Globant (GLOB) Q2 2026 Earnings Call Transcript

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DATE

Thursday, Aug. 13, 2026 at 4:30 p.m. ET

CALL PARTICIPANTS

• Investor Relations Officer - Arturo Langa
• Chief Executive Officer - Martín Migoya
• Chief Technology Officer - Diego Tartara
• Chief Financial Officer - Juan Urthiague
• Chief Revenue Officer - Fernando Matzkin

TAKEAWAYS

• Revenue -- $614.4 million, representing 1.2% sequential growth while remaining generally unchanged from the prior year quarter.
• Glob.AI ARR -- $52.8 million as of June 2026, growing 61% from the $32.8 million reported in March 2026 due to accelerated adoption of the AI native delivery model.
• Glob.AI ARR Guidance -- $110 million or more by the end of 2026, reflecting management's expectation for the continued scaling of output-based revenue.
• Glob.AI Pipeline -- $436.8 million, up from $352 million in the first quarter of 2026 as demand for AI modernization projects increases.
• Revenue Per Head -- $95,800 on a run rate basis, increasing 9.7% year over year as the company utilizes AI integration to deliver more value per professional.
• Top 50 Client Growth -- 6.9% year over year, driven by higher penetration of AI Pods and the Glob.AI platform within large accounts.
• Top 20 Client Growth -- 6.6% year over year, with 16 out of the top 20 relationships showing positive growth trends.
• AI Pod Adoption -- 45 clients, representing 45% of the top 20 accounts as management steers customers toward output-based pricing.
• Adjusted Gross Margin -- 36.5%, down from 38.1% in the prior year quarter due to dollar weakness in delivery centers like Colombia and lower utilization rates.
• Adjusted Operating Margin -- 13.2% for the second quarter, which was below the guided range primarily due to currency headwinds and margin pressure.
• Adjusted Net Income -- $60.3 million, resulting in an adjusted net income margin of 9.8%.
• Adjusted Diluted EPS -- $1.40 for the quarter, compared to $1.53 in the second quarter of 2025.
• Fiscal Year 2026 Revenue Guidance -- $2.428 billion to $2.462 billion, representing a downward revision from the previous range of $2.462 billion to $2.508 billion.
• Fiscal Year 2026 Adjusted Diluted EPS Guidance -- $5.75 to $6.15, assuming 43.6 million average diluted shares outstanding.
• Third Quarter 2026 Revenue Guidance -- $607 million to $615 million, including a positive foreign exchange impact of 25 basis points.
• Business Optimization Charge -- $32.3 million in the second quarter, reflecting costs for workforce alignment and office footprint consolidation.
• Latin America Revenue Growth -- 5.9% year over year, led by Argentina and other regional delivery centers.
• Europe Revenue Growth -- 6.8% year over year, representing 20.9% of total revenue.
• New Markets Revenue -- 17.7% decrease year over year, acting as a 115 basis point drag on total revenue growth due to project delays in the Middle East.
• Free Cash Flow -- $12.6 million for the quarter, compared to negative $2.9 million in the second quarter of 2025.
• First Half Free Cash Flow -- $48.7 million, which management stated was the highest first half of cash generation on record.
• Cash and Short-Term Investments -- $168.8 million as of June 30, 2026, providing a liquid balance sheet position.
• Share Repurchase Authorization -- $125 million available as of June 30, 2026, after the company invested $25 million during the second quarter.
• Total Employees -- 27,411 at quarter end, down from 30,084 in the prior year quarter as the company rightsizes its workforce.
• Data and AI Studio Revenue -- Close to 11% of total sales, growing approximately 35% year over year.

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RISKS

• Urthiague stated, "Due to the ongoing conflict, this specific geography suffered unexpected project delays over the course of the second quarter," referring to the 17.7% revenue decline in the new markets segment.
• Urthiague warned that some travel clients have slowed the pace of their transformation programs to protect their own profitability, which management attributed to volatile oil prices.
• CFO Urthiague noted that currency appreciation in Latin America, particularly in Colombia, created a significant headwind, impacting margins by approximately 4 percentage points over the last two years.
• Migoya mentioned that decision cycles in North America have become more protracted, contributing to a 2.4% contraction in revenue from that region.

SUMMARY

Management discussed the company's transition toward an AI-native delivery model, emphasizing the shift from headcount-based pricing to output-based and consumption-based models. The company launched the Glob.AI platform to provide enterprises with self-service access to AI Pods, aiming to automate complex technological workflows through specialized agents. Strategic initiatives included new leadership appointments and expanding alliances with major artificial intelligence providers to integrate advanced models into client projects. The company also initiated a business optimization program to align its cost structure with current market conditions and currency fluctuations in key delivery centers.

• The company appointed Sarab Narang as CEO of Glob.AI, who previously led the commercialization of artificial intelligence business at ServiceNow and AWS.
• Migoya stated, "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."
• The company established a multi-year alliance with Anthropic, becoming a preferred services partner in the Claude Partner Network with several AI Pods already in production.
• Diego Tartara stated, "This leverage enables us to decouple enhancements in output, velocity, and delivery from headcount growth," highlighting the operational shift to asset-based engineering.
• Management reported that the platform provides token sovereignty, ensuring client data is locked within a dedicated vault and not used to train third-party models.
• The company productized previous custom successes, such as accelerating drug discovery at Pharma Mar and compressing supply chain cycles at YPF, into a standard catalog for all clients.
• Partnerships with Vercel and OpenAI have been expanded, allowing clients to modernize enterprise digital products and ship AI-built applications natively in one click.

