Meanwhile, technical debt across the Global 2,000 stands at $1,500,000,000,000 to $2,000,000,000,000 according to HFS Research, and Forrester reports US customer experience quality at an all-time low, after four consecutive years of decline. What this tells us is not that AI is failing; it is that the industry is entering its execution phase. After an eighteen-month cycle of experimentation, enterprises now understand what AI can do for their business and are actively seeking the capability to implement it at scale. This shift from exploration to execution is currently driving our record bookings.
We are living through a generational transition. Think about what happened when AWS launched. It did not just offer cheaper servers; it gave birth to an entirely new industry. Cloud-native companies, modern SaaS, the entire startup ecosystem of the last fifteen years—none of that existed before AWS made elastic, accessible infrastructure possible. That is the moment we are at now in technology services. AI-native delivery—intelligent agents supervised by domain experts, operating on a token subscription model—is not a better way to do what we already do; it is the foundation of an industry that does not yet fully exist.
Globant S.A. has been the first to define what AI-native technology services look like, and 2026 is the year the market begins to validate that bet.
Our core business—deep software engineering, digital transformation, and domain expertise built over two decades—is not going anywhere. Enterprises will continue to need that capability for many years to come, and we will continue to grow it. What we are doing now is adding a new and powerful layer on top of that foundation, an AI-native offering that scales with the AI opportunity itself. For years, a company’s digital products were its moat. Building differentiated software required hundreds of top engineers and hundreds of millions of dollars. AI has made it faster and more accessible to build, and that is actually a demand accelerant for the entire industry.
When every company can build software more efficiently, differentiation no longer comes from whether you can build; it comes from how much you build, how fast you iterate, and how continuously you evolve. We are entering an era of dramatically more software creation, and dramatically faster competitive cycles. Our deep engineering expertise and two decades of domain knowledge—now supercharged by AI—position us perfectly to meet that demand. Against that backdrop, we see four clear and growing avenues of demand. First, agentic workflow orchestration. Enterprises need autonomous AI agents coordinated across complex systems. Not point solutions, but end-to-end workflows that actually move business processes forward. Second, core modernization at AI speed.
The Global 2,000 carries $1,500,000,000,000 to $2,000,000,000,000 in accumulated technical debt, a massive anchor on innovation. AI-native delivery allows us to attack this backlog at a pace previously thought impossible, enabling the enterprise agility clients need to compete and win. Third, custom software reclaiming ground from SaaS. For years, SaaS was the default answer for enterprise software needs. AI-native delivery is now expanding the range of what enterprises can build economically, making highly personalized software viable for use cases that were previously only practical with off-the-shelf platforms. This is not about replacing SaaS; it is about enterprises having more options, more control over their data, their workflows, and their competitive differentiation.
SaaS and custom software are increasingly complementary, and we are uniquely positioned to deliver both. Fourth, AI governance and corporate sovereignty. As enterprises deploy agents from multiple vendors across departments, data scatters and control erodes. They need a trusted orchestration partner to govern it all and keep every interaction under their control. Our partnerships with NVIDIA, OpenAI, AWS, Salesforce, SAP, Oracle, Microsoft, Google, Adobe, and others are central to this strategy. We are the AI-native orchestration layer that makes it work for our clients.
Our AI Pods are AI-powered service units, specialized by task and industry. AI Pod Software creates and evolves technology. AI agent workflows supervised by Globant S.A. experts produce working software artifacts on a token subscription model. AI PodOps automates business processes in production, with institutional knowledge compounding with every token consumed. The customer owns everything. No seats. Only usage. Unlike traditional models, our AI Pods operate on a subscription-based capacity model. Clients subscribe to a dedicated tier of orchestrated output, with a defined token consumption cap. The delivery engine powering both is Globant Enterprise AI, our proprietary platform with four interconnected hubs.
The Enterprise Hub connecting securely to all corporate systems, the AI Hub routing intelligently across 140+ LLMs while preserving full data sovereignty, the Agent Hub where we build and publish industry-specific agents encoding twenty years of domain expertise, and the AI Pods Hub where clients subscribe and scale.
What I want to be explicit about is that this platform did not appear overnight. We have been investing in Globant Enterprise AI for years, building real product, real orchestration infrastructure, real security and compliance architecture. That investment is embedded in our operating expenses, reflected in our current EBIT margin. In other words, the margin profile you see today already carries the cost of building a proprietary AI platform. Twelve months ago, AI Pods revenue was zero. In 2025, we have reached an exit-rate ARR of $20,600,000 with gross margins between 45% to 60%, compared to our blended gross margin of 38%. This is not an experiment; this is a business.
For 2026, we are targeting between $60,000,000 and $100,000,000 in AI Pods exit-rate ARR. On top of that, we expect that margin profile to improve further as the subscription model scales and the cost per token continues to decline. This represents a fundamental shift in our structural profitability DNA. As AI Pods scale as a share of revenue, they are expected to expand our overall margin profile. Our AI Pods pipeline reached $283,000,000 in Q4, up 34% over Q3, and now represents 8% of the total pipeline versus just 3% in Q2. Over 60 AI Pods operate across clients globally, with 24 new subscription offerings closed last quarter alone.
Several of our top 10 clients have completed rigorous security and procurement approvals and are actively running AI Pods on the platform today. The pipeline is converting, the revenue is flowing, and we are just getting started.
Based on the record bookings we are reporting today, the accelerating AI Pods adoption across our client base, and the improving pipeline conversion trends, we have a clear line of sight to returning to positive year-over-year organic revenue growth by mid-2026. This is not a hope. It is supported by the bookings we have already signed and the pipeline that is converting. Our 100 Squared accounts drove 73% of total bookings this quarter, a clear reflection of the market’s shift toward high-value, long-term transformations. Underlying these record bookings is our reorganization around AI Studios by Industry. The record bookings we are reporting today are a direct reflection of that organizational transformation we did last year.
