Prepared remarks
Good afternoon, and welcome to Globant's Second Quarter 2026 Earnings Conference Call. I am Arturo Langa, Investor Relations Officer at Globant. 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 Martín 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, and 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 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 three 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 Glob.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 Glob.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 4x the size of the IT services market that Globant has been evaluated in. We are taking this deliberate decision to respect our current market while expanding our offering to a larger total addressable market. We are steering clients toward the new model. Our AI-native delivery system of AI Pods has now been adopted by 45 of our clients in many of their projects. Today, practically everything we deliver carries AI. We do not count that as Glob.AI ARR, which only captures the revenue that is delivered and charged differently on the output, value or consumption our clients receive, not on the hours behind it. It is a strict measure and that is deliberate. When this number grows, it is not AI being bolted on to existing work. It is the business model itself changing. 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.AI ARR 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. One, core modernization. There is a technical debt backlog between $1.5 trillion 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 Vercel and Claude-powered AI Pods are turning multi-month rebuilds into same-week releases. Three, 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 proving their depth, since that top 50 growth is a sign they understand these clients' industry as well. Our core business is acting as the distribution engine that carries Glob.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 towards 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 the 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 in plain 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 named 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 Sarab Narang is joining us as Glob.AI's CEO. Sarab 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 multiyear 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. GUT 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'm 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, Martín, and hello, everyone. Martín just laid out our strategic vision for Glob.AI and how it fundamentally changes the way clients engage our services. My focus today is on the engine behind it: the technology architecture, the operational mechanics and the first-mover advantages that allow this delivery model to perform at scale. The legacy professional services model trades hours for bespoke solutions, forcing engineers to re-solve the same foundational problems repeatedly. Glob.AI breaks that cycle by operating as an asset-based engine. Within the platform, we have codified more than 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 realize extraordinary cross-industry compounding value. An IT root cause analysis workflow created for an airline, for example, can be replatformed into a pharma supply chain or a media distribution pipeline in weeks rather than months. AI Pods are 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 described when introducing Glob.AI, raw LLM prompting creates significant token waste, hallucinated logic and costly retry loops that require extra 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, and enforces quality gates at every step. By optimizing the orchestration layer, we ensure that every token consumed produces verifiable, production-ready output. Enterprise adoption also depends on control. Glob.AI’s architecture intelligently routes across more than 140 LLMs, delivering full model independence so clients are never locked into a single provider. Crucially, every transaction is secured within the client’s dedicated token vault, guaranteeing absolute token sovereignty. No client data is exposed or used to train third-party models, enabling organizations to safely compound their institutional intelligence over time. We are seeing the impact of this platform model 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 lets us decouple improvements in output, velocity and delivery from headcount growth. It also enables us to layer in nonlinear and recurring revenue with structurally better unit economics that will, over time, transform the business. This is the structural signature of moving up the value chain. Now I’d rather show than tell. Let me take you on a short Glob.AI tour. The capability behind Glob.AI is not theoretical; it reflects what we have proven in the field. In prior calls we shared how early AI Pod deployments produced milestone efficiency gains: accelerating drug discovery research at PharmaMar by 15x, compressing supply chain contract cycles by 40% at YPF, and reducing legacy migration timelines from 14 months to 2 months. What makes Glob.AI so significant today is that those high-impact successes are no longer bespoke projects. We have productized those learnings into our standard catalog. Backed by deep co-engineering alliances with hyperscalers and model providers, Glob.AI turns proven enterprise outcomes into an on-demand, repeatable capability available to every client from day one. We have built the underlying platform, established the governance framework and proven the economics at scale. Everything I just described compounds into one metric: Glob.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 five out of our eight 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 remains 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. Sixteen 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 rightsizing 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, and 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 billion to $2.462 billion, from $2.462 billion to $2.508 billion 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. And with that in mind, we will take the first question from the line of Bryan Bergin from TD Cowen.
Questions and answers
I wanted to ask on the business transition. So you're showing strong sequential growth in pods, now target of $110 million. I think that's up from $60 million to $100 million before. Based on what you're seeing here, just how long are you anticipating this transition period to be as Glob.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 in a deliberate way, and it's something that we will keep executing quarter-over-quarter. The demand that we have seen and the acceptance of the model and the positive signs we are seeing from the market are very encouraging. It is still a small percentage, but we think that we will keep accelerating this. Now if you ask me, if the revenue from the new markets wouldn't be affected, we'd already be in the positive growth side without the need of reviewing the whole forecast for the year. So overall, it's a very positive movement, and it will accelerate a lot of the growth. Probably by the end of next year, we will see a pretty strong effect of that kicking in. I cannot say right now in a very exact way. With that caveat, I will let the second part to Juan.
