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<\/a><\/p>\n<\/p>\n
I spent long hours in the film room in college\u00a0football,\u00a0breaking\u00a0down plays\u00a0frame by frame.\u00a0As a center,\u00a0you\u2019re\u00a0responsible for the whole offensive line.\u00a0You\u2019re\u00a0making protection calls,\u00a0reading the defense,\u00a0and\u00a0communicating the threat before the snap. Coaches,\u00a0playbooks,\u00a0and\u00a0footage\u00a0of yourself and your opponents\u00a0\u2013\u00a0every one of those\u00a0inputs\u00a0becomes part of your edge.\u00a0You earned\u00a0those\u00a0insights\u00a0the hard way, and\u00a0the preparation you put in during the week becomes\u00a0the team\u2019s success.<\/p>\n
It also stays with you. You\u00a0don\u2019t\u00a0share it\u00a0outside of\u00a0the\u00a0team.<\/p>\n
The\u00a0instinct\u00a0to\u00a0protect what gives you an advantage is\u00a0exactly\u00a0the right frame for understanding why so many sports organizations are stuck with AI right now.\u00a0And\u00a0it\u2019s\u00a0why what we proved with an NFL team at SHI AI Labs matters.<\/p>\n
Through\u00a0our\u00a0Country Digital Acceleration\u00a0(CDA) program,\u00a0we deployed AI infrastructure designed to run advanced analytics entirely within the organization\u2019s own environment.\u00a0No data leaving the building, no black-box model, and no cloud dependency on systems that have no stake in whether the team wins on Sunday.\u00a0The result\u00a0wasn\u2019t\u00a0just a faster pipeline. It was something harder to build and more valuable to\u00a0own:\u00a0confidence.\u00a0The confidence to\u00a0actually act on what the data was telling them.<\/p>\n
AI only becomes useful when\u00a0it\u2019s\u00a0grounded in your data:\u00a0your playbooks, your systems, your way of operating.\u00a0Out-of-the-box models\u00a0can\u2019t\u00a0deliver that.\u00a0Without that grounding, you lose relevance\u00a0and\u00a0trust.<\/p>\n
The problem\u00a0isn\u2019t\u00a0the models.\u00a0It\u2019s\u00a0everything around them.\u00a0<\/strong><\/h2>\nTeams across the\u00a0league\u00a0are experimenting with AI. They have the data:\u00a0player tracking, performance metrics, opponent tendencies, scouting reports,\u00a0and\u00a0fan behavior. They have access to powerful models.\u00a0But most\u00a0teams\u00a0are still stuck in pilot mode.<\/p>\n
Not because\u00a0the technology\u00a0isn\u2019t\u00a0capable enough. Because the environment around it\u00a0isn\u2019t\u00a0built for what AI\u00a0actually demands\u00a0in a sports context.<\/p>\n
Think about\u00a0what\u2019s\u00a0at stake. Playbooks, contract\u00a0details, and proprietary game strategy developed over\u00a0months.\u00a0Even new practice tape\u00a0represents\u00a0a genuine competitive edge,\u00a0until\u00a0they\u00a0don\u2019t. Sports organizations are working with some of the most sensitive, time-critical data of any industry.\u00a0They\u00a0operate\u00a0on weekly cycles where a single leaked insight at the wrong moment can have real consequences.<\/p>\n
In other parts of the ecosystem,\u00a0like broadcasting and fan experiences,\u00a0those constraints become even more immediate, where real-time delivery is critical.\u00a0A delay of even a few seconds can break the experience, whether\u00a0it\u2019s\u00a0live stats\u00a0syncing\u00a0with a broadcast, in-venue\u00a0interactions, or second-screen engagement.<\/p>\n
The standard enterprise AI playbook\u00a0\u2013\u00a0move your data to a cloud environment, run models there, get insights back\u00a0\u2013\u00a0creates a fundamental tension with that reality.\u00a0You\u2019re\u00a0asking organizations to\u00a0hand\u00a0their most sensitive competitive assets to infrastructure they\u00a0don\u2019t\u00a0fully control, in exchange for analytical power they could have kept in-house.<\/p>\n
Most organizations sense this tension. Very few have resolved it.<\/p>\n
That\u2019s exactly where\u00a0S\u016bmerSports\u00a0comes in. Their platform is purpose-built for\u00a0sports,\u00a0designed to ingest the unique data sets teams rely on, and apply models that are tuned to the realities of the game, from player performance to team strategy.<\/p>\n
