I’ve talked to a lot of CIOs about AI strategy. The conversation usually centers on models, vendors, and use cases. It almost never centers on the network.
That’s the gap.
Network infrastructure plays a major role in how AI performs at scale.
Under load. At scale.
The PGA Championship is 300+ acres of simultaneous data streams hitting one system.
That’s not a controlled environment. That’s a stress test.
And it held.
The Operations Command Center was built with PGA of America and CapTech. Here’s how the layers work:
→ Powered by T-Mobile’s 5G network, fragmented data across 300+ acres is transmitted at the speed AI inference actually requires
→ Powered by CapTech’s AI, patterns surfaced earlier in the process
This isn’t a sports story. It’s an enterprise story.
Hospitals. Airports. Campuses.
Every one of them has the same problem: fragmented data, delayed decisions.
The architecture here solves for that.
Picture this.
300+ acres. Thousands of fans. Dozens of systems running simultaneously.
Ticketing. POS. Gate cameras. SuperMobile. An AI-enabled, autonomous robot. All feeding operational data into different systems.
The traditional answer? More dashboards. More analysts. More lag.
The AI answer? Unify it into real-time operational visibility and let the system surface patterns earlier in the process.
But here’s the catch most people miss: → That only works if the network supports responsive decision-making
The enterprise proof point from the PGA Championship isn’t the AI.
It’s what made the AI work.
Network infrastructure designed to support AI workloads at scale.
If you’re building an AI operations strategy, that’s the question worth asking first.
Watch the full breakdown here: https://t-mo.co/3QVpSQX
Built with @TMobileBusiness, PGA of America, and CapTech.
What does your network look like when multiple systems are operating simultaneously?
T-Mobile for Business Partner