INDUSTRY GLOSSARY

• AI Pods: Specialized, AI-native service units that deliver work through agentic workflows and are priced based on output or consumption.
• Glob.AI: A self-service platform where enterprises can find and deploy AI Pods to automate business and technological processes.
• ARR (Annual Recurring Revenue): A metric used to measure the predictable and recurring portion of revenue from subscription or consumption-based models.
• Globers: The internal term used by Globant to refer to its employees and professionals.
• Agentic Workflows: Automated processes run by AI agents that can perform tasks, coordinate systems, and execute complex business logic autonomously.
• 100-Squared Strategy: A corporate initiative focused on growing the company's top 100 accounts to over $100 million in annual revenue each.
• Token Sovereignty: A security framework ensuring that data used in large language model transactions remains private to the client and is not shared with model providers.

Full Conference Call Transcript

Arturo Langa: Welcome to Globant's second quarter 2026 earnings conference call. I am Arturo Langa, Investor Relations Officer at Globant. All participants on this call will be in listen-only mode. After today's presentation, there will be an opportunity to ask questions. Please note this event is being recorded and streamed live on YouTube. By now, you should have received a copy of the earnings release. If you have not, a copy is available on our website, investors.globant.com. We will begin with remarks by our Chief Executive Officer, Martín Migoya, our Chief Technology Officer, Diego Tartara, and our Chief Financial Officer, Juan Urthiague, followed by a Q&A where they will be joined by our Chief Revenue Officer, Fernando Matzkin.

Before we begin, I would like to remind you that some of the comments on our call today may be deemed forward-looking statements. This includes our business and financial outlook and the answers to some of your questions. Such statements are subject to the risks and uncertainties as described in the company's earnings release and other filings with the SEC. Please note that we follow IFRS accounting rules in our financial statements. During our call today, we will report non-IFRS or adjusted measures, which is how we track performance internally and the easiest way to compare Globant to our peers in the industry.

You will find a reconciliation of IFRS and non-IFRS measures at the end of the press release we published on our investor relations website announcing this quarter's results. I will now turn the call over to Martín Migoya.

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, a way of pricing it, which is already starting to compound. For more than 20 years, we have engineered the digital reinvention of the world's leading organizations, building the software, products, and platforms that run their businesses, delivered by dedicated high-performing teams and priced through on fixed-scope engagements or time and materials. That work remains the backbone of Globant.

One year ago, I introduced you to AI Pods, a new AI-native revenue stream built on that foundation, but priced on the actual output and value we deliver or on consumption rather than on the hours we bill. As AI Pods deliver more work at higher margin for a similar price, our top line can understate the progress underneath it. As this grows, it will be relevant to assess annual recurring revenue per head, AI Pod margins, and client penetration alongside the total revenue line. Before I go further, let me be precise about two names you will hear all call. Glob.AI is the platform we opened to the market last week.

AI Pods are the service units that live on it, run by AI agent workflows, and supervised by our experts. The revenue they create, I will call 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. 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 four times the size of the IT services market that Globant has been evaluated in. We are taking this deliberate decision to respect our current market 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 onto 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 +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 providing their depth. Since that top 50 growth is a sign they understand these clients' industries well.

Our core business is acting as the distribution engine that carries 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 toward AI, data, cloud, and integration work. Having said all this, we are operating in a tougher environment this quarter. Geopolitical pressure in our new markets, volatile oil prices weighing on travel, and longer decision cycles in North America. Juan will take you through a revised outlook for the full year. Last week, we launched Glob.AI, and with that, we are opening this same model to any enterprise through a single self-service platform.

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 name partners, secure code review with Anthropic, digital twin engineering on NVIDIA Omniverse, prototype to product with Vercel, and enterprise integration with Salesforce and MuleSoft. The platform runs across the broader model ecosystem as well, Anthropic, OpenAI, Google, Azure, AWS, NVIDIA, Meta, among others. I am glad to announce that 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 multi-year alliance with Anthropic, becoming a preferred services partner in the Claude Partner Network. Since signing, we have moved quickly. Several Claude-powered AI Pods are already in production. They 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. 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. This is just the start.

We are 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 longstanding client Mercado Libre, the first agency-client partnership to achieve this milestone at the festival.

GUT also launched new work for Google Chrome and Ray-Ban Meta, created the world's first clay bar for Stella Artois at Roland-Garros during the French Open, and introduced RIMOWA's For a Lifetime of Lives platform, celebrating craftsmanship through stories of longevity and evolution. We are building a meaningfully different services business deliberately with a growing base of revenue underneath it, and with a number I have asked you to hold us to every quarter. This next chapter also means disciplined choices today, including decisions on our cost base to fund the transition and protect our margins. I do not take those lightly, and I am grateful to our teams for the resolve they are showing.

Thank you to our clients, our partners, and our Globers around the world, building this alongside us every day. With that, Diego will show you the machine underneath. Thank you.

Diego Tartara: Thank you, Martín, and hello, everyone. Martín just laid out our strategic vision for Glob.AI and how it fundamentally transforms the way clients acquire our services. My focus today is on the underlying engine, the technology architecture, the operational mechanics, and the first-mover advantage that makes this delivery model perform at scale. The legacy professional services model trades human hours or custom solutions, forcing engineers to solve the same foundational problems repeatedly. Glob.AI breaks that cycle by operating as an asset-based engine. Within the platform, we have codified over two decades of enterprise engineering and industry domain knowledge into curated battle-tested playbooks. These are deterministic, documented agentic workflows designed for production-grade reliability.