Bryan, so the guidance for the year stands now at $2,445 million at the midpoint. That is minus 0.4%. The FX tailwind there is about 70 basis points. So on a constant currency basis, you would be talking about minus 1.1%. When we look at the guidance change, the majority of it is explained by a reduction in the forecast for the new market business. The week after we reported back in May, there was news from Saudi reducing budgets, delaying projects and things like that. As you know, it's a market that we have been expanding quite nicely over the last few years. We will continue to do so. We see a lot of deals that are just getting postponed or slowed down, but not canceled at all. We keep having very interesting conversations. We are confident about the recovery of that market in the near future. So half of the guidance revision is driven by that. About $10 million is 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. The rest is a mix between some assumptions we are doing on certain migrations plus the overall business environment and where we are right now. It's important also to look at 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 at 40% to 50% quarter-over-quarter rates, very quickly it starts to become more relevant. As you pointed out, we had been talking about $60 million to $100 million for this year. Now we are already over $52 million with very good visibility for the second half of the year. We are passing through the first initial stage of trying, testing and understanding what it means to work with an AI Pod. Many of our top customers, actually 45% of the top 20, are already using it. Those customers are starting to scale. So we feel confident about the ability to scale this business to over $110 million by the end of the year.
Okay. Just a follow-up here. On the optimization you took, can you just talk about the savings you anticipate from those programs?
Yes. Basically, what we are doing here is we have been 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 in front of us, and also with the skill sets that are required with a new way of delivering services that we have established. Because of that, we had to make some changes in the organization. Also in terms of delivery centers, we optimized our delivery centers. The impact in the second quarter of that was roughly $32 million. We're expecting around $20 million to $25 million for the third quarter, and that will finalize the program for the year. We think that is going to help us first save money, because otherwise we would have had part of that talent in the talent pool without the possibility of allocating them to new projects. Second, it's going to help us offset a massive FX headwind that we are seeing because of U.S. dollar weakness. If you look at our largest development center, which is Colombia, since the election of the new President, it appreciated almost 15%. That is a massive impact on our numbers. So we are going to offset that, and we are going to invest more. As we discussed on the call, we just announced a new CEO for our Glob.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.
The next question comes from the line of Tien-Tsin Huang from JPMorgan.
I want to ask on the optimization. You said you're taking this decision very seriously. What areas were impacted exactly? How much of it was influenced by what you saw 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 FX and inflation trends like you talked about in Colombia? I just want to better understand that.
The whole program has different reasons. On one side, we are migrating to this new model that requires Forward Deployed Engineers and AI engineers that are slightly different from what we used to have, so there's a transition in the talent that we are seeing and that is one reason for the optimization. We are also seeing a transition in the demand of the traditional business. The demand of the traditional business is moving away from web UI testing into more data, cloud and integration implementation. That transition created demand for new profiles that we didn't have much before and we needed to start training and reskilling. There are also programs to run the company more efficiently. As you saw, the increase in revenue per head conveys that we are becoming more efficient in delivering our revenue. So it's an effort that has many different components inside it.
It's basically reskilling our workforce to the new type of demand and the new services we are providing through AI Pods. You definitely require a different skill set. We also need to protect our margins and make sure that we offset the U.S. dollar weakness that impacts our Latin America business and also make space for the investments required in Glob.AI.
How much was the FX impact in the last year?
If we look at all the currencies in Latin America for the last 1.5 to 2 years, we are talking an overall impact just for FX of about 4 percentage points. We have been able to increase our revenue per head which helped offset part of that. We also made some efficiencies last year that helped. But the magnitude of the headwind we have suffered in Latin America has been significant and is impacting our margins.
I appreciate that. Just on the model and Glob.AI, looking back over Globant's history, I always think of the 100 Squared account approach as being important. So thinking about Glob.AI and how this ramps, can you give us an idea of what the revenue per client could be 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 apply that to the new model? Trying to better understand how this can ramp beyond some of the metrics you gave for this year.
We're seeing very good traction on the 100 Squared program. That group of customers grew about 7%, which is encouraging, and it is where we're delivering these new things and the first experiences during the last nine to twelve months of execution of our AI Pods. In terms of amount of revenue, if we maintain gross margin higher on AI Pods, the economics are different — AI Pods have a different pricing construct that can optimize margins and supervision. This model is not traceable to the original hours-based model because the original model was headcount-based, either fixed price or time and materials. The AI Pod model carries tokens plus token supervision in a single price, either per million tokens or per output, and that creates a totally different math. These new economics let you optimize margins and deliver more with less. In some accounts we may need more supervision, but it's managed in a different manner. This transition is not just experimental; I think it's the future of the company: moving to output, consumption and value rather than hours. The old model will not disappear immediately, but this is a step-by-step transition. We have one year of implementing this and have strong signals. We had been talking about $60 million to $100 million for this year; now we're at least $110 million. I think it will keep compounding because it makes a lot of sense for our customers. The new projects around changing interfaces, automating processes and making sense of massive amounts of information require a different way of delivering. This service is not just for the 100 Squared customers; it enables serving other segments as well, and we will keep expanding as we progress.
