Globant launches Glob.AI OS to govern AI across the enterprise

Globant has launched Glob.AI OS, a governance layer that connects large organisations to more than 320 open and closed AI models from more than 25 providers, including OpenAI, Anthropic and Google. The company announced the product on 13 November 2026 as an expansion of its Glob.AI platform. The launch is less a model release than an attempt to own the control plane that sits above every model an enterprise already uses.

The pitch targets a problem most large companies now recognise. Multiple teams have adopted AI tools in fragmented ways, running different models and systems tied to sensitive data. Globant argues that access to AI is no longer the bottleneck; controlling its use at scale and tying it to production work is. Glob.AI OS is designed to give one answer to who used a model, on what data, at what cost, and under which policy.

What does Glob.AI OS actually do?

The software acts as a single layer for managing AI activity, costs, identity and oversight across different models, tools and teams. It routes tasks to different models while giving companies visibility into spending, usage and policy controls. In practice, that means an IT organisation can see, in one place, which department called which model and what it cost.

The product is built around four components:

• Intelligence Hub — model access and routing across the connected provider set.
• Corporate Hub — links the system to company data and internal systems.
• Agentic Hub — a space for testing and running AI agents (software programs that perform tasks with limited step-by-step human instruction).
• Token Vault — manages budgets, permissions and audit records, with tokens being the units of AI model usage that providers bill on.

Deployment is flexible. Glob.AI OS runs as software as a service or inside a client's own cloud environment. Users reach it through chat, web, desktop and command-line interfaces, or through headless deployment using MCP (Model Context Protocol, an open standard for connecting AI systems to external tools and data).

Globant positions the system as a way to bring governed AI into day-to-day work rather than confining it to specialist teams. Through what the company calls Glob.AI Apps, employees can use AI tools inside existing workflows, ask questions, delegate tasks, work with coding agents and run prebuilt agentic systems from Globant and its partners. The stated goal is breadth of adoption with central accountability.

How does the pricing model change?

The launch also signals a shift in how Globant sells AI work to large clients. Instead of charging by hours worked or software seats, customers can pay for outcomes or consumption through what Globant calls its AI-Native Services model. That is a notable departure for an IT services industry built on billable labour.

The model combines two elements. The first is AI Pods, which Globant describes as human-supervised delivery units that bring together documented workflows, AI agents and oversight from specialists, including Forward Deployed Engineers (technical staff embedded directly with clients). The second is the new operating layer, which provides governance and shared access to data, controls and institutional knowledge across those pods.

Sarab Narang, Chief Executive Officer of Glob.AI, framed the commercial change directly: "We are making it seamless for global leaders to take the best out of AI leveraging Globant's 23+ years of experience working with top companies in almost every industry in a completely reshuffled engagement model where they only pay for outcomes."

Narang also made the governance claim more concrete: "Glob.AI OS lets a large organization put AI in the hands of many teams and many agents, while keeping one clear answer to who used it, on what data, at what cost, and under which policy." That quote is the product's core promise, and buyers will need to test whether audit coverage holds across all 320-plus models in practice.

What results has Globant reported?

Globant says the AI Pods element of the platform is already in use at several Fortune 500 companies across media, entertainment, professional services and finance. The company cited a set of client results tied to those engagements, all of which come from Globant itself rather than independent verification.

• FIFA recorded a 20% increase in throughput efficiency while maintaining or improving quality rates.
• YPF, the Argentine energy company, reduced contract timelines by up to 40%.
• PharmaMar gained insights 15 times faster in oncology research.
• An unnamed commercial bank completed a COBOL migration in two months, compared with a projected 14 months under a more traditional model.

More broadly, Globant said AI-Native Services engagements have delivered up to seven times faster legacy modernisation and three times faster product discovery. The bank comparison is the most striking figure, since COBOL modernisation has resisted automation for decades. The absence of the bank's name, however, limits how much weight that claim can carry.

Why does this matter now?

The launch lands in a competitive moment. IT services groups and software providers are racing to define how large companies adopt generative AI beyond pilot projects. For corporate buyers, the central issue is managing multiple AI models and agents while keeping oversight of costs, data access and accountability.

Globant, which operates in more than 30 countries, has been positioning itself around AI-led consulting and delivery as client demand shifts from experimental work to operational deployment. The company says client data remains under the customer's control and is not used to train external models — a commitment aimed squarely at the data-governance concerns that have stalled enterprise rollouts.

Martín Migoya, Co-Founder and Chief Executive Officer of Globant, framed the launch as a category creation: "AI has taken the world by storm, but having access to technology is not enough. Enterprises need a new way to put it to work, with more transparency, control and speed. Glob.AI brings intelligence, enterprise context and the agentic layer together so organizations can access AI Native Services and ship working, production-ready outcomes through AI Pods."

Migoya went further: "We are extremely proud of what we are bringing to the world. Our deep expertise and experience in delivering business solutions for the best companies in the world has prepared us for this exact moment. We're not just reinventing services; we have created a new category of delivering business outcomes."

That is confident language, and it should be read as positioning rather than settled fact. Rivals including the major consultancies and hyperscalers are pursuing similar governance and outcome-based models, and Globant has not published independent benchmark results for Glob.AI OS itself.

What are the caveats?

Three gaps stand out in the announcement. First, all performance figures come from Globant's own account of client engagements, with no third-party audit named. Second, the outcome-based pricing model is new, and the announcement does not specify how outcomes are measured or disputed in contracts. Third, while the product claims unified audit and cost control across 320-plus models, the announcement does not detail how coverage works for models added after deployment or for partner-built agents run inside the Agentic Hub.

None of this makes the claims implausible. It does mean enterprises evaluating Glob.AI OS will want the Token Vault's audit trail demonstrated against their own compliance requirements before signing outcome-based contracts.

The stakes are straightforward: if outcome-priced, centrally governed AI delivery works at Fortune 500 scale, it pressures every services firm still billing by the hour. If it does not, Globant's category claim joins the long list of enterprise AI reinventions that stayed in the pilot phase.