Because these agentic assets are modular and validated, we achieve extraordinary cross-industry compounding value. An IT root cause analysis workflow built for an airline client, for example, can be replatformed into a pharma supply chain or media distribution pipeline in a matter of weeks rather than months. AI Pods serve as our direct vehicle for monetizing this compounding IP, taking clients from a natural language challenge to production-ready deployment without the traditional friction. As we set out in introducing Glob.AI, raw LLM prompting produces significant token waste, hallucinated logic, and expensive retry loops that includes additional time for human supervision. Glob.AI solves this through structured deterministic process optimization.

Before an AI agent executes work, our platform automates context assembly, architecture mapping, and data preparation, enforcing automated quality gates at every step. By optimizing the orchestration layer, we ensure that every token consumed yields verifiable production-ready output. At the same time, enterprise adoption hinges on control. Glob.AI's architecture routes intelligently across more than 140 LLMs, providing complete model independence so clients are never locked into a single provider. Crucially, every transaction is locked within the client's dedicated token vault. This guarantees absolute token sovereignty. No client data is ever exposed or used to train third-party models, allowing organizations to compound their own institutional intelligence safely over time.

We are seeing the impact of this platform model directly in our operational performance. By integrating specialized AI agents into continuous delivery workflows supervised by our experts, we are restructuring the software engineering life cycle. This leverage enables us to decouple enhancements in output, velocity, and delivery from headcount growth. Furthermore, this enables us to layer on nonlinear and recurring revenue to the mix with structurally better unit economics that will, over time, transform the business. This is the structural signature of a business moving up the value chain. Now, I would rather show than tell. Let me take you on a short Glob.AI tour. It starts simply.

Once you have selected the plan and the AI Pods from the catalog, or through chat with the Glob.AI agent, you would be able to access the projects module. From that moment on, you are ready to begin. Glob.AI works at two levels: your organization and your projects. Your organization is the foundation where you configure the tools and settings that apply to all your projects. Your projects are where the work happens, each with its own goals and one or more AI Pods running simultaneously at different prices, which always include not only the tokens consumed, but also the human supervision required for the project. Let's start at the organization level.

The first thing your organization needs is context, and you can give it directly from the tools your organization already uses. Connect your GitHub to read, write, and open pull requests directly on your repositories. Google Workspace to pull signals from Gmail, Calendar, and Drive. Atlassian to sync with your Jira tickets and Confluence pages. You can also provide context through a document or a description. Once you're inside your project, everything is in one place. At the top, you see your project at a glance, resuming the totals of your project, how many AI Pods are active, your goals, your token consumption, your current spend with your limit, and the start button that sets everything in motion.

Right below, your customized dashboard also gives you four key metrics. Selecting the period, you can see the spend, tokens used, tasks total, and artifacts produced. Beneath that, a quick summary, your active goals, the units of work in progress always visible at a glance. And finally, your AI Pod fleet, the specialized teams running your delivery. On the right is your communication channel, the Glob.AI agent. Just describe what you need in plain language or start with one of the suggestions below. You can also mention your forward deployed engineer, the Globant expert assigned to your project and your direct line to the human overseeing every step. Whenever you need to communicate, you're one message away from your expert.

Goals are the units of work your AI Pods will execute, and you create them by simply telling the agent what you want to achieve. You can connect a repository and let the agent analyze your code base directly. You can describe your needs in plain language, or you can attach a document and let the agent extract the requirements from there. Either way, the agent proposes the goals. You review them, you approve them, and if something needs to change, you tell it directly or bring in your expert, always available in the chat with full context on your project. When you're ready, you say start.

That single action locks in your project and hands it to the forward deployed engineer who supervises every step of what comes next. Artifacts is your shared workspace for project files. Here you'll find every document, spec, ABR, or implementation summary produced by your AI Pods ready to preview with a single click. You and your FDE can all upload files here, keeping everything in one place. Some artifacts will also be delivered directly to your GitHub repository as commits or pull requests. Clicking on your name gives you access to your notification settings. You choose how Glob.AI reaches you: email, WhatsApp, or Telegram, and you decide which events trigger each channel.

Approvals waiting on you, actions required, new deliverables, or run updates. Clicking on your organization gives you a complete control panel with four tabs: overview with all the general information; members, where you manage your team; billing, your full financial picture; and last, usage, a breakdown of consumption for the current cycle. The capability behind Glob.AI is not built on theory. It reflects what we have been proving on the ground. In our previous calls, we shared how early AI Pods deployments drove milestone efficiency gains, whether accelerating drug discovery research at PharmaMar 15-fold, compressing supply chain contract cycles by 40% at YPF, or reducing legacy migration timelines from 14 months down to 2.

What makes Glob.AI so significant today is that those custom high-impact successes are no longer bespoke projects. We have productized those learnings into our standard catalog. Backed by our deep co-engineering alliances with hyperscalers and model providers, Glob.AI turns those proven enterprise outcomes into an on-demand, repeatable capability accessible to every client from day one. We have built the underlying platform, secured the governance framework, and proven the economics at scale. Everything I just described is what compounds behind one number, Glob.AI ARR. We look forward to driving this next chapter together. Thank you very much.

Juan Urthiague: 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 remained the highlight. Top 50 clients grew 6.9% on a year-over-year basis, top 20 at 6.6%, and top 10 at 4.4%, all well above company average in line with our 100- squared strategy.

16 out of our top 20 relationships are showing positive year-over-year growth, and we continue to scale recently signed large deals. Adjusted gross margin was 36.5%, slightly down as dollar 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, 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. 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. 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%-23% range. Adjusted diluted EPS is expected to be between $1.43 and $1.53 per share, assuming an average of 43.2 million diluted shares outstanding.