Several of our top clients have already moved past the pilot phase and are scaling AI Pods across their entire operations.
Let me share a few examples. We are working with EmployBridge, an Apollo-backed portfolio company, driving AI-led transformation through our AI Pod subscription model. After a successful pilot phase, EmployBridge decided for AI Pods as their core operating layer, accelerating delivery and driving rapid adoption across the business. We are also working with Banco Galicia, one of Latin America’s most prominent banks. After the pilot phase with our AI Pods, they performed an assessment to gauge the efficiency of the model among other vendors and similar teams. Our AI Pods ranked first in nearly every criterion, leading the institution to move to the decision to move to a scaled phase.
With YPF, Argentina’s century-old state oil company, with our human-supervised AI agents, we created a resource orchestration platform to help YPF better coordinate their complex supply chain, reaching over 5,000 providers. Our solution has already helped them reduce the requirement-to-contract process cycle by 30% to 40%, as well as boost the productivity of their supply buyers by up to 50%. Through the use of AI on Globant’s orchestrated platform, we are helping them with inventory optimization, enabling YPF’s managers to obtain the best possible products for the task at hand before ordering new inventory. We have a long-standing relationship with FIFA, helping them enrich their fan engagement channels in the digital age.
Through the deployment of AI Pods, we were able to move beyond traditional consulting services and achieve a major financial milestone for the organization, reducing costs by 20% without compromising the velocity or quality of our engineering output. Our initiative with LaLiga demonstrates how AI Pods rapidly transform an entire ecosystem. In just three months, we moved from concept to execution, deploying AI agents across critical functions like budget preparation, contract analysis, and audience data. The result is a massive leap in institutional productivity. By moving from traditional services to AI-native solutions, we are enabling LaLiga to ship new functionality at a speed previously deemed impossible.
We also applied our AI Pods model to our long-standing partnership with Santander to power their new digital payment platform, Santander Pay. By deploying a specialized product-definition AI agent within the pod, we cut the projected time for the app’s product definition in half. This AI-native approach drove a 50% increase in the client team’s overall productivity. In summary, it clearly demonstrated how we can accelerate the software development life cycle for one of the world’s leading financial institutions.
The professional services industry is being restructured right now. The companies that own the orchestration, the domain expertise, and the talent to supervise AI at scale define what comes next. We will be relentless in delivering value for our clients, our partners, and our shareholders. We will be disciplined in how we invest, and we are determined to build what we believe is the defining AI-native technology services company of the next decade. Globant S.A. has spent twenty years building the foundation for this moment. We have the platform, we have the people, we have the offering. And with that, I will hand it over to Diego Tartara, our CTO. Thank you very much.
Diego Tartara: Thank you, Martín. Hello, everyone. It’s great to be here. Following Martín’s perspective for the industry, we keep on firmly executing on our own reinvention and those of our clients, listening to customers, helping them understand their gaps, and curating tailored solutions that create real business value. This goes beyond cost savings and efficiencies into strategic areas such as increasing market share or improving customer satisfaction. To do this, Globant S.A. has overhauled our delivery model to ensure that the quality of our delivery is both technology-focused and client-centric. The teams that previously executed under the delivery and operational areas have now been brought under the technology umbrella.
This way our teams operate without siloed priorities and have more cohesion between offering solution quality and delivering results on time. The result has been tech-powered solutions for our clients that have a stronger operational backing.
I would like to share a few examples with you. We are working with a leading bank in North America that is launching a strategic enterprise-level modernization of its credit and debit card platform, moving from Gen 2 to Gen 3 accounts on AWS. Globant S.A. has been selected as the strategic partner to lead this migration, delivering a next-generation cloud blueprint that elevates performance, accelerates delivery, and positions this line of business for continuous innovation at scale. This project showcases our strength in helping financial institutions that are already in the cloud and at the forefront of innovation to continue pioneering the industry.
We have also been working with Trafilea, a global e-commerce group that builds and scales direct-to-consumer brands, needed to rapidly migrate new clients’ stores to their Trafilea platform. We built an AI-powered solution that automates the entire process, resulting in a 40 times faster migration. This not only saved Trafilea significant time and resources but also enabled faster onboarding of new customers. In the pharmaceutical industry, we are working with PharmaMar, world leader in the discovery, development, and commercialization of marine-derived anticancer drugs, to accelerate oncology research with AI.
Through Globant Enterprise AI, together we created a multi-agent AI system that delivers more than 90% accuracy in complex data retrieval and reduces time to insights up to 15-fold, helping scientists select high-potential drug candidates for clinical development in a fraction of the time previously required. This intelligent system integrates information from internal databases, scientific publications, and regulators such as the FDA and EMA, allowing PharmaMar’s teams to identify promising treatment combinations and make more informed, faster decisions. We also partnered with Therese to develop the foundations of the world’s first universal agentic protocol for tourism. AWS, Salesforce, Amadeus, Red Sea Global, and Riyadh Air, among others, are also part of the initiative.
We presented it at Davos in Switzerland to over 30 global CEOs. It is gaining strong traction as the standard for how AI delivers seamless, personalized traveler experiences at scale.
GUT had a landmark 2025. The agency closed the year with breakthrough campaigns for some of the world’s most high-profile brands, including a fully integrated 360-degree campaign, Renaissance of Snacking, that took over the Las Vegas Sphere and launched Cheetos and Doritos Simply Naked product line. GUT is a genuine competitive differentiator, and its creative momentum continues to grow. Strengthening our partnerships with leading AI model developers, enterprise platforms, and hyperscalers remains a key priority. Globant S.A. continues to present its strategic partnership with OpenAI to top clients in its key markets. Weeks ago, we hosted their first multi-industry event in Spain to discuss opportunities with over 60 current and potential clients in that region.