\nThis is\u00a0AI\u00a0built for how\u00a0sports teams\u00a0actually operate.<\/h2>\n<\/blockquote>\nDon\u2019t\u00a0move the data. Move\u00a0the intelligence.\u00a0<\/strong><\/h2>\nThe approach we proved with that NFL team is conceptually simple: run the AI where the data already lives. Keep everything inside the organization\u2019s own environment \u2014 governed, secured, fully under their control \u2014 and bring the intelligence to the data rather than the other way around.<\/p>\n
That same philosophy applies to the infrastructure itself.<\/p>\n
S\u016bmerBrain,\u00a0S\u016bmerSports\u2019 AI engine purpose-built for professional sports operations, now runs on Cisco AI PODs: pre-integrated,\u00a0validated\u00a0full-stack solutions designed to take the hardest part of AI off the table.<\/p>\n
This is plug-and-play AI infrastructure: Easy to assemble, no hoping the components work together. It\u2019s a system organizations can stand up quickly and trust to run consistently from day one.<\/p>\n
That simplicity matters more than it sounds.\u00a0Most\u00a0teams\u00a0don\u2019t\u00a0fail at AI because of the model. They\u00a0get stuck\u00a0because getting everything around the model to work together\u00a0\u2013 compute, networking, security, data\u00a0pipelines\u00a0and operations \u2013\u00a0is too slow, too complex, and too risky.<\/p>\n
In\u00a0sports, the cost of getting stuck is measured in\u00a0outcomes\u00a0on the field.\u00a0When you remove that friction, AI stops being a project\u2014and starts becoming\u00a0an operational advantage.<\/p>\n
As CEO Lorrissa Horton\u00a0recently posted:\u00a0\u201cOrganizations\u00a0don\u2019t\u00a0fail at AI because the model underperforms. They fail because everything around the model\u2026is slower and harder than anyone planned.\u201d<\/p>\n
Together,\u00a0S\u016bmerBrain\u00a0and Cisco AI PODs make it possible to go from data to insight much faster,\u00a0without the integration work that typically slows teams down.<\/p>\n
The Moneyball moment\u00a0already\u00a0happened. This is the next one.\u00a0<\/strong><\/h2>\nThe realization that better data could reshape competitive outcomes is old news now. Every organization accepts it. The question\u00a0isn\u2019t\u00a0whether to use data anymore.\u00a0It\u2019s\u00a0whether you can operationalize AI fast enough, and securely enough, to turn that data into a real edge before someone else does.<\/p>\n
Coming from\u00a0the college football\u00a0world, I understand something about that pressure. The edge\u00a0you\u2019re\u00a0chasing\u00a0isn\u2019t\u00a0abstract.\u00a0It\u2019s\u00a0the difference between a coaching staff that walks into Saturday\u00a0with conviction and one that walks in with questions. AI should be building that conviction.\u00a0It\u00a0can\u2019t\u00a0do that if the people using it\u00a0don\u2019t\u00a0fully trust where\u00a0it\u2019s\u00a0running or what\u00a0it\u2019s\u00a0doing with their data.<\/p>\n
The same logic extends beyond football operations. In the front office, secure AI infrastructure changes\u00a0what\u2019s\u00a0possible in draft strategy, roster construction, and contract decisions.\u00a0This\u00a0isn\u2019t\u00a0because\u00a0the analysis\u00a0gets\u00a0better, but\u00a0because executives can\u00a0trust\u00a0and act on it.<\/p>\n
In the back office, the same rigor applied to the game can be applied to the business: translating fan behavior into personalized experiences,\u00a0optimizing\u00a0stadium operations, finding revenue opportunities\u00a0and patterns that would be impossible to surface manually, simply because of the scale and complexity involved.<\/p>\n
The organizations that pull ahead\u00a0won\u2019t\u00a0be the ones with access to the best models. Everyone has good models now. The winners will be the ones that built the environment to\u00a0actually use\u00a0them \u2014 where the data is trusted, the infrastructure is reliable, and the gap between insight and decision is measured in seconds, not meetings.<\/p>\n
That\u2019s\u00a0what we proved in a real environment, with a real\u00a0NFL\u00a0team, under real conditions.<\/p>\n
The technology is ready. The question is whether your infrastructure is.<\/p>\n
\nExplore\u00a0Cisco AI PODs\u00a0to\u00a0learn\u00a0how\u00a0you can\u00a0stand up your AI use cases\u00a0and\u00a0put\u00a0secure,\u00a0game-ready intelligence inside your organization\u00a0today.<\/h2>\n<\/blockquote>\n
\u00a0<\/p>\n<\/p><\/div>\n