With respect to the full year, we are revising our revenue guidance to a range of $2,428,000,000-$2,462,000,000 from $2,462,000,000-$2,508,000,000 previously. In terms of profitability, we now expect our adjusted operating margin for the full year to be between 13.5% and 14.5%, driven by the increasing dollar weakness. The IFRS effective income tax rate is expected in the 21%-23% range. We now expect adjusted diluted EPS of $5.75-$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.

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 then ask your question. Please mute your line after the question is done. I will also ask you please to limit yourself to one question and one follow-up. Thank you very much. With that in mind, we will take the first question from the line of Bryan Bergin from TD Cowen. Bryan, please go ahead. Your line is open.

Bryan Bergin: All right. Thank you. Hi, guys. I wanted to ask on the business transition. You are showing strong sequential growth in Pods, now targeting $110 million. I think that is up from $60 million to $100 million before. Based on what you are 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 re-acceleration in the overall company trajectory? I guess as it relates to your revised revenue outlook for 2026, 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?

Martín Migoya: Okay, let me tackle the first one. That is a very important question. I think that the transition to the new model is something that we are doing it in a deliberate way, and it is something that we will keep on executing quarter-over-quarter. Honestly, the demand that we have seen and the acceptance of the model and the positive signs we are seeing from the market are very encouraging. Still is a small percentage, but we think that we will keep on accelerating this. If you ask me, if the revenue from the new markets would not be affected, we would be already in the positive growth side without the need of reviewing the whole forecast for the year.

So I think overall, it is a very positive moment, and it will accelerate a lot the growth. Probably by the end of next year, we will see a pretty strong effect of that kicking in. I cannot say it right now. I do not think it is a piece of information we can describe right now in a very exact way. So with that caveat, I would leave the second part to Juan.

Juan Urthiague: Thank you. Hi, Bryan. The guidance for the year stands now at $2,445 at the midpoint. That is -0.4%. The FX tailwind there is about 70 basis points. So organic constant currency, you would be talking about 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 all this news from Saudi reducing budgets, delaying projects, and things like that. As you know, it is a market that we have been expanding quite nicely over the last few years, and we will continue to do so.

We see a lot of deals that are just getting postponed or getting slowed down, but not canceled at all. We keep on having very interesting conversations, so we are confident about the recovery of that market in the near future. 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 little bit of a mix between some assumptions we are doing on certain migrations, plus the overall business environment and where we are right now. Again, I think, and it is important to also look at how the new business and the part of the business that we are pushing very hard is evolving. Yes, it is still small, but when you start to compound at 40%, 50% quarter-over-quarter rates very quick, it starts to become more relevant. As you pointed out, we have been talking about $60 million-$100 million for this year.

Now we are already over $52 million, and with very good visibility of the second half of the year because we are passing through the first initial stage of trying and 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. What that means is that 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.

Bryan Bergin: Okay. Thank you for all that detail. Just to follow up here on the optimization you took. Could you just talk about the savings you anticipate from those programs?

Juan Urthiague: Yeah. 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 that we have in front of us, and also with the skill sets that are required with the 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, again, our delivery centers. The impact in the second quarter of that was roughly $32 million. We are expecting around $20 million-$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 pool or part of that talent in the talent pool and without the possibility of allocating them to new projects maybe. Second, it is going to help us offset a massive FX headwind that we are seeing because of the 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%, and that is a massive impact on our numbers. We are going to offset that. We are going to invest more.

As we were discussing in the call, we just announced a new CEO for our AI Pod, sorry, Glob.AI business, and we have to invest in that business because we believe that is the future of the company. We will be using the money for that, and that will save us cost that we would have had otherwise.

Bryan Bergin: Okay. Thank you.

Juan Urthiague: You are welcome.

Martín Migoya: Welcome.

Arturo Langa: Thank you very much, Bryan. The next question comes from the line of Tien-tsin Huang from JPMorgan. Tien-tsin, please go ahead.

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

Martín Migoya: Well, hi, Tien-tsin. How are you?

Tien-tsin Huang: I'm okay.

Martín Migoya: The whole program has different reasons, right?

Tien-tsin Huang: Yeah.

Martín Migoya: You see that on one side, we are migrating to this new model that requires a certain type of forward deployed engineers and certain type of AI engineers that are slightly different from what we used to have. There's a transition on the talent that we are seeing that is causing one of the reasons of the optimization. Also, we are seeing a transition on the demand of the traditional business. The demand of the traditional business is moving away from web UI testing into more data and more cloud and implementation. That transition also created some demand of new profiles that we didn't have that much before, and we needed to start training and retraining. The effect has that.

Also, it has been the impact of several programs and things to run more efficiently the company. As you saw, the increase on the revenue per head has a big message inside that, as we are becoming more efficient to deliver our revenue. It's an effort that has many different components inside of it. I don't know, Juan, if you want to add anything to that.

Juan Urthiague: No, I think, it is basically a kind of a reshuffle, not a reshuffle and maybe reskilling our workforce to the new type of demand, also to the new type of services that we are seeing that we are providing to our customers through the AI Pods. You definitely require different skill set. Also, we need to protect our margins. We need to make sure that we offset all the U.S. dollar weakness that impacts our Latin America business. Also, make space for the investments that will be required in Glob.AI.

Martín Migoya: How much was the FX impact in the last year?

Juan Urthiague: If we were to look at all the currencies in Latin America for the last.

