There’s no such thing as a passive fan anymore. Every match, every highlight, every notification competes for attention in a dynamic ecosystem where sports, media, and entertainment have fully converged. For global leagues, growth depends not only on what happens on the pitch but also on how consistently they deliver meaningful experiences across every touchpoint.
For LALIGA, with over 260 million fans worldwide, that means operating like a living global digital platform where success depends on deeply understanding its data and technology platforms, and where AI plays a new and critical role.
At NVIDIA GTC 2026, we took the stage alongside Sportian, our sports products division, to present how we partnered with LALIGA to build the sports industry’s first full-scale agentic AI ecosystem assisted by NVIDIA accelerated computing.
The focus of the session was clear: The challenge of enterprise AI is no longer building models, it’s making them work at scale. To rise to this challenge, LALIGA partnered with Globant to push AI beyond isolated use cases and into the core of the organization. The discussion surfaced a few defining shifts:
AI as Infrastructure
One of the central ideas shared during the session is that AI must be treated as infrastructure, not as a collection of isolated features.
At LALIGA, AI is embedded similarly to cloud or data platforms:
- It is governed centrally
- It is orchestrated across systems
- It is designed for scale from day one
This approach enabled the organization to move from fragmented pilots to a fully operational ecosystem with over 500 active models operationalized at scale.
From Fragmentation to Decision Velocity
At scale, the real value of AI goes beyond efficiency. It’s velocity. By connecting data teams and workflows, LALIGA eliminated operational silos. Insights now flow more fluidly, accelerating decision-making in areas where timing is critical, such as matchday operations, fan engagement, and commercial strategy. This enables LALIGA to be more responsive and adaptive.
Human-in-the-Loop by Design
But even as AI systems expand, human oversight remains embedded in the model. Tools like Sportian’s Calendar Selector illustrate this balance. AI explores complex scenarios across multiple variables, while humans retain final control to ensure alignment with commercial, regulatory, and strategic priorities.
This approach enables scale without losing control. It ensures that AI augments decision-making rather than replacing it.
AI Pods at the Core of Scalable Execution
At the center of this transformation are Globant’s AI Pods: coordinated systems where human expertise and intelligent agents work together across workflows, including use cases like real-time sports analytics. This model enables organizations to move from fragmented experimentation to structured, scalable execution.
By connecting data, systems, and teams, AI Pods increase decision velocity across the organization. Insights are no longer trapped in silos. They move across departments, enabling faster and more informed actions.
The impact is visible across LALIGA’s value chain:
- Up to 25% higher matchday revenue, with AI optimizing demand, schedules, and ticket pricing
- Stronger engagement and media value growth, with AI helping drive a €1B+ media rights business in Spain.
- Real-time AI analytics enhance both on-pitch decisions and the fan experience.
- 500+ AI models in production, processing 816TB of data, and supporting 5.5M+ concurrent users at true enterprise scale.
- Multi-agent AI systems forecast demand and optimize match calendars and ticket pricing.
This level of scale reflects a shift in mindset. AI is treated in the same way as cloud or data platforms: as infrastructure that powers the business, not as an add-on.
“At NVIDIA GTC, we demonstrated that while many generative AI pilots fail to reach production, LALIGA has successfully operationalized agentic AI by treating it as foundational infrastructure rather than a series of isolated experiments”
Carolina Dolan Chandler, CTO, Media, Entertainment, Sports & Hospitality AI Studio at Globant
A Blueprint Beyond Sports
The significance of what Globant and LALIGA presented extends beyond the sports industry. The core challenge for enterprise AI is orchestrating, governing, securing, and scaling models across real operations.
What was shared at NVIDIA GTC 2026 offers a working blueprint:
- Treat AI as infrastructure
- Build systems, not isolated use cases
- Combine agentic workflows with human oversight
- Design for scale from day one
This is how organizations move from experimentation to impact.
Globant’s approach with AI Pods demonstrates that the gap between AI ambition and business results can be closed. The shift is already happening. And in the case of LALIGA, it is happening at scale.