This is\u00a0AI\u00a0built for how\u00a0sports teams\u00a0actually operate.<\/h2>\n<\/blockquote>\nDon\u2019t\u00a0move the data. Move\u00a0the intelligence.\u00a0<\/strong><\/h2>\nThe approach we proved with that NFL team is conceptually simple: run the AI where the data already lives. Keep everything inside the organization\u2019s own environment \u2014 governed, secured, fully under their control \u2014 and bring the intelligence to the data rather than the other way around.<\/p>\n
That same philosophy applies to the infrastructure itself.<\/p>\n
S\u016bmerBrain,\u00a0S\u016bmerSports\u2019 AI engine purpose-built for professional sports operations, now runs on Cisco AI PODs: pre-integrated,\u00a0validated\u00a0full-stack solutions designed to take the hardest part of AI off the table.<\/p>\n
This is plug-and-play AI infrastructure: Easy to assemble, no hoping the components work together. It\u2019s a system organizations can stand up quickly and trust to run consistently from day one.<\/p>\n
That simplicity matters more than it sounds.\u00a0Most\u00a0teams\u00a0don\u2019t\u00a0fail at AI because of the model. They\u00a0get stuck\u00a0because getting everything around the model to work together\u00a0\u2013 compute, networking, security, data\u00a0pipelines\u00a0and operations \u2013\u00a0is too slow, too complex, and too risky.<\/p>\n
In\u00a0sports, the cost of getting stuck is measured in\u00a0outcomes\u00a0on the field.\u00a0When you remove that friction, AI stops being a project\u2014and starts becoming\u00a0an operational advantage.<\/p>\n
As CEO Lorrissa Horton\u00a0recently posted:\u00a0\u201cOrganizations\u00a0don\u2019t\u00a0fail at AI because the model underperforms. They fail because everything around the model\u2026is slower and harder than anyone planned.\u201d<\/p>\n
Together,\u00a0S\u016bmerBrain\u00a0and Cisco AI PODs make it possible to go from data to insight much faster,\u00a0without the integration work that typically slows teams down.<\/p>\n
The Moneyball moment\u00a0already\u00a0happened. This is the next one.\u00a0<\/strong><\/h2>\nThe realization that better data could reshape competitive outcomes is old news now. Every organization accepts it. The question\u00a0isn\u2019t\u00a0whether to use data anymore.\u00a0It\u2019s\u00a0whether you can operationalize AI fast enough, and securely enough, to turn that data into a real edge before someone else does.<\/p>\n
Coming from\u00a0the college football\u00a0world, I understand something about that pressure. The edge\u00a0you\u2019re\u00a0chasing\u00a0isn\u2019t\u00a0abstract.\u00a0It\u2019s\u00a0the difference between a coaching staff that walks into Saturday\u00a0with conviction and one that walks in with questions. AI should be building that conviction.\u00a0It\u00a0can\u2019t\u00a0do that if the people using it\u00a0don\u2019t\u00a0fully trust where\u00a0it\u2019s\u00a0running or what\u00a0it\u2019s\u00a0doing with their data.<\/p>\n
The same logic extends beyond football operations. In the front office, secure AI infrastructure changes\u00a0what\u2019s\u00a0possible in draft strategy, roster construction, and contract decisions.\u00a0This\u00a0isn\u2019t\u00a0because\u00a0the analysis\u00a0gets\u00a0better, but\u00a0because executives can\u00a0trust\u00a0and act on it.<\/p>\n
In the back office, the same rigor applied to the game can be applied to the business: translating fan behavior into personalized experiences,\u00a0optimizing\u00a0stadium operations, finding revenue opportunities\u00a0and patterns that would be impossible to surface manually, simply because of the scale and complexity involved.<\/p>\n
The organizations that pull ahead\u00a0won\u2019t\u00a0be the ones with access to the best models. Everyone has good models now. The winners will be the ones that built the environment to\u00a0actually use\u00a0them \u2014 where the data is trusted, the infrastructure is reliable, and the gap between insight and decision is measured in seconds, not meetings.<\/p>\n
That\u2019s\u00a0what we proved in a real environment, with a real\u00a0NFL\u00a0team, under real conditions.<\/p>\n
The technology is ready. The question is whether your infrastructure is.<\/p>\n
\nExplore\u00a0Cisco AI PODs\u00a0to\u00a0learn\u00a0how\u00a0you can\u00a0stand up your AI use cases\u00a0and\u00a0put\u00a0secure,\u00a0game-ready intelligence inside your organization\u00a0today.<\/h2>\n<\/blockquote>\n
\u00a0<\/p>\n<\/p><\/div>\n