Martín Migoya: 5%.

Juan Urthiague: Year and a half to two years, we are talking an overall impact just from the FX of about 4 percentage points. Now, of course, we have been able to increase our revenue per head. That help us offset part of that, and we also made some efficiencies last year that also helped us. But the magnitude of the headwind that we have suffered in Latin America has been very, very significant and impacting our margins.

Tien-tsin Huang: Yeah. I appreciate that. It is out of your control, so it is good to get in front of it. Just quickly, thinking about the new model and Glob.AI, enjoyed hearing from everybody and Diego. Looking back over Globant's history, I always think of the 50 squared account AI and how this ramps. Can you just give us an idea of what the revenue per could be from a client perspective as you penetrate your top 10, 20, 30, as you learn? Is there any analogy or parallel that we can draw back to how Globant grew under the prior model and assign that to the new model?

Just trying to better understand how this can ramp beyond some of the metrics you gave for this year.

Martín Migoya: Yeah. We are seeing very good traction on the 100-squared program. Indeed, that group of customers grew, the top 50 grew like 7% or something like that, which is extremely encouraging, right? It is where we are delivering these new things and the first experiences during these last nine months of execution or a year of execution of our AI Pods. So this is very encouraging by itself. In terms of amount of revenue, let us say that we maintain the gross margin as we have a much higher gross margin on the AI Pods, and we have maybe, let us say $1.2-$1 on the new service line. But not in all accounts.

It depends a lot on every single account. Remember, it cannot be traced back to the original model because the original model was headcount.

Tien-tsin Huang: Yeah.

Martín Migoya: Either fixed price or time and materials. This new model carries tokens plus token supervision in a single price, either per million token or per output. That creates a totally whole different math, right? This new math is a place in which you can optimize margins, you can improve supervision, you can do it more with less, or maybe, in some accounts, we need to put more supervision for certain specific projects, but it is managed in a totally different manner from before. That is why I like to say that this transition is not just like a playground that we started. I think it is the future of a company.

Moving to output, to consumption, to value from a totally different model of before. I am not saying that this old model or traditional model will disappear. But yes, I am saying this is a transition. Step by step, you are seeing us gaining momentum on this new way of delivering, on this new way of charging our customers that is absolutely decoupled from the traditional way. So making a parallel between those two things sometimes becomes difficult. Honestly, we have not much story. We have one year implementing this.

Tien-tsin Huang: Yeah.

Martín Migoya: We already have some signals. It is enough for us to put a pricing on those million tokens or on these outputs that we are charging, but we need to see many more things happening to be able to take the kind of conclusion you want. What I can say is, listen, we are evolving in a very nice way, growing in a very nice way. It surpasses my own expectations. I said it, $60 million-$100 million, now it is at least $110 million. I think it will keep on compounding because it makes a lot of sense for our customers.

Many of the new projects that I referred to in past earnings calls, about changing interfaces, automating processes, making sense out of massive amount of information, all these things requires a totally different way of delivering. This new service is not just for those 100-squared customers. This is a beautiful part because this is also to serve other segments which may be smaller, and we are still learning how to do it. We will keep on expanding on this as we progress. I do not know, Juan or Fer, if you want to add something.

Fernando Matzkin: No, the last thing you said, it is quite important. With Glob.AI, we are also thinking of how to widen our base of clients, how to serve clients with different kind of scale that we couldn't do before in different segment to accelerate our revenues. We have proven the success of the 100-squared model, like you said. It is very well reflected on the growth of at least our 50 top customers. Now the challenge is sustaining the growth of a segment of client that has a different dynamic, right? Needs to be served differently.

We believe that with Glob.AI, we also have a way to reach a much wider base of customers with a simpler way and a more sustainable way.

Tien-tsin Huang: Mm-hmm. Yeah. No, thank you for the thoughts. Nice to see you all.

Juan Urthiague: Thank you.

Martín Migoya: Thank you, Tien-tsin.

Arturo Langa: Thank you, Tien-tsin. Nice to see you. The next question comes on the line of Maggie Nolan from William Blair. Maggie, please go ahead.

Maggie Nolan: Hi. Thank you. Nice to see you. Maybe I wanted to build on one of those past questions. Let's see. Okay. Yeah. You had talked about in the comments that you thought that Glob.AI and having a focus on outcomes was opening up new budgets to you that you hadn't had access to before. So maybe can you elaborate on where that growth is coming from? Who are the new buyers? Is that growing, or do you view total addressable market as growing, and kind of what's changing your ability maybe to go deeper in clients?

Martín Migoya: Well, look, that paragraph that you mentioned, refers to the following. For years, as I described at the very beginning of the earnings call, we have been creating experiences and software products and we have been very close to using technology to create experiences that engage in an emotional way with our customers. That is the core, that's what define us. Now, as AI came, there are many other places in which, it's not just creating those experience, but also operating part of those backends and processes that before were not an opportunity for us.

As I described that the process automation and the change on the landscape of how to use AI for pretty much everything, including automating every process, creating new org charts that reflects that automation, we believe that our AI Pods, which are now the AI Pod software that what you see in Glob.AI if you go, will evolve into operations and will include that same concept of having an agent operating something for you and having humans being able to analyze the edge cases and charging in a way that is per unit of that specific business case. Let's say travel, well, it will be per trip or know your customer, per know your customer.

That agentic work that this Glob.AI concept is opening up is much larger than the software development life cycle that we have right now on the Glob.AI. What I'm saying is, with this new idea, with this new concept, with this new definition of how services and AI native services will be rendered, we are able to tackle much more than just the original software development life cycle, and we can expand our presence into AI Pods for operations, right? That's what I'm referring about when tackling new budgets.