In December, AWS granted us competency certifications in both financial services and media and entertainment, further solidifying the autonomy and quality of solutions of our AI Studios. We also received the SAP Excellence Award 2025 Delivery Quality in Latin America, thereby becoming the most-certified SAP partner in the region. Our Salesforce ecosystem capabilities also expanded significantly, reaching expert-level implementation distinctions across MuleSoft Anypoint, Data Cloud, and AgentForce, along with top-tier partnership status across multiple Salesforce clouds.
Our teams will take the stage at the NVIDIA GTC in March to share how LaLiga is transforming its business through the most ambitious AI program in global sport, using agentic AI to build connected intelligence across operations, competition management, content, marketing, sporting performance, broadcast, and fan engagement. In such a disruptive year, we considered it especially important to share our perspective with the global business community. In Q4, we published industry reports on retail, games, and our annual tech trends outlook. You can download all of them at reports.globant.com. While AI continues to dominate many conversations, the real differentiator in 2026 will be execution. Companies that want to remain relevant must accelerate their transformation journeys.
Over the past year, we have evolved Globant S.A. to be the partner of choice for organizations ready to act and set the pace for the next decade. Thank you very much.
Arturo Langa: Hello, and good afternoon, everyone.
Juan Urthiague: I am pleased to discuss our fourth quarter results. We are encouraged by the stabilization of our top-line performance and a shift toward more optimistic client sentiment, which represents a meaningful improvement over the conversations we were having nine months ago. We closed the year with a solid quarter in terms of operational discipline, with revenues, operating margin, and free cash flow metrics above our initial estimates. In the fourth quarter, our revenue stood at $612,500,000, coming in above our guidance of $605,000,000. This represents a 4.7% year-over-year decline, including a positive FX tailwind of 180 basis points.
Now let’s turn to profitability. Our adjusted gross profit margin for the quarter was 37.6%. Gross margins were slightly impacted by the USD weakness relative to LATAM currencies, and to a lesser extent by statutory cost increases in two of our main delivery centers, Colombia and India. However, our adjusted operating margin remained at 15.5% for the quarter, flat sequentially. We successfully optimized our delivery pyramid and tightly managed our SG&A, allowing us to protect the bottom line while we work on accelerating our growth. The effective tax rate for the quarter stood at 23.5%, and our adjusted net income for the quarter was $68,900,000, representing an adjusted net income margin of 11.3%.
Adjusted diluted EPS was $1.54, consistent with our profitability targets.
I am particularly proud of our cash generation mechanics this quarter. During the fourth quarter, we generated $152,800,000 of free cash flow, marking the highest quarterly figure in our company’s history and achieving a free cash flow to adjusted net income ratio of 221.6% for the fourth quarter, or 355.3% on an IFRS basis. On a full-year basis, free cash flow reached a record $211,700,000, translating to 76.6% of adjusted net income and 203.6% on an IFRS basis. During the fourth quarter, we invested $50,000,000 to repurchase shares, as per the plan announced in October 2025. We plan to continue executing on the share repurchase program.
A significant improvement in our days sales outstanding, combined with working capital and CapEx efficiencies, helped drive an improvement in our liquidity. We ended the year with $250,300,000 in cash and short-term investments, an increase of nearly $83,300,000. With a modest total net debt position of $116,400,000, our balance sheet remains strong, providing us with the flexibility to continue our capital allocation strategy, including our share repurchase program.
Now let’s move to our outlook. Let’s start with our 2026 full-year guidance. Based on current market conditions, we are providing a revenue range of $2,460,000,000 to $2,510,000,000, implying 0.2% to 2.2% year-over-year revenue growth, with approximately 100 basis points of FX tailwind. We have set the lower end of our range as a prudent baseline. The upper end reflects the conversion trends we are already seeing in our pipeline and the accelerating adoption of AI Pods across our client base. In terms of profitability, we are expecting an adjusted operating margin to be between 14% to 15%. This range includes the impacts of USD weakness and statutory cost increases in Colombia and India.
We view the lower end as a stress test scenario, as it assumes a further appreciation of local currencies beyond today’s spot rates. The upper end contemplates a more positive currency environment and the benefits of our ongoing efforts in SG&A dilution and increased utilization. We continue to prioritize our operational discipline to offset these headwinds and drive toward the higher end of our margin target. The 2026 IFRS effective income tax rate is expected to be in the 21% to 23% range. Finally, we are guiding an adjusted diluted EPS of $6.10 to $6.50, assuming an average of 44,200,000 diluted shares. The lower end incorporates the conservative margin assumptions I mentioned earlier, specifically the potential for continued USD weakness.
At the same time, the upper end reflects the operating leverage we expect as we scale.
For Q1 2026, we expect revenues in the range of $598,000,000 to $604,000,000. This is an improvement relative to prior years where the Q1 decline was more significant. The Q1 year-over-year guidance implies at the midpoint a 300 basis points improvement relative to the Q4 year-over-year performance. For Q1, we expect our adjusted operating margins to be between 14% to 15%. Gross margins will be slightly impacted by the weakness of the USD, plus certain statutory cost increases in Colombia and India as mentioned before.
The IFRS effective income tax rate is expected to be in the 22% to 24% range, and adjusted diluted EPS for the first quarter is expected to be between $1.44 to $1.54, assuming an average of 43,700,000 diluted shares. To conclude, 2025 was a year of consolidation and evolution. We have diversified our revenue streams, shifted our go-to-market, streamlined our operations, and strengthened our financial foundation. We enter 2026 with a healthy pipeline, a more efficient delivery model which embeds AI in all our projects, and the financial strength to capture the opportunities ahead. Thank you for your continued support.
Arturo Langa: Thank you, Juan, and hi, everyone. 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 ask your question. I will also ask you to please limit your time to one question and one follow-up. With that in mind, we will take the first question from the line of Brian Bergen from TD Cowen. Brian, your line is open. Please go ahead.
