Leading a shared AI platform

The platform

A regulated financial institution runs a shared AI platform that gives application teams a common foundation for building and operating AI. It brings together governed model access, standard deployment patterns, observability, and cost tracking by application.

The value comes from making those capabilities available to every application. Teams use the shared services and controls but still own their application logic. Applications can run on the platform itself or call its governed endpoints from outside.

My role

The senior solutions architect and delivery team built the technical foundation. My role was the people, priorities, and operating responsibilities around that work. I helped sort out competing demands on a small group of specialists, made ownership and escalation expectations clear, and encouraged the team to build knowledge others could reuse.

Keeping it running

A shared platform also means shared operational dependencies. I made sure architecture capacity, support responsibilities, and executive priorities got the same attention as the technology. The larger goal is an AI capability the institution can keep running well as more applications come to depend on it.

What I’d do differently

I’d make support boundaries and capacity tradeoffs explicit before committing the team to more work. Each new commitment needs a clear owner, time to deliver it, and an agreement about what moves down the list.