The approach we proved with that NFL team is conceptually simple: run the AI where the data already lives. Keep everything inside the organization\u2019s own environment \u2014 governed, secured, fully under their control \u2014 and bring the intelligence to the data rather than the other way around.<\/p>\n
That same philosophy applies to the infrastructure itself.<\/p>\n
S\u016bmerBrain,\u00a0S\u016bmerSports\u2019 AI engine purpose-built for professional sports operations, now runs on Cisco AI PODs: pre-integrated,\u00a0validated\u00a0full-stack solutions designed to take the hardest part of AI off the table.<\/p>\n
This is plug-and-play AI infrastructure: Easy to assemble, no hoping the components work together. It\u2019s a system organizations can stand up quickly and trust to run consistently from day one.<\/p>\n
That simplicity matters more than it sounds.\u00a0Most\u00a0teams\u00a0don\u2019t\u00a0fail at AI because of the model. They\u00a0get stuck\u00a0because getting everything around the model to work together\u00a0\u2013 compute, networking, security, data\u00a0pipelines\u00a0and operations \u2013\u00a0is too slow, too complex, and too risky.<\/p>\n
In\u00a0sports, the cost of getting stuck is measured in\u00a0outcomes\u00a0on the field.\u00a0When you remove that friction, AI stops being a project\u2014and starts becoming\u00a0an operational advantage.<\/p>\n
As CEO Lorrissa Horton\u00a0recently posted:\u00a0\u201cOrganizations\u00a0don\u2019t\u00a0fail at AI because the model underperforms. They fail because everything around the model\u2026is slower and harder than anyone planned.\u201d<\/p>\n
Together,\u00a0S\u016bmerBrain\u00a0and Cisco AI PODs make it possible to go from data to insight much faster,\u00a0without the integration work that typically slows teams down.<\/p>\n
The Moneyball moment\u00a0already\u00a0happened. This is the next one.\u00a0<\/strong><\/h2>\nThe realization that better data could reshape competitive outcomes is old news now. Every organization accepts it. The question\u00a0isn\u2019t\u00a0whether to use data anymore.\u00a0It\u2019s\u00a0whether you can operationalize AI fast enough, and securely enough, to turn that data into a real edge before someone else does.<\/p>\n
Coming from\u00a0the college football\u00a0world, I understand something about that pressure. The edge\u00a0you\u2019re\u00a0chasing\u00a0isn\u2019t\u00a0abstract.\u00a0It\u2019s\u00a0the difference between a coaching staff that walks into Saturday\u00a0with conviction and one that walks in with questions. AI should be building that conviction.\u00a0It\u00a0can\u2019t\u00a0do that if the people using it\u00a0don\u2019t\u00a0fully trust where\u00a0it\u2019s\u00a0running or what\u00a0it\u2019s\u00a0doing with their data.<\/p>\n
The same logic extends beyond football operations. In the front office, secure AI infrastructure changes\u00a0what\u2019s\u00a0possible in draft strategy, roster construction, and contract decisions.\u00a0This\u00a0isn\u2019t\u00a0because\u00a0the analysis\u00a0gets\u00a0better, but\u00a0because executives can\u00a0trust\u00a0and act on it.<\/p>\n
In the back office, the same rigor applied to the game can be applied to the business: translating fan behavior into personalized experiences,\u00a0optimizing\u00a0stadium operations, finding revenue opportunities\u00a0and patterns that would be impossible to surface manually, simply because of the scale and complexity involved.<\/p>\n
The organizations that pull ahead\u00a0won\u2019t\u00a0be the ones with access to the best models. Everyone has good models now. The winners will be the ones that built the environment to\u00a0actually use\u00a0them \u2014 where the data is trusted, the infrastructure is reliable, and the gap between insight and decision is measured in seconds, not meetings.<\/p>\n
That\u2019s\u00a0what we proved in a real environment, with a real\u00a0NFL\u00a0team, under real conditions.<\/p>\n
The technology is ready. The question is whether your infrastructure is.<\/p>\n
\nExplore\u00a0Cisco AI PODs\u00a0to\u00a0learn\u00a0how\u00a0you can\u00a0stand up your AI use cases\u00a0and\u00a0put\u00a0secure,\u00a0game-ready intelligence inside your organization\u00a0today.<\/h2>\n<\/blockquote>\n
\u00a0<\/p>\n<\/p><\/div>\n
Explore\u00a0Cisco AI PODs\u00a0to\u00a0learn\u00a0how\u00a0you can\u00a0stand up your AI use cases\u00a0and\u00a0put\u00a0secure,\u00a0game-ready intelligence inside your organization\u00a0today.<\/h2>\n<\/blockquote>\n
\u00a0<\/p>\n<\/p><\/div>\n