Brian Bergen: Hey, guys. Thank you. So two questions. I will ask them both upfront here. First, just a growth clarification for the year on the upper end. I think you mentioned it assumes a solid pod demand trend that you have been seeing in 4Q. But does it also require some level of macro or broader demand improvement versus it being, like, the same macro backdrop? And then my second question is on the power model, on your Gen AI solutions, when we think about, you know, the clients that are utilizing these pods, is it pieces of work, broader engagements?
Can you kind of just talk about where it is being used specifically as well as then the net impact from, like, a transition from old to new? You if you can kind of get us there.
Juan Urthiague: Thank you, Brian. How are you? So as for the first part of the question, you know, the upper end of the guidance assumes that we will continue to, you know, perform very well with our pods, plus some improvement in the overall market. The midpoint is the most likely scenario as usual, where, you know, we see basically more or less more of the same. I mean, no big changes on the macro. No big changes on the business overall. And that is how we built the guidance for the year in terms of revenues. As for the second part, I will let the team here.
Brian Bergen: Yeah.
Martín Migoya: The what we are seeing in our seven out of our ten top customers, we are seeing that people are loving it. And when I say loving it, it is that people are really looking to change the model from hours or other types of engagement into this kind of output model, in which of course we charge the tokens, but always there is a business result attached to those things. So what is happening is that sometimes we are transitioning that work from, you know, our current kind of engagement to this new kind of engagement. In some of our customers, there are some small pilots that are, you know, starting to happen.
In some others, we are going now from pilots into scale, you know, without any kind of asking the middle because the results are really amazing, as I laid out in the examples I provided. So that is kind of changing the whole dynamic around the future of the company. Right? Now we are able to not just, our teams with new people, but now we can also, you know, be connected to everything that is happening on the AI space in a direct way. There is a new market that we are creating, which is called the AI-native technology services companies.
And those AI-native technology services companies must find a way to deliver their services, having agents that repeat certain processes that ensure that what is produced is enterprise class with the right security, with the right kind of characteristics for what they need to be produced, and then humans supervising those assets that are being created. And that transition is being twelve months ago. Indeed, nine months ago, this product did not exist. And now we are in a situation that we have in 2025 more than $20,000,000 in ARR, and now we are scaling big customers like the one I mentioned—like FIFA, like Santander, like LaLiga, like EmployBridge, and many others. So I feel that change is very healthy.
It positions us in a different place. And of course, everything is mounted on top of what we already have—more than 800 relationships with top-notch corporations, 28,000 people that are ready to supervise all kinds of products that we can produce with those stations, the right technology platform to be able to deliver those services, and a commercial model which is absolutely different from anything that we have seen before. And the best thing is not just a prediction, but also a real business. So we are extremely happy with that. I do not know if that answers your question.
Brian Bergen: Well, I guess it partly did. The aspect I am trying to get at is you mentioned certainly the gross margin is very high relative to what your historical is, right, in these pod structures. But trying to think about the revenue transition. So if you start from scratch, great—an engagement. But if you start on a client that had an existing engagement, what is that revenue like? Getting more productive. Is there a netting impact there?
Martín Migoya: On the revenue? I am absolutely happy with exchanging the revenue. I mean, we are kind of getting the teams that we had in that customer and transforming that into AI Pods with a very different revenue proposition and a different revenue value proposition. So it is a transition that is happening slowly, but it is happening. And sometimes there are new customers. Sometimes there are customers that are working with us on a fixed price that we are delivering now in this new way. So that transition is starting to happen, and we expect that transition to gain momentum as the year progresses. Yeah. And in certain customers, Brian, what you are going to get is that
Juan Urthiague: this additional productivity that we have can translate into helping them to reduce, you know, all the technical debt that you typically find in organizations. In other cases, you know, it may be in a specific project that you are able to maybe to price in a way that is more cost efficient. So there is going to be a lot of cases. Right? But the common factor here is that a lot of the technical debt that many of our customers have, you know, now we can be more productive, and we can offer them to do basically part of that additional work with our AI Pods as well.
Martín Migoya: K. Thank you, guys. Thank you.
Arturo Langa: Thank you, Brian. The next question comes from the line of Maggie Nolan from William Blair. Maggie, please go ahead.
Maggie Nolan: Hi. Thank you. I am hoping that you could comment on your expectations for Latin America in 2026, just particularly given some of the recent uncertainty that has resurfaced related to tariffs.
Juan Urthiague: Sure. So, you know, Latin America, as we remember, at the beginning of ’25, we faced some issues and the region a few quarters was showing negative growth. But then, towards the second half of the year, we started to recover, and we actually ended up in a very healthy manner in Latin America, the fastest region for the quarter. There are different, as you pointed out, you know, there are different situations in different countries. Argentina and Chile, which are two of our main operations, are doing very well. Brazil is okay. You know, we are basically performing in line with our expectations. And now, of course, we need to see what is going to happen in Mexico.
Now it is a little bit of an unknown at this point. But the main countries are performing well. I think that the recovery that we achieved in the second part of the year, when we look at which are the customers driving that, most of them are in Argentina. We do not see—we are not—we do not see any headwind coming from Latin America.
Maggie Nolan: Okay. Great. And then you sounded pretty optimistic about converting the pipeline as well, but I also caught in the prepared remarks that maybe you are expecting clients to look for larger-scale or longer-duration projects, which I would imagine would kind of change the pace of pipeline conversion. It would change the ramp-up of revenue over time. So can you help us understand how that is reflected in the guidance and maybe if it is different from historic—
Fernando Matzkin: Yeah, Maggie. So what we are seeing is, you know, shorter sales cycles in smaller deals, and the bigger deals still lagging just a little bit behind, slower than we would like to, in terms of closing and ramping up. But leveraging the amazing quarter we had in Q— the amazing quarter we had—expecting to start Q4 and also Q3, ramping up, onboarding, and converting to revenue very quickly in Q2 and in H2 even. So, you know, it is true that the clients are cautious, are taking time to make decisions when it comes to very large investments. But the robustness of the pipeline is still there. The quality of the deals is very solid.