Also there's a market share game too, because when you present this new way of doing things and new way of charging things, for every dollar you sell on this new model, you are able to capture maybe another dollar, right, of that same budget because you are doing things that other vendors were doing. We're extremely excited about those two things happening at the same time, expanding into other places and capturing more dollars for that work that we used to do. In many cases, it was the reason why we win and we won. I have many examples, several examples. Otherwise, we would have lost.

I think that this new definition of how to do things and how to charge for things is a really new approach to our customers. We launched it last Thursday, and the impact, the amount of people that call us, the amount of people that are interested in understanding more about how this Glob.AI model works, in essence, it's a huge effort and a huge intellectual effort in creating something that didn't exist, and we are the first providing it. I am extremely proud about the whole team that is developing this.

I am extremely proud that we are being able to take it to the market, and we are being able to convince our customers and not just a couple of cases now. It will be more than $100 million in ARR. That is an absolute success, and I think that this is something that you will see us insisting more and more on deliberately entering to asking our customers to change the model. That will be a process. That will be a process itself. It reminds me to some other companies changing how they do business. I think overall it will be very exciting to see that transition and to run it.

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

Martín Migoya: Excellent question, Maggie. Listen, the ocean of our industry is pretty flat. You see other companies, they are pretty much all of us in the same kind of level of growth. What we found is that it's not just the old model with new tools, the success, but a totally different delivery system. When I see that we found a place that is growing fast and we want to build our company around it's exactly what we want to do. Now, there are some industries that are taking this faster than others. We are seeing a lot of success in financial services, in media and entertainment. We are seeing a lot of success in airlines.

This thing makes things more efficiently and faster, 30% faster. So, as we see the game evolving from cost reduction, that has been the main focus of everybody with AI today, to revenue generation, which is what I believe is the smart way of using AI, then we will see a lot of industries coming into this space. The message I want to convey is not just a region or an industry, but it's a new way of delivering what you should pay attention to. That's why I ask you to hold us accountable to that number of transition as we evolve this company.

This is where we are putting our energy on how to deliver in a much more efficient way. It's not just adding people plus AI. People plus AI means a lot of slop, a lot of time used to supervise that slop, a lot of rework over and over and over. When you put order in that process, like what we do with our Glob.AI way of delivering, then things become much more efficient, and things become independent on the model that you want to use, and things become scalable. Then Glob.AI, this is just the beginning of Glob.AI. Glob.AI is much broader than that.

Glob.AI is the initiative we use to transform Globant into an AI native company, the whole Globant into an AI native company. It is our AI native services arm. Let's see. I cannot answer with just a space, a region, or an industry. This is wide. This is very wide.

Maggie Nolan: Understood. Thank you.

Martín Migoya: Very welcome.

Arturo Langa: Thank you, Maggie. The next question comes from the line of Bryan Keane from Citi. Bryan, please go ahead. Your line is open.

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

Martín Migoya: Well, look, in essence, a lot of our customers has been spending the same amount of money getting more productivity, right? That has been the case in the vast majority of the things. I think in the future that could evolve to first we need to convince procurement, we need to convince more people, and that process could be slower than just running the traditional game, but we're ready to pay that. I think that the margin overall will be much better. The capabilities for us to improve the margins even further from where we are today is still better. So I think that deflationary scenario is something that we're not seeing right now.

If we need to pay for it, we will pay it. I want to do that transition and that's why I said, listen, we are migrating this. Even understanding that in some accounts this will be some softened demand, but I believe that overall the picture will be totally different.

Juan Urthiague: I was going to say there, Bryan, that sometimes what we've seen is that it may take longer, or a lot longer to convince, to persuade the procurement teams and persuade our customer to transition and keeping the same level of revenues with more productivity. Sometimes we're seeing that we know that we want to migrate, okay? Sometimes, we can do it faster if we are willing to provide some efficiencies immediately to the customer in terms of price.

But we believe that because the model is so much more efficient and it makes so much more sense for them, that we should be able either to expand on other areas of the organization, to win market share from other vendors. Because what we believe is that this is a model in which we need to deliver services. Now, if we can do it faster, we will do it. I think that's a key message that we are giving here.

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

Juan Urthiague: It's a combination of different things. The market is competitive, right? There are some occasions where we are being able to deliver with less headcount because we are being more efficient with our delivery model right now. If you look at the total headcount, it's down roughly almost 10% year-over-year with revenue per head going up almost 10% year-over-year. So we are being more efficient. In some cases, we have been able to get some additional pricing. But I wouldn't take that as the norm because the market is very competitive right now. But we have been able to increase our revenue per employee because we are delivering in a more efficient manner.

In some cases, we are charging that with a new model, with the AI Pod revenue or the Glob.AI revenue model. In other cases, it may be a fixed price where we are able to deliver more efficiently and hence increasing the revenue per head. It is not that we are charging like Glob.AI because it may be a fixed price, but we are still getting more revenue per employee because we are delivering more efficiently. I think that explains the sharp increase. If you look at the revenue per head three, four, five years ago, it was around $60,000-$65,000 per employee. Now we are getting close to over $90,000 and getting close to $100,000.

Bryan Keane: Yep. Okay. Thank you so much.

Juan Urthiague: You are welcome.

Martín Migoya: Welcome.

Arturo Langa: Thank you very much, Bryan. The next question comes from the line of Arvind Ramnani from Truist Securities. Arvind, please go ahead.

