and over, and you get the same. And this is something you don't get out of AI. It drives enterprises crazy. So you don't have control on the spend. In many occasions, models are actually working with both ends, the definition and the testing. And what happens in the middle kind of looks like brute force, like trial and error, and that's how graph engineering In many occasions work. So we totally change this and we use what we know where the humans can actually add value, make sure things are completely right and you provide the accountability for that. So Coming back to your original question, I think that every single company we talk to actually finds a lot of comfort and feel a lot more comfortable with this model. This is the type of services we've been provided for over 20 years. It's not about the output, it's not the source code, it's not the executable. It's about the process, capturing what the client actually needs, providing value on top of that, and holding yourself accountable for that output, for that business impact.

Let me illustrate this with one example, which is from a few days ago. What happened was one of our customers at Globe AI needed to do some kind of architecture definition for a pretty complex project around ERPs and APIs and connections. He dropped it on Globe AI. Our guys got it. I mean, the agents start to do the work. Supervision happens across that work. We interact with the customer two or three times, and we finish that in a record 48 hours. Now, to do that same thing, even with Globe, and in a traditional way, would have taken at least two or three weeks. because of the meetings and the things and the scope gathering, all those things, all that process that was extremely inefficient before has been concentrated in a very simple way of doing it now. So that value that is created by understanding the customer faster and getting to the accountable result as Diego was describing is the main thing that we are talking about today. And that's the transformation I want to make for the whole company, for the whole globe and for our whole customers. And I think that yields much better margins, much more predictable revenue, much more consistent and recurrent revenue. It yields, I believe, results for our customers that are way beyond just using AI with a set of engineers. And I think it's the answer that many will follow. And it's not just us. I mean, we are starters and we are innovators in this vision. But it won't stop here. So it will be fun. Yeah.

Just quick follow up.

The next question comes from the line of Jonathan Lee from Guggenheim. Jonathan, please, your line is open.

Great, good to see you guys and thanks for taking my questions. Martín, I appreciate the vision and understand you're not seeing deflationary pressure today around AI pod work, but how are you thinking about combating it when it does emerge, particularly when clients come back at renewal and demand a larger share of productivity gains? What are the structural defenses in the pod model that let you hold pricing when procurement inevitably pushes back?

I didn't get the question exactly, please. Can you repeat it again?

So as you think about the deflationary pressure that you're not seeing today, what happens when you need to combat it going forward if it does emerge? And is there anything structural in the POD model that lets you hold pricing when procurement pushes back on pricing?

Oh, well, okay, listen, I think that if procurement comes back, there's always a negotiation, but that happens in every single model, not just in the AI pod model. But the thing is, I believe that we can be much more efficient. If the customer wants to do the same, it will cost less money. If the customer wants to do more, which is most of the cases that we are finding, they will spend the same amount of money. And the thing is that the customers will want to do a lot more. So I think they will end up spending, in this new model, more money to produce much more, as we have been describing in the past. So, again, this is a game about the amount of software and the amount of solutions and processes that must be created or used in this new era. So I think the game cannot be predicted that simple, saying, putting like a constant value on the amount of things that can be done or that are needed, sorry. So as this amount of things moves everywhere, and in many occasions moves up as the new needs, as described before, the new needs happen, then where you land with the AI pods there, I think, in my opinion, will be increasing. Now, that's demonstrated on the top accounts that are growing at 6.9%.

And also, you know, even in that scenario, potentially, you know, when a customer wants to do the same and there is a potential saving for the customer there, the model runs at higher margins than the traditional model. And, you know, maybe we will be making the same dollar amount. Thank you very much. Thank you very much. But there are some occasions that we are seeing that if we take a haircut, we can accelerate the migration. And this is what we are saying today. We are willing to accelerate because it gives us a better position in front of the customer. It protects us from the competition. And we believe that it's going to drive more business into global and going forward. And at the same time, because we can be more efficient, especially as we scale, there is more margin to be earned along the way. So there are multiple things happening at the same time. It's still hard to model even for us. We are building models, you know, every day. But we are seeing at least that there is clear traction, that there is a clear improvement in margins, and that the customers are enjoying and are coming back to scale the model. So that's how we are looking at this.

And the game and the play for us is we found that space that is growing fast. We want to expand that transformation and do it faster. This is a very simple way of putting it.

I think one additional thing is that, as we all know, the market as it is today has been concentrating on the cost saving machine and operational side of things. And it's a lot more difficult. I think it's a worse scenario because it's a lot more difficult when you're part of the cost equation, right? And that's the type of pressure and companies may want to reclaim part of those efficiencies. Once the markets start recovering and moving to a revenue generating engine and stop neglecting products, as an example, things will definitely change. We've seen this over and over. Thank you for watching!

Thanks for that color. And just as a follow up, you know, what macro backdrop is assumed in the revised 26 outlook? I mean, does the range assume current conditions persist, maybe some stabilization, North American decision cycles or continued deterioration? And how much cushion is built into the low end for further softening?

Now, I think that, you know, the most likely scenario is a midpoint that it basically assumes already significant impact from the Middle East and and our travel customers. Of course, I mean, again, if things get a little bit better on the macro side, especially in the U.S., we can probably be a little bit above that range. And again, if things get worse, we are not seeing that now, but, you know, the cushion is there just to make sure that, you know, the range is something we will achieve no matter what. But the most likely scenario for us is always the midpoint. and unfortunately we have seen some factors that impacted our previous guidance that we could not control. But the midpoint is our most likely scenario. We have plans to get higher than that. We will try to execute on those. There are some things that we don't control.

And to add to that, 90% of our revenues throughout the year are already contracted. And in some of the markets that have been affected, particularly in new markets, we have pivoted the pipeline towards banking, towards public sector, areas that are less affected as entertainment and travel and hospitality, which was Thank you very much, Jonathan. Nice to see you. So that will be all for the Q&A section today. Thank you all for your time. And now I will ask Martin to provide some closing comments.

Thank you, Arturo. Thank you, everybody, for being here today. I'm looking forward and thank you for your continued support. I'm looking forward to see you on our next quarter call. Thank you. Goodbye.