The 100 Squared are performing very, very well, where vast majority of the bookings are coming from. As Martín said, 73% in Q4. I am pretty confident that, you know, this combination will allow us to, you know, move forward in a very confident way.
Juan Urthiague: Thank you. Thank you, Marie.
Martín Migoya: You, Maggie.
Arturo Langa: Thank you, Maggie. Next question comes from the line of Puneet Jain from JPMorgan. Puneet, please go ahead.
Puneet Jain: Hey. Thanks for taking my question. So with all the news flow over the last one or two months around evolution of agentic AI, what does that mean for IT services spend? Like, Martín, you mentioned that it is time for some of those AI investments to move into execution. Are you seeing, like, increased urgency among your clients to embrace agentic AI given, like, all the news flow over last one or two months?
Martín Migoya: Yeah. In the last few months, what we have seen is that, you know, companies are moving into action in that space. The avenues are how can I accelerate my technical debt? How can I replace some
Fernando Matzkin: not very deep software-as-a-service solutions? How can I automate my processes
Martín Migoya: using AI? How can I replace workflows of agentic AI processes that I had before? Of course, they must be supervised by humans.
Juan Urthiague: And I believe those three avenues, and the fourth avenue is that
Fernando Matzkin: how
Martín Migoya: can I improve my customer experience? You know, that research really bummed me when I read it, about the idea of consumer happiness. Yes. Consumer happiness about how interfaces and experiences are evolving is falling in the last four years in a row. So there is a big technical debt of $1,500,000,000,000 to $2,000,000,000,000, but also there is a big consumer experience debt. So another avenue of demand is saying, okay. How can I update all these interfaces to the next generation of interfaces? So all these avenues are creating, like, a lot of demand for AI.
I believe that the way to deliver those next-generation services, those AI-native services, must be absolutely different from what we did in the past. And, you know, imagine that we have each of these AI Pods, Puneet, are like a recipe or like a set of instructions, like a process, that we have been refining for years and years. It has different steps to create enterprise-ready, security-ready types of solutions. And what we are producing using those tools is really, you know, much, much more scalable than before and really much faster than before. So customers are seeing that now. If you just throw AI tools to people, you do not get those results.
And that is why it is so important to stress the point that this new industry is the way to create—it is the way to create the savings that you are expecting. Or, if you do not want savings, it is the way to create the productivity that you are expecting from these AI teams. So that is why I believe that the AI Pods are really catching up. It is a pretty simple way of understanding how to make those savings real as opposed to just keep on throwing licenses of AI tools to people to use them. I am not really sure that they will use them in the correct way.
And, again, it is much more different to orchestrate and to supervise a set of agents producing software, and that is real productivity, than just throwing AI tools to people. It is an order of magnitude of difference between the two things. And this is exactly what we are doing on our AI Pods. So, yeah, I am seeing momentum, and that will keep on growing. That will keep on growing.
Puneet Jain: Okay. No. No. Thanks for that. And then all this spending on AI, whether it is for code modernization, consumer experience, AI Pods, do you think, like, it will represent incremental spending on IT services, or will those budgets stem from cutting elsewhere—other parts of discretionary spend?
Martín Migoya: No. Look. I mean, I think humanity will create a 100x more software than before. Period. And that is only expansionary for us. So I do not see that this will—oh, well, no. Now we are happy with this small increment on the productivity and this small increment on the functionality of our product. But you hear and you listen—companies delivering much faster functionality than before live. I see. I read many examples during the last few weeks. So I believe that this is something that it will only keep on growing. And the more you can do, the more you consume, and that is a historical trend. Right?
In every single—so if we can produce more software faster, we will use more software. And we will expect more functionality, and we will expect more, you know, customers to be happy. So it is not a trend. I mean, sometimes what I see, you know, analysts—and when I see reports and when I read reports, I see that there is a kind of limited amount of scope. And what I am trying to—the message I am trying to convey to you is that there is no limit amount—there is no limited amount of scope. Just the technical debt—another industry of our size—just the technical debt. Right?
If you add on top of that the consumer experience debt, all the new techno—there is no way that it will be the same amount of software as before. It will be a 100x more software. So that will be translated into better solutions, with more platforms, with more AI Pods, with a stronger pipeline—well, all these things are building up. In my speech, I said for years, we have received—sorry. For almost two or three years now, the vast majority of the investments has gone into AI infrastructure that do not necessarily translate into demand in the professional services space.
Before, that same investment were going into better cloud that was yielding better software as a service, more implementation services, but that cycle now needs to come back. And that is why I made the point on the technical debt, on this consumer experience debt, because at some point, those things need to catch up. Otherwise, the consumer experience index will keep on going down for years and years and years, and it does not make any sense. In the moment we can have the better and the best experience for our customers, we are having a decline in customer satisfaction for interactions with companies. How can we explain that?
So one way or another, companies have been distracted, investing on AI, throwing AI to people. Now is the time to make it—to get it—serious.
Puneet Jain: Thank you.
Arturo Langa: Thank you, Puneet. Next question comes from the line of Brian Keane from Citi. Brian, please go ahead. Thank you. Thanks for taking the questions.
Brian Keane: I guess just thinking high level, you know, Globant S.A.’s always been a double-digit grower—organic grower—and this year was kind of a transition year, you know, grew 2% for the year and, obviously, down five for the fourth quarter. What can you point to, like, specifically happened this year that might not be recurring in years to come? Was it just certain client consolidation? Was it any AI pricing pressure that was priced into the model? Like, what exactly is the difference that happened this year that necessarily will not recur as we go forward?
Martín Migoya: You mean this year, 2025. Right?
Brian Keane: Yes. 2025 versus, yeah, going forward.