With Glob.AI, we're also thinking of how to widen our base of clients and serve clients of different scales that we couldn't serve before. We've proven the success of the 100 Squared model, reflected in the growth of our top clients. The challenge now is sustaining growth for a client segment with different dynamics and needs to be served differently. We believe Glob.AI gives us a way to reach a much wider base of customers in a simpler and more sustainable manner.
The next question comes from the line of Maggie Nolan from William Blair.
You talked about Globant AI and having a focus on outcomes opening up new budgets you hadn't had access to before. Can you elaborate on where that growth is coming from, who the new buyers are, whether that is growing, whether you view the total addressable market as growing, and what's changing your ability to go deeper in clients?
That paragraph refers to the following. For years we have been creating experiences and software products. As AI came, there are many other places where it's not just creating experiences but operating parts of the back-ends and processes that before were not an opportunity for us. With AI Pods, which are the AI Pod software you see in Glob.AI, we will evolve into operations and include the same concept of having an agent operate something with humans analyzing edge cases, charging per unit of specific business cases — for example, travel per trip or for Know Your Customer tasks. That agentic work opens markets much larger than the software development lifecycle. This concept allows us to tackle more than the original software development lifecycle and expand our presence into AI-native operations. So we're able to access new budgets and capture more dollars for work we used to do. Many of the new projects I referred to in past calls — changing interfaces, automating processes, making sense of massive amounts of information — require a different way of delivering. This new service can serve other segments which may be smaller, and we are learning how to do it while expanding.
That's helpful. EMEA was expected to be a future growth driver and has been important. You said you were prudent in your outlook for that region as dynamics have changed. From an end-market perspective, whether vertical or geography, where are you turning your attention as a potential growth driver over the next 12 to 18 months and what should we look for as success metrics there?
The industry is pretty flat overall, but we have found a space that is growing fast: AI-native services. That is where we are focusing. Some industries are adopting faster than others. We see a lot of success in financial services, media and entertainment, and airlines — areas where AI yields efficiency and revenue generation, not just cost reduction. The message is not a single region or industry; it's a new way of delivering. This is where we are putting our energy. It's not just adding people plus AI, which leads to inefficiencies and rework. When you put order into the process, as we do with Glob.AI, things become efficient and scalable. Glob.AI is broader than the initial offering: it's our initiative to transform Globant into an AI-native company. It will be wide-ranging, not limited to a single geography or vertical.
The next question comes from the line of Bryan Keane from Citi.
When you talked about making a choice to push more work to AI Pods and that seems like it's costing your existing business or hurting core revenue, can you talk about that deflationary pressure and why that doesn't last longer as we go through this transition over the next couple of years? Are we going to have to run in negative revenue territory due to the deflationary pressure that pushing work to the AI Pod model may cause?
A lot of our customers have been spending the same amount of money while getting more productivity. That has been the case in the vast majority of projects. In the future, convincing procurement will take time and could be slower than traditional approaches, but we're prepared for that. Margins overall should be much better. The capabilities to improve margins further are strong. We may see softness in some accounts during the transition, but I believe the overall picture will be different and positive.
Sometimes it may take longer to persuade procurement teams to transition while keeping the same revenue with more productivity. We can accelerate if we provide efficiencies to the customer immediately, but because the model is more efficient, it makes sense for them and we should be able to expand to other areas and win market share. If we take some haircuts in some projects to accelerate the migration, we are willing to do that because it gives us a better position with customers and protects us from competition. We believe this will drive more business into Globant and, as we scale, there is more margin to be earned along the way. It's hard to model precisely, but traction is clear and customers are coming back to scale the model.
That's helpful. As a follow-up, revenue per head jumped to almost double digits. How much of that is like-for-like pricing? Are you getting better pricing in the market? Trying to understand that number.
It's a combination of different things. The market is competitive. In some occasions, we are delivering with less headcount because we are more efficient with our delivery model. Total headcount is down roughly 10% year-over-year while revenue per head is up almost 10% year-over-year, so we are more efficient. In some cases, we've achieved additional pricing, but I wouldn't take that as the norm because the market is competitive. Some of the increase is from the new model—AI Pods—or fixed-price engagements where we deliver more efficiently, hence increasing revenue per head. If you look 3 to 5 years ago, revenue per head was around $60,000 to $65,000; now we're close to $100,000.
The next question comes from the line of Arvind Ramnani from Truist Securities.
I have a couple of questions on Glob.AI. How does the workload split across OpenAI, Anthropic and open-weight models today, and how do you expect that to shift over the next 18 months? Second, you mentioned many clients leveraging Glob.AI are existing clients. What percentage are new clients? And what's Globant's unique value proposition — enterprise context, routing logic, what's proprietary to your firm?
