The Hidden Risks of AI Adoption in Sports

A few years ago, I sat in a room listening to a group of prominent data scientists lay out the future of AI in business. I’ll admit it, I thought I knew exactly how to build a winning AI setup. Collect as much data as possible, plug it into some smart models, build a few slick dashboards, and wait for the magic to happen. I couldn’t have been more wrong.

What I didn’t realize at the time, and what so many teams still miss today, is that AI isn’t magic. It’s not a shortcut. And if you set it up wrong, it can do more harm than good. It can bury you under bad data, point you toward the wrong decisions, and waste years of time and millions of dollars.

The teams that actually succeed with AI don’t start with data. They start with decisions. The first thing you need to get right is crystal clarity on the questions you’re trying to answer. Not just today, but five years from now. Without clear questions guiding everything, you’re just hoarding data for the sake of it. And in a field as competitive and time-sensitive as professional sports, that’s a death sentence.

Building the right infrastructure is the next critical piece. At the time, there was still some debate about whether cloud systems were the future. That debate is over. If you’re serious about using AI at scale across performance departments, medical teams, front offices, and scouting networks, cloud platforms aren’t just a nice-to-have; they’re essential. Cloud gives you the speed, security, and flexibility you simply can’t match on a local server. AI needs room to breathe and grow, and only cloud-based systems can deliver that.

Even with good infrastructure, there’s another trap that kills most AI projects before they even get off the ground: silos. Data teams often build isolated data systems and each one is independent and disconnected. It’s almost guaranteed to fail. AI can’t thrive when it’s pulling from ten different sources, each telling a slightly different story. You need a centralized, clean, shared data system that acts as a single source of truth for everyone. Without that, your AI outputs will be just as messy and confused as the systems feeding them.

Then there’s the question of people. This was probably the biggest surprise for me. Early on, I thought the key hires were the front-end developers. The ones who could build beautiful dashboards and create flashy apps that made us all go "wow." But the real heroes behind any successful AI system are the ones you don’t see: the data engineers who build and maintain the pipelines, the analysts who surface the real insights, and the statisticians who ensure that those insights actually stand up to scrutiny. Front-end developers are great — once the foundation is solid. But hiring them too early is like decorating a house before you pour the concrete. It might look good for a little while, but eventually it’s going to fall apart.

One more thing: AI doesn’t replace expertise, it enhances it. No matter how powerful the model, it needs to be informed by the people who know the game best: the coaches, the medical staff, the scouts, the players. If you’re not constantly looping their real-world knowledge into the system, you’re building something that might work perfectly in theory and completely fail on the field.

Looking back, I can say the most important lesson is this: AI success comes from doing the boring stuff well. It’s not about chasing the latest tool or the biggest promises. It’s about slowing down, building the right foundation, hiring the right people, and asking the right questions. When you get that part right, AI can transform how your team trains, competes, and wins. Rush through it, skip steps, or get seduced by the hype, and it’s just another expensive mistake waiting to happen.

Everyone’s racing to add AI to their programs right now. The real winners won’t be the teams who move the fastest. It’ll be the ones who build the smartest.