Martín Migoya: Yeah. I think 2025 was a year of, you know, uncertainty in general. Companies retracted budgets in many cases. I think it was a year in which macro uncertainties were extremely hard to overcome for many of our customers, and we suffered that. I think that right now, the situation is a little bit more clean in that aspect. So that increases my expectations of having a more normal year. That kind of compounding downwards on the revenue last year, we bottomed on that revenue, and we expect to come back to growth by the year over year, by the half of this year.
So the exit rate will come back to a pretty decent level of growth as we approach the end of this year. So what you see on the year over year is kind of a—okay. It was a year of reaccommodation, restructuring, you know, customer uncertainty, so on and so forth. The whole industry growing slower, which is kind of a killer, and now catching up, and we are starting to know, grow again, and towards the end of the year, the exit rate will be much healthier than what you are seeing now.
A note on the year over year that you are seeing; this already represents something that is stationary, right, that has to do with the moment of the year, and it represents a huge improvement from what we did last year at the same time.
Juan Urthiague: The first quarter compared to the first quarter of last year. So the beginning of this year is definitely better than the prior year. But the cadence of the quarter last year is somehow impacting, you know, the growth rate for 2026. When you look at 2026 exit rates, you know, they are more like mid-single digit. And if we keep on compounding, that should put us in a better place for ’27. Now, of course, there has been an industry situation. I mean, if you look at the vast majority of the players, they are all between 0% and 3%, 4%, 5%.
So there has been less growth in the sector, after massive investments, you know, in COVID times and around that time. There is a little bit of, you know, getting, you know, going past that period of massive investments. But the needs are there. The pipeline shows that the customers have been accumulating debt—technical debt—and that needs to start converting at some point. Of course, a bigger macro, a solid US economy, should help eventually. I think that we are coming out of two years of a lot of uncertainty globally, and that has not helped. But all in all, you know, in summary, I think that the fourth quarter shows a bottom in terms of year-over-year numbers.
Q1 already shows a better performance relative to Q4, and the expectation is for that to continue throughout the year.
Brian Keane: Yeah. My quick follow-up, Juan, is what do we—how do we model out headcount growth and revenue per head for this year? And does that model change at all as we are embracing more of the AI Pods?
Juan Urthiague: Yeah. Definitely yes. We are seeing that, you know, we can do slightly higher numbers. We can continue to grow our revenue per head with the same or even less headcount. The AI Pod model by definition requires less people. It is, you know, it is the AI Pods, which are agents supervised by some few people. So there is less need for talent. So I think that not just for Globant S.A., but in general, the sector will start to change a little bit that trajectory of headcount and revenue that we have seen in the past twenty years.
Definitely, the more we are able to penetrate our customers with AI Pods, the more the mix of AI Pods relative to the rest of the business increases. That should be a positive for revenue per head and also for margins.
Brian Keane: Got it. Thank you.
Juan Urthiague: You are welcome. Thank you, Brian.
Arturo Langa: The next question comes from the line of Arvind Ramnani from Truist Securities. Arvind, nice to have you again with us. Please go ahead. It appears that there is an issue on the line of Arvind, so we will jump to the next question. The next question comes from the line of Jim Schneider from Goldman Sachs. Jim, please go ahead.
Jim Schneider: Good afternoon. Thanks for taking my question. I was wondering if you could maybe address, on the AI Pod business, the path to get to the upper end of the range on the $100,000,000 in a run-rate ARR in that business. What is required for you to get there? How many more bookings do you need to put in? How much is supported by your existing pipeline of AI Pods business? And I guess maybe you could just kind of talk about the broad outlook or your confidence of kind of getting to the high end of that range.
Martín Migoya: Great question. Thank you, Jim. The higher part of that range could be achieved with not many big customers moving into that model. But let us see. That is why we are always being cautious here. We are extremely excited about the progress of that. Now we are seeing, you know, engagements of $20,000,000, $18,000,000, $15,000,000 being transitioned into this kind of engagement, which is extremely encouraging for us. So we expect to achieve those numbers. But I do not want to be—I mean, it is the first time we are guiding them. I do not expect to guide those numbers every quarter neither, but I am trying to be moderate here.
But I am quite optimistic about the possibilities of reaching to that top-line, top guidance that we did at the 2026.
Fernando Matzkin: You know, the behavior of the pipeline when it comes to AI Pods—if I can, if I can ask— to Martín’s, is very encouraging. We have seen a positive trend and a very accelerated growth. And on top of that, the, you know, the openness of our top customers to start piloting, right? Piloting and to start scaling up. You know, when you review the list of clients that are starting to ramp up in this new technology, it is really encouraging. So we are very confident, and, you know, we trust that we are going to be very close to the range that we guided in terms of AI Pods. Yes.
Jim Schneider: That is helpful color. Thank you. And then maybe you can talk a little bit about the profile of the gross margin for your overall business as we head through the year. Juan, I know you mentioned some issues relative to FX and regional costs that were sort of providing some pressure in Q4. Should we expect that we are sort of at a trough on gross margins and we can see acceleration throughout the year? Or how should we think about how that shapes up? Thank you.
Juan Urthiague: Thank you, Jim. So, I mean, yes, we have been impacted by the US dollar weakness. If you look at Colombian peso, Mexican peso, Chilean peso, Brazilian real—you know, most of the currencies where we operate in Latin America—have had significant appreciations throughout 2025. And that is what impacted, you know, the first—sorry, the last quarter of last year, and what is also impacting the beginning of this year. I think that the dollar is getting to a point where it is, you know, on an average, it is kind of from, you know, very low place relative to historical terms. So there has to be a little bit coming from there.
But also, more importantly, I think that we need to keep on focusing on not just, you know, looking at what is happening with the currencies, but moving the business towards AI Pods, because that is where, you know, productivity increases, that is where margins become higher, and the more we operate on those models, you know, the more efficient we can run them. So I think that pricing will be, you know, okay. I mean, it is not going to be a massive growth this year in terms of pricing for the general business.
