UNAM Tightens AI Policy: Pumas and Mexican Football's Technology Governance Challenge

At a meeting with no passes and no goals, the Artificial Intelligence Coordinating Council of the National Autonomous University of Mexico (CCOIA-UNAM) approved a classroom mobile-device policy framed as "formative, not prohibitive." For someone who has spent fifty years reading football through formations, space and pressing rhythms, this is the kind of item I usually cross out from the first line. But one detail made me stop: UNAM is the parent institution of Pumas UNAM, one of the biggest clubs in Liga MX. When the organization behind a football club sets out an artificial-intelligence governance policy, football should listen. In my drawer there are football notes older than the internet, and they taught me one thing: football's biggest changes usually begin where nobody is looking. UNAM is one of the largest university systems in Latin America, gathering dozens of faculties, institutes and affiliated schools, with hundreds of thousands of students each year. In Mexico, the debate over classroom phone use has escalated into a national policy topic, as several states push hard restrictions. UNAM chose a different road: instead of banning, it builds capability. CCOIA-UNAM is the body coordinating the university's AI initiatives. According to published internal figures, a survey recorded 44 participating entities, 119 research lines, 280 courses, 67 tools and 27 forums related to AI. The university also asked its Ethics Committee to create a dedicated AI subcommittee, while running training sessions for teachers on using technology "ethically and responsibly." A student hackathon is part of the plan. The striking part lies in the philosophy behind the numbers: UNAM treats technology as a variable to be governed, rather than a threat to be removed. The policy also stresses protecting teachers' "pedagogical authority" — a principle that sounds very familiar to anyone who has worked in a football dressing room. For Pumas, this parent institution provides more than a name and a stadium; it shapes an entire value system, from scouting to how young players are treated. For Pumas UNAM, the story is not distant at all. Modern football has become a data industry, where every pass, every run and every breath of a player can be digitized. A Liga MX club today runs two parallel systems: the on-pitch system — formation, tactics, fitness — and the system inside the analysis room, where machine-learning models process millions of tracking data points each match. When UNAM, as parent institution, sets a standard for AI governance, it draws a boundary around how Pumas can exploit technology. Let us be concrete. An AI model can predict a centre-back's injury risk from running volume and fixture congestion. It can classify thousands of youth-league plays to find the pattern of a creative midfielder. It can simulate hundreds of set-piece scenarios and point to the optimal shooting angle. All of this has existed at European clubs for years. The gap between Liga MX and Europe is not money — many Mexican clubs have respectable budgets — it is process. A European side may spend three years building a data department before harvesting results. A Mexican side is usually pressured to win in its very first season. The question for UNAM and Pumas is not whether to use AI, but how to govern it without losing identity. Pumas is a club built on its youth academy — the "cantera" — where players like Hugo Sánchez once came through. An AI system can screen thousands of children faster, but it can also smother the intuition of scouts who trust the eye more than the algorithm. This is the central contradiction every club with an academy tradition must solve: how to use data to widen vision without turning players into numbers. This is where I want to pause, because it relates directly to how I read a match. A tactical diagram is only paper; the players are the ones who write the game. A data model can tell you the team should press high, but it cannot tell you that your full-back is losing confidence after three straight games of being attacked behind his back. AI is good at finding the numerator, but weak at understanding the why. And in football, the why usually decides the what. Based on my experience watching matches, the best tactical decisions I have ever witnessed did not come from a data sheet, but from a coach who read fear in his players' eyes. When football returned after the pandemic in empty stadiums, I learned that crowd pressure is a variable that cannot be digitized — and it changes player behaviour in ways no model predicts correctly. UNAM's "formative, not prohibitive" policy is, in fact, a governance lesson for football. At many clubs, technology is adopted half-heartedly: buying expensive analytics software, hiring data experts, but never changing the decision-making process. The result is data trapped in a PDF report nobody on the coaching staff reads. That is "technology theatre" — a way to burn money to look modern. UNAM, with a philosophy of building capability rather than issuing bans, shows another road: train people first, tools second. With 280 courses and teacher training sessions, they are doing it in the right order. If Pumas applies the same principle, it will train its analytics staff before buying systems, and let coaches master the tool rather than let the tool master the coaches. But be careful with this too-pretty story. The biggest blind spot of any technology-governance program — at a university or a club — is the gap between policy on paper and reality in the dressing room. An AI ethics subcommittee can meet every month, but if the head coach still makes decisions by instinct and emotion, every data model is mere decoration. At UNAM, a similar risk exists: a policy approved at council level may never reach a 200-student classroom, where a tired teacher is still fighting a student's phone. For Pumas, the risk is even greater. Football is an industry of immediate results. A coach can be sacked after four defeats, and nobody has time to wait for a data-governance program to "mature." The transfer market does not run on money, but on fear — and fear always beats long-term models. That is why many clubs talk about AI yet still buy players on the whim of an agent. A skilled agent can craft a more compelling narrative than any data model, and in a market of asymmetric information, the story often sells at a higher price than the truth. UNAM's AI policy will only matter to Pumas if it changes how the club decides in the most stressful weeks of the season, not merely in workshops. A club can have an ethics subcommittee, 280 courses, a hackathon — and still lose a match because nobody read the report before kick-off. Technology governance, in the end, is not a software problem, but a problem of people and organizational habits. What I will track this season is not CCOIA-UNAM's announcement, but the small signals: whether Pumas hires more data analysts, whether its youth academy adopts a new evaluation model, whether the coaching staff actually reads reports before each match. I found the football of the future in a match nobody filmed. Perhaps I will also find it in a policy nobody noticed.