We just announced a partnership with Anthropic and are excited by what we can do together; we are already seeing pipeline impact. Models will keep evolving and customers will decide what to use. Glob.AI is model-agnostic: it can use any model for development or software creation, including open-weight models. We process a significant portion of tokens using our own infrastructure and open-weight models when requested. Some customers use Anthropic or OpenAI; we provide full independence in choice. Regarding our moat, Glob.AI is a services play: we mix creation of experience, software and definition with AI and humans, packaged into a transparent single price per consumption or output. Glob.AI helps with definition, creates a specification and triggers agents and workflows depending on the task. These playbooks have been curated and evolved over time and require different levels of human supervision enforced by quality gates. We charge transparently, either per million tokens or per output like a user story. This coordination and elimination of friction to buy services is our key value. It's independent of the model chosen and plays across any infrastructure. The platform coordinates efficiently with human supervision so there is no long ramp-up, procurement delays or unclear costs. This is the evolution of services: the AWS-like experience for professional services where you can explain your project and execute the mission professionally with accountability and repeatability.
That's a valid question. Every enterprise project needs accountability and repeatability. Frontier models provide excellent output but not accountability. Globant brings accountability through humans. Models are often inefficient, consuming many tokens for tasks that could be solved with far less spend. Our battle cards are formulas for the proper way to deliver specific value: well-defined steps with required inputs and outputs and supervision quality gates. You get a repeatable system that yields the same result. That is what enterprises need — control over spend and predictable outputs. We changed the approach by using humans where they add value, ensuring things are right and providing accountability for business impact.
Let me illustrate with an example. A few days ago a customer dropped an architecture definition for a complex project around ERPs and APIs into Glob.AI. Agents began work, supervision occurred across that work, we interacted with the customer two or three times, and we finished in a record 48 hours. To do the same thing traditionally would have taken two to three weeks because of meetings and long discovery. The inefficiency of the old process has been concentrated into a much simpler way of executing now. That speed and accountability is the value we provide and the transformation we want for the whole company and our customers. This yields better margins and more predictable, recurring revenue, and it is more than just using AI with engineers. It is a new delivery model many will follow.
The next question comes from the line of Jonathan Lee from Guggenheim.
Martin, I appreciate the vision and understand you're not seeing deflationary pressure today around AI Pod work. 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?
If procurement pushes back, there will always be negotiation, but that happens in every model. We can be more efficient; if the customer wants the same scope, it will cost less. If the customer wants more, which is the case in many instances, they will spend the same amount and do more. The amount of software and solutions needed in this new era is expanding, so we expect spending to grow. The model allows flexibility: sometimes we will take a haircut to accelerate migration, but because the model runs at higher margins than the traditional model, there is structural room to absorb pressures while winning market share and earning more projects. We are willing to accelerate the transition to secure a better long-term position and protect margins as we scale.
Sometimes customers will want the same scope at lower cost, and we might accept a haircut to accelerate the migration. The model runs at higher margins than the traditional model, so even with a slight revenue reduction in some accounts, the dollar economics can be favorable. We believe delivering services this way will win market share, drive more business into Globant and improve margins as we scale. It's hard to model precisely, but the traction and margin improvements are clear and customers are scaling the model.
The game for us is we found a space that is growing fast and we want to expand that transformation quickly. That is the simple way of putting it.
One additional point: today the market has concentrated on cost savings and operational improvements. That places firms in a worse bargaining position because you're part of the cost equation. Once the market recovers and moves toward revenue generation and product improvement, demand dynamics will change. Product-focused initiatives drive features, content and differentiation — not just cost saving. We expect to see that shift and it will change how value is captured.
As a follow-up, what macro backdrop is assumed in the revised '26 outlook? Does the range assume current conditions persist, some stabilization in North American decision cycles, or continued deterioration? And how much cushion is built into the low end for further softening?
The midpoint is our most likely scenario and assumes a significant impact from the Middle East and our travel customers. If macro conditions improve, especially in the U.S., we could be above the range. If conditions worsen, we are not seeing that now. The range includes cushion to ensure we achieve the guidance no matter what. The midpoint remains our most likely outcome.
To add, 90% of our revenues throughout the year are already contracted. In affected markets like new markets, we have pivoted the pipeline toward banking and public sector areas that are less affected than entertainment and travel. We are rebuilding the pipeline, closing some new opportunities, and we expect new markets to recover toward the end of the year.
That will be all for the Q&A session today. Thank you all for your time. And now I will ask Martín to provide some closing comments.
Thank you, Arturo. Thank you, everybody, for being here today and for your continued support. We look forward to speaking with you on our next quarterly call. Goodbye.