But definitely there is an opportunity to increase our share of AI Pods and hence maintain or improve our gross margins as we scale that business. Thank you. You are welcome.
Arturo Langa: Thank you, Jim. The next question comes from the line of Jonathan Lee from Guggenheim. Jonathan, please go ahead.
Jonathan Lee: Great. Thanks for taking my questions. I wanted to ask, you know, what in your customer conversations in January and February gives you confidence around the conversion timelines that are contemplated in your outlook, particularly given some of the client caution you have called out and some of the conversion challenges you may have seen historically?
Fernando Matzkin: So, you know, we are seeing clients, you know, more open to resuming big-deals conversations than in the past. We are seeing also some of the volatility and the uncertainty, you know, lowering their levels in their conversations. And also, another interesting fact to consider, Jonathan, is that when we architect the numbers for 2026, we were able to bake in some very relevant deals that we closed in Q3 and Q4. Right? So some other deals that we are working on that, hopefully, will close before the end of Q1.
Some of that volatility going away and some of the, you know, clients being more open and those deals that we closed and we are in the process of onboarding and ramping up give us the confidence that, you know, the trajectory will be different.
Jonathan Lee: Great. That is encouraging to hear. And just as a follow-up, can you help decompose what you are expecting across your verticals over the course of the year? And are there any that you expect to decelerate versus accelerate relative to what you have seen?
Juan Urthiague: When we look at, you know, our different industries for this year and for last year, financial services had a good year, you know, growing approximately 13%. We have seen consumer, retail, and manufacturing performing very, very well. We continue to expect to see that behavior in that particular industry. So far, we have not seen the recovery of professional services, which has been kind of one of the drags during 2025. Technology will come back. We are starting to see some big deals shaping up with our tech customers, which was another sector that was not doing as we wanted last year. But definitely, when we look at the Q4, and finally healthcare—healthcare and gaming, right?
Those are the two that are big deals that have already been signed, that are in the process of ramping up, and that are part of the explanation of, you know, the sequential growth that we should see for the rest of the year. You know? So that is in general. I try to go to all the industries as we report them. So hopefully, it helps.
Jonathan Lee: Very helpful. Thanks for that, guys.
Juan Urthiague: You are welcome.
Arturo Langa: Thank you very much. The next question comes from the line of Sean Kennedy from Mizuho. Sean, please go ahead.
Sean Kennedy: Hi, everyone. Congrats on the bookings growth and momentum in the business. Great to see. So I was wondering about AI Pods and the conversations with your customers. Are you seeing their procurement teams becoming more comfortable with the AI Pods business model versus legacy?
Martín Migoya: That is a great question, Steve. Sure. Thank you. You know, this has been one of the most challenging things. However, as the thing gains momentum—as the idea gains momentum in the industry, on the analyst side, on you guys— the procurement teams are getting more relaxed. And also, I believe that the fact that we are talking something that is extremely solid and is, I would say, an order of magnitude more transparent than the traditional model—procurement love it. So whenever you can tie any asset that is being produced to the amount of tokens, and understand that correlation is what you are paying, it is much better than saying we consumed this amount of hours to do whatever.
So I think the AI Pods offering is extremely solid. Of course, a long road on convincing more people about this. The more you help us, the more we can do it. So we appreciate, you know, any kind of explanation on your reports. And analysts from Forrester, from IDC, from McKinsey, from Bain—they are already explaining this way of working. And 70% of the people—as I read on a report the other day—70 of the people that are buying technology are expecting a change in the way the engagements happen. And the answer to that change is either a monthly subscription amount of tokens, or some kind of combination there, but it must be on that seed code.
So procurement teams are responding quite well to that. Having said that, of course, it is always complicated, but it is not impossible. And the business is pushing very hard for that. And then there is also—when we started this, we tied the AI Pods with the capacity that it was equivalent to a team of X amount of people. Right? And procurement is used to that type of instrument. And we have become much more mature nowadays, and we are actually talking and correlating the consumption and the subscription with outcome to a certain—and we provide full transparency as well.
So there is also—you know, we have been getting a lot more mature in describing and showcasing how an AI Pod performs, and data has also relaxed a lot the procurement teams.
Sean Kennedy: Got it. And then, you know, as my follow-up, I think you stated that the high end of the guide embeds that the current conversion levels that you are seeing are consistent. So I was just wondering how it has been trending over the last few months. Thank you.
Juan Urthiague: You mean for AI Pods or in general?
Sean Kennedy: Just in general in total.
Juan Urthiague: You know, look, they are—I mean, we issued the guidance a couple of weeks ago, you know, so far, the conversion rates that we are seeing, some of the big deals that we closed last year that are ramping up, they make us comfortable, you know, to be at the midpoint of what we guided. The pipeline that we have plus, you know, the expectation of some improvement, you know, throughout the year somehow can take us to the upper end. But definitely, you know, with the current level of—or the current conversion rates, plus what we have already done during Q4 and the beginning of this year, that will take us—should take us—to the midpoint of our guidance.
But, again, that does not—the top—you know, the midpoint does not include any material improvement or material change on the overall environment.
Sean Kennedy: Got it. Great. Thank you.
Diego Tartara: Good luck.
Sean Kennedy: Luck in twenty six.
Maggie Nolan: Thanks. Thank you. Thanks.
Diego Tartara: Thank you.
Arturo Langa: Thank you, Sean. The next question comes from the line of Arvind Ramnani from Truist Securities. Arvind, please go ahead.
Arvind Ramnani: Thanks, Arturo, and good afternoon, everyone. Good set of results. Yeah. Lots of questions on AI, I will kind of hop on to that trend. Look. I mean, AI Pods generated about—I think it is $21,000,000 in ARR this quarter, you know, very impressive given it is still early. But it is still less than, like, 1% of your overall revenue. So still pretty small. But, you know, when you look at this model, you know, it is basically designed to do more work with tokens and less with humans. And, you know, as these AI Pods scale, how do you prevent them from cannibalizing your core seat-based revenue?