Arvind Ramnani: Hey. Thanks everyone, and good afternoon. Just had a couple of questions on Glob.AI. Martín, you said that's kind of where you're focused your efforts on. Just a couple of questions over there. How does the workload split across these OpenAI and Anthropic and open weight models today? And how do you expect that to shift over the next 18 months? Then, second question around that is, you also mentioned a lot of those clients who are leveraging Glob.AI is existing clients. But how many clients or what percentage are kind of new clients who are not using that? And just last question on that is, what's Globant's kind of unique value proposition? Is it around enterprise context?

Is it around routing logic? What's proprietary to?

Martín Migoya: Yeah.

Arvind Ramnani: Your firm? Yeah.

Martín Migoya: Okay. I will start with the first and then the last, and I will let Diego to complete. I see that we just announced a partnership with Anthropic. We are extremely excited with the things we can do together. We are already seeing some impact from that pipeline coming into our scope of work, which is very exciting. I see models will be evolving, and our customers will decide what to use. When we see Glob.AI, we see something that it could use pretty much any model on the core when developing the software or creating the software, including open weight models, if that's the case.

We're processing a big chunk of our tokens with our own infrastructure and our own models, using open weight models in many occasions. And some of our customers are requesting that, some of our customers are saying go full-fledged with Anthropic or with OpenAI. So we have pretty much full independence on that specific thing. And of course, tools will keep on evolving and keep on becoming more and more sophisticated. To the specific mode that I would like to describe about Glob.AI, Glob.AI is a play of services.

Basically, we are mixing in an absolute frictionless way the creation of the experience and the creation of the software and the definitions that you need with AI and with humans, and putting and packing everything into a single price, and a single price per consumption or per output. So basically, what you saw, the demo, the video that Diego showed to us, is a video that makes it very clear that Glob.AI helps you with the definition, helps you with the creation of the specification, and then a set of agents get triggered, a set of loops get triggered, or a workflow gets triggered.

Depending on what you need to do, those things are different, and this playlist has been curated and evolved with time. Depending on which playlist you are using, they will require different levels of supervision for humans that are watching what those agents are creating, but we charge you in an extremely transparent way, either per million token or per output in case of a user story or in case of a. What I'm saying is that this coordination, that elimination of the friction to buy services is the real thing that we are providing, and that's independent on any model that you may choose.

That's why we are saying this is real AI native services, because it's playing the same role that in the past when cloud didn't exist and we needed to create compute. You buy the servers, you buy the connectivity, you used to buy, or to hire the people to manage those servers, and then suddenly someone coordinated everything in a beautiful way, and it was so easy to ramp up infrastructure. Well, professional services is in that old era, and with Glob.AI, we're creating the AWS of the services.

It's extremely easy to go explain your project, create the context, connect with your Jira or with your GitHub or with your whatever repository you want, and then execute the mission, and that mission will be played in an extremely professional manner, supervised by the best people that can lead you to the right enterprise result you are looking for. That concept, it seems too easy, but it's extremely sophisticated. It's the evolution of services, and it's not just a platform. It can play with codecs, with our own coder. It can play with Claude Code. It can play with pretty much any of the AI tools that are out there.

Then it can be played into any type of infrastructure. But the thing is, it always gets coordinated in a pretty efficient manner with a human. No more ramp-up of teams, no more long time of procurement for something. No more not understanding how much something will cost. It's a totally different thing, and that's why Diego showed the video. But I don't want to screw your speech.

Diego Tartara: No, no, not at all. Sorry. I think it is good, and it is a totally valid question. One of the things that we do find on every single enterprise project has a ton to do with accountability and repeatability. You will not get accountability from a frontier model. You use it, you get a result. Is it good? Is it bad? It is up to you. You implement, that is what you get. Globant brings you accountability. That is why we have humans. The second most important aspect is that the model is not repeatable. When you go to a frontier model, it does. Generating output is super good.

It is amazing, and we are capturing 100% of the value there. But when it comes to planning, how do I execute something, it is a combination of how you prompt it, how much information you gave it, what is the decision and thought process on the model. It consumes a ton of tokens for solving sometimes easy tasks that could have been solved with probably 10% of the spend, as an example. We actually moved away from that, and what you see there as battle cards are actually the formula. So how is the proper, what is the proper way of delivering this type of value?

What is a proper way of doing a migration, a replatforming, an SAP S/4HANA migration, as an example? That blueprint, it has a ton of very well-defined steps. Every step, it has a required input, not less, not more than that, and a required output, and a supervision for that, which is called a quality gate. So you get a repeatable system, a system that you can feed over and over, and you get the same, and this is something you do not get out of AI. It drives enterprises crazy. So you do not have control on the spend.

In many occasions, models are actually working with both ends, the definition and the testing, and what happens in the middle kind of looks like brute force, like trial and error, and that is how graph engineering in many occasions work. So we totally change this, and we use what we know, where the humans can actually add value, make sure things are completely right, and you provide the accountability for that. Coming back to your original question, I think that every single company we talk to actually finds a lot of comfort and feels a lot more comfortable with this model. This is the type of services we have been providing for over 20 years.

It is not about the output, it is not the source code, it is not the executable. It is about the process, capturing what the client actually needs, providing value on top of that, and holding yourself accountable for that output, for that business impact.

Martín Migoya: Let me illustrate this with one example, which is from a few days ago. What happened was one of our customers at Glob.AI needed to do some kind of architecture definition for a pretty complex project around ERPs and APIs and connections. He dropped it on Glob.AI. Our guys got it. The agents start to do the work. Supervision happens across that work. We interact with the customer two or three times, and we finish that in a record 48 hours. To do that same thing, even with Globant, in a traditional way, would have taken at least two or three weeks because of the meetings and the things and the scope gathering.

