With the National Football League (NFL) back tonight, love seeing Seattle Seahawks analyst Brian Eayrs and coaches across the league using new Copilot and Excel tools to help with decision making in the booths and on the sidelines. Learn more: https://lnkd.in/gxevJptr
Transcript
Why do I do it? It's a good question. I always dreamed that I could someday make it to the NFL. I was a backup backup backup quarterback. But that probably wasn't my path forward. My dad was a director of research and development for the Minnesota Vikings. And I followed him to work every day I could. One of the things he told me is that a lot of people are fighting to be in charge, but there���s a real value to being a great assistant. There's an art and a science to football. The analytics task is to make sure the science is the best in the world. So I always ran towards technology. (Patrick, have you thought about our fourth down model today?) With Copilot in Excel, I can see plays, snap count, personnel, and anticipate like never before. It's a fresh perspective on how to see the game. You might not feel that it's a huge role. But it is. We're very grateful that we get Excel in the coach's box. Brian's the right hand man, to all things football information. Now we're able to keep track of things digitally. Coach, you with me? Being in my ear, explaining the situation that he anticipates. How we said we were gonna act to them. He's my number one guy in game decision making. He's kind of the man behind the scenes. I've been dreaming of winning a Super Bowl since I was six years old. The moment that it all actually hit me was when I saw my last name on Super Bowl ring. It felt like my dad and I somehow connected in the space of NFL greatness. It was an incredible moment.
Satya, this raises an interesting question beyond football. The advantage may not simply come from giving coaches more information or more AI. It may depend on where AI enters the decision-making sequence. Observe → interpret → decide → communicate → execute. In an environment where seconds matter, putting intelligence at the wrong point in that sequence could create another bottleneck instead of removing one. Perhaps the real opportunity is not simply AI-assisted decision making, but designing the Human + AI sequence so that each does what it does best at exactly the right moment. Football may become one of the most visible real-time laboratories for figuring that out.
My favourite bit here is Excel on an NFL sideline. Coaches will use anything that sits inside a tool they already trust, which is why adoption is usually a familiarity problem, not a technology one.
Great example of AI moving beyond the lab and into high-stakes, real-time decision support. Seeing Copilot and Excel used across the league highlights how practical tools can complement human judgment.
Satya Nadella Interesting to see tools like Copilot and Excel being used for real time decision making in the NFL. I don’t follow American football closely however I really enjoyed watching the World Cup. It’s impressive how data is supporting decisions even in high pressure sports environments.
Great to see technology, sport, and community coming together. The strongest innovation stories are often the ones that create shared experiences beyond the product itself.
The interesting design challenge is making the decision path legible: surface the data used, the confidence, and the next-best action without interrupting the coach's flow. In high-pressure settings, explainability has to be part of the interaction, not a report after the fact.
Interesting example of AI creating demand across both the front line and the back office of sports. From real-time monitoring on the field to decision-making in the booth, AI is becoming part of the operational loop. This requires the convergence of AI infrastructure, models, and agents - creating a new class of AI workload and, potentially, a significant new source of infrastructure demand.
This is a useful pattern for enterprise AI: start with decisions that are frequent, data-rich and time-sensitive, then put intelligence directly inside the existing workflow. The competitive advantage is less about access to a model and more about how quickly an organization can redesign work around it.
As AI becomes part of real-time decision-making, how do you see Copilot evolving from an assistant into a trusted decision-making partner for enterprises?
Real time decision making tools finally making their way from the boardroom to the sidelines is a great example of AI's practical value beyond typical business use cases. Seeing NFL analysts and coaches use Copilot and Excel to process data on the fly shows how these tools are becoming genuinely useful in high pressure, fast paced environments. Exciting to see sports and enterprise tech intersect in such a tangible way.
To view or add a comment, sign in