And then secondly, what is the internal modeling saying about the crossover point when you are generating more from, like, token-based revenue versus headcount-based revenue?
Martín Migoya: I am not in a position to prevent cannibalization. So I want that transformation to happen. And that puts us in the right side, and that means that as AI grows, we will keep on growing. And the way we are delivering our services with these AI Pods is by far very scalable.
It kind of distills years and years of experience, and the configuration files that we are using for each of those AI Pods and how the agents must run the process is really, you know, very, very impressive to see how those small recipes to achieve the assets that our customers have are really changing the way we are using AI and the current models that we have. Because we are baking into those configuration files all the experience that we have in the company. So this is the north for us on how to deliver technology and solutions moving forward.
And, of course, there will be customers that are comfortable with the hours and our current business we have with them, so on and so forth. And I am truly happy to keep on doing that. But we are extremely encouraging our customers to move to this new model because we believe in the benefits for transparency, for productivity, for the long-term relationship we have with them. And not necessarily AI Pods are, you know, cheaper. They are more productive. So we see a transition here in which we are going to be transitioning from one kind of basis to the other type of basis. So it will be like a rational migration rather than anything else.
So I do not know if that answers your question.
Juan Urthiague: And for the second part, Arvind—so as we migrate, you know, the business to AI Pods, and AI Pods start to gain share, you know, hopefully, by the end of the year, with the current forecast that we have in our, you know, internal projections, the numbers that Martín mentioned, you know, in terms of ARR for the end of the year. So definitely, you know, we went from zero to $20,000,000 run rate in just, you know, two quarters.
The model has been under a lot of evolution, you know, a lot of testing with customers, a lot of internal work on, you know, making sure that it creates a differentiator for Globant S.A. that, you know, makes us stronger relative to other players or other models that might be out there. And now, you know, it is starting to accelerate. As we discussed before, you know, when we look at which are the customers that are now getting on board, which is the size of some of the deals, you know? Now it is not just, you know, the—okay. Let us try with a small thing here.
But some customers are actually talking about $10,000,000, $15,000,000, $20,000,000 being migrated to the new model. So I think that we are starting to get that acceleration after a couple of quarters of understanding the customer, getting feedback. You know, we just launched this in Q3 last year. So it is only a few quarters that the model is around. It is already creating general interesting revenues, creating a lot of momentum with customers. I would say that by the end of the year, it will be more relevant, you know, relative to our overall pie. Definitely, it should be next year when, you know, the curves start to get closer. Yeah.
And also for your models and for everything that you are covering, guys, I think that we must acknowledge here that the industry is shifting and that this new industry of AI-native services is going to be, in essence, different from what it used to be. And AI-native services is leveraged on, of course, on knowledge, but also on repeatable processes and things that we have never had before. So in the same way Amazon created the cloud, you know, computing industry when they launched Amazon Web Services, of course, at our scale—I do not want to be operating with Amazon—but at our scale, we are kind of executing this vision of AI-native technology solutions.
And the way to model that and the way to do that is absolutely different from what it used to be. That is why I started talking about ARR, because it is kind of a recurring revenue that is not coupled to the amount of people. Right now, we are using, you know, people to supervise what the agents are producing, every kind of asset has a certain amount of time for those things to be supervised, so we can calculate how much people we need for that. But what we believe is that the revenue has nothing to do with the amount of people that we are putting there.
The revenue has to do with the amount of assets that we are creating and how enterprise-ready those assets are. Right? You can use CodeX, but, you know, you will not get all the discipline and the rigorous, you know, approach that enterprise software needs. So what we are creating here are processes to create assets that are enterprise-ready, security-ready, that they have scalability and the repeatability and maintainability that you need to have moving forward. So it is really a different way of understanding the industry itself. There is a $2,000,000,000,000 industry that must change to a new model. And this is the very beginning of that.
Arvind Ramnani: Yeah. Perfect. That is incredibly helpful. Just a quick follow-up here. Just a quick follow-up. In terms of, like, the tokens—right, the tokens cost you a particular amount of money, and then you are charging your customers. What are the margins you are making there—higher or lower than the company?
Martín Migoya: As we reported, margins are between 45% to 60% depending on the AI Pod, and depending on the customer and depending on the things that we need to create. We expect that margin, as we progress with time, to increase as we get more efficient with technology and as we get more efficient supervising, and the technology for supervision gets improved, too. So this is the kind of model we have in mind. As we are able to supervise more assets with the same amount of people, with less people, or better technology, we will need less supervision, and we want to increase our margins. And that is a virtuous cycle that happens here. But not just that.
Every single conversation and every single token is being stored on our Enterprise AI platform, and those tokens can be used to improve the processes and to retain corporate sovereignty of the processes of the company. So this is kind of an explosion of productivity, and it will reshape the whole industry. And not just for software development— it will happen also for process operation. Today, we have AI Pods— pulse—AI Pod Software— and we have another AI Pods called AI Pods Operations. And those AI Pods of operations, they get that kind of doing things for operating certain processes of companies, and we charge it also per consumption.
So it is a radically different way of understanding how professional services and how services will be rendered moving forward. There is a lot of value to add for companies like Globant S.A., and there is a lot of, you know, change of mindset that is needed to understand this new industry. I could be talking forever about this, but thanks.
Arvind Ramnani: Very helpful. Thank you so much. Thank you. Thank you so much.
Arturo Langa: Thank you very much, Arvind. Unfortunately, that is all the time we have for our question and answer section for today. So with that, I will now ask Martín to provide some closing remarks. The line is open.
Martín Migoya: Thank you so much, Arturo, and thank you, every one of you, for your support, for your help, and for being here today. Bye-bye. See you next week.
Jim Schneider: Talk to you. Bye. See you. Thank you.
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Globant (GLOB) Q4 2025 Earnings Call Transcript was originally published by The Motley Fool