All those things, all that process that was extremely inefficient before, has been concentrated in a very simple way of doing it now. So that value that is created by understanding the customer faster and getting to the accountable result, as Diego was describing, is the main thing that we are talking about today. That is the transformation I want to make for the whole company, for the whole Globant, for our whole customers. I think that yields much better margins, much more predictable revenue, much more consistent and recurring revenue.

It yields, I believe, results for our customers are way beyond just using AI with a set of engineers, and I think it is the answer that many will follow. It is not just us. We are starters, and we are innovators in this vision, but it will not stop here. So it will be fun.

Arvind Ramnani: Yeah. Just quick follow-up. Sorry.

Martín Migoya: Go ahead.

Arturo Langa: The next question comes from the line of Jonathan Lee from Guggenheim.

Martín Migoya: Go ahead.

Arturo Langa: Jonathan, please. Your line is open.

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

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

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

Martín Migoya: Oh, well, listen. I think that if procurement comes back, that's always a negotiation. But that happens in every single model, not just in the AI Pod model. But the thing is, I believe that we can be much more efficient. If the customer wants to do the same, it will cost less money. If the customer wants to do more, which is most of the cases that we are finding, they will spend the same amount of money. And the thing is that the customers will want to do a lot more. I think they will end up spending, in this new model, more money to produce much more, as we have been describing in the past.

Again, this is a game about the amount of software and the amount of solutions and processes that must be created or used in this new era. I think the game cannot be predicted that simple, saying putting a constant value on the amount of things that must, that can be do or that are needed. Sorry. As this amount of things moves everywhere and in many occasions moves up as the new needs, as described before, the new needs happen, then where you land with the AI Pods there, I think in my opinion, will be increasing. Now, that's demonstrated on the top accounts that are growing at 6% and 6.9%.

Juan Urthiague: And also, even in that scenario, potentially when a customer wants to do the same and there is a potential saving for the customer there, the model runs at higher margins than the traditional model.

Martín Migoya: Correct.

Juan Urthiague: And maybe we will be making the same dollar amount with a little bit less revenue on some accounts, but at the end of the day, the way we look at this is, look, this is the way services need to be delivered going forward. If we do it better and faster than others, even if we lose some money in some projects where we need to reduce a little bit the revenue, we will earn more projects, we will win market share, and we will eventually grow faster again. That's how we are looking at it. And we are willing to take some of those cases. Again, it's not in every case.

We've seen many cases so far where it just keeps adding.

Martín Migoya: Yeah.

Juan Urthiague: More work. But there are some occasions that we are seeing that if we take a haircut, we can accelerate the migration. This is what we are saying today. We are willing to accelerate because it gives us a better position in front of the customer, it protects us from the competition, and we believe that it's going to drive more business into Globant going forward. At the same time, because we can be more efficient, especially as we scale, there is more margin to be earned along the way. So there are multiple things happening at the same time. It's still hard to model, even for us. We are building models every day.

But we are seeing at least that there is clear traction, that there is a clear improvement in margins, and the customers are enjoying and are coming back to scale the model. That's how we are looking at this.

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

Diego Tartara: I think one additional thing is that as we all know, the market as it is today has been concentrating on the cost-saving machine and operational side of things, and it's a lot more difficult. I think it's a worse scenario because it's a lot more difficult when you're part of the cost equation, right? That's the type of pressure, and companies may want to reclaim part of those efficiencies. Once the markets start recovering and move into a revenue-generating engine and stop neglecting products as an example, things will definitely change. We've seen this over and over. When the target is the product, you want to squeeze in new features, additional content, et cetera.

So it's not about saving the money, it's about making your product better and stand out in front of others. So hopefully we will see that happening soon.

Jonathan Lee: Thanks for that color. Just as a follow-up, what macro backdrop is assumed in the revised 2026 outlook? I mean, does the range assume current conditions persist, maybe some stabilization in North American decision cycles or continued deterioration? How much cushion is built into the low end for further softening?

Juan Urthiague: No, I think that the most likely scenario is a midpoint, that it basically assumes already a significant impact from the Middle East and our travel customers. Of course, again, if things get a little bit better on the macro side, especially in the U.S., we can probably be a little bit above that range. Again, if things get worse, we are not seeing that now, but the cushion is there just to make sure that the range is something we will achieve no matter what. But the most likely scenario for us, as always, is a midpoint. Unfortunately, we have seen some factors that impacted our previous guidance that we could not control.

But the midpoint is our most likely scenario. We have plans to get higher than that. We will try to execute on those. There are some things that we don't control.

Fernando Matzkin: To add to that, 90% of our revenues throughout the year are already contracted. In some of the markets that have been affected, particularly in new markets, we have pivoted the pipeline towards banking, towards public sector areas that are less affected as like entertainment and travel and hospitality, which was where our focus was put in new markets so far. We are rebuilding the pipeline. We are closing some of those new opportunities. We also expect that new markets suffered quite a lot in this quarter. We also expect a quick recovery towards the end of the year.

Arturo Langa: Thank you very much, Jonathan. Nice to see you. That will be all for the Q&A section today. Thank you all for your time. Now I will ask Martín to provide some closing comments.

Martín Migoya: Thank you, Arturo. Thank you, everybody, for being here today. I am thankful for your continued support and looking forward to see you on our next quarter call. Thank you. Bye-bye.

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