Can you imagine a World Cup without AI?

It’s certainly hard to imagine. But perhaps the real interest no longer lies in seeing just how far artificial intelligence can go, but rather in understanding how to use it efficiently and measure the value it generates.

Two years ago on this blog article, we dubbed the Paris Olympics the “AI Games”, but the 2026 World Cup proves that this technology is no longer a unique feature: it’s become an essential part of what it means to hold a major sporting event.

One of the best examples is Football AI Pro, an assistant powered by generative artificial intelligence that can analyze millions of soccer data points to provide teams with tactical information before and after matches. Unlike general-purpose models, this tool is based on a language model trained specifically using FIFA’s taxonomy and official data, allowing it to generate analyses tailored to the context of the game and answer complex questions about how matches unfold.

AI is also being used in refereeing through 3D models of players that improve the accuracy of semi-automatic offside calls, in intelligent stabilization systems for referee cameras to provide sharper images in real time, and in tools capable of automatically generating highlights, replays, and personalized content for broadcasts, digital platforms, and fans.

Beyond each of these applications, what is truly significant is that AI is already integrated throughout the entire event production and analysis chain, helping with decision-making, viewer experience improvements, and the optimization of processes that, just a few years ago, required completely manual work.

However, behind all this innovation is a question that’s much less apparent to fans: From an operational and economic standpoint, what’s involved in keeping such a complex AI ecosystem running? And, above all, how can we measure the value it actually generates?

FIFA has not released these figures, and isolating the specific cost of AI is no simple task, since it’s part of a much broader technological system that includes communications, sensors, data analysis, and audiovisual production. In fact, this lack of visibility underscores one of today’s major challenges in relation to AI: the need for mechanisms to measure, manage, and optimize its use to ensure that it delivers real value.

This insight goes beyond the world of sports and is directly linked to a challenge that many organizations are currently facing. The widespread adoption of generative AI has ushered in a new paradigm: for the first time, many software capabilities have a variable cost associated with the use of AI models. The challenge is no longer simply to incorporate artificial intelligence, but to govern it, measure it, and use it efficiently.

At GMV, we’re no strangers to this challenge either. Having usage metrics, governance mechanisms, and criteria for selecting the most appropriate model for each case is becoming a strategic priority. The question is no longer just how much it costs to use AI, but how to maximize the value of each interaction with these models, identify best practices for using them, and ensure sustainable use as they become increasingly prevalent within organizations.

After all, just as in a World Cup, success isn’t measured by the amount of artificial intelligence deployed, but by its ability to create value in every decision and every euro invested.

Author: Jesús Mariano Pascual Díaz