Build the foundation the AI roadmap depends on.

Start beneath the model

At Ticketmaster, I led product work across AI, ML, and platform capabilities. The roadmap depended on a more fundamental question: did we have the data foundation needed to build on?

I brought that dependency into the product plan. The work connected discovery with stakeholders, clarity around data ownership, and modeling decisions with engineering. Delivering a useful foundation meant understanding the decisions it would support before deciding what to build.

Turn a platform dependency into a deliverable

A platform initiative can become an open-ended effort to improve everything. I worked to make this one concrete: define the use cases, understand the data, resolve ownership, and connect those decisions to a delivery sequence.

My responsibility was product leadership within a team. I owned the framing, prioritization, and alignment; engineering partners brought the implementation expertise. Keeping those responsibilities connected let us move from an AI roadmap dependency to work the team could execute.

Make the model serve the use case

The data model was a product decision as well as a technical one. Definitions and ownership needed to support the questions downstream teams would ask. I worked across that boundary so the platform could provide a shared foundation rather than require each future use case to reconstruct its own interpretation.

This was also a sequencing decision. Investing in the foundation created a clearer path for the AI and ML capabilities that depended on it.

Deliver in one quarter

We delivered the data foundation in one quarter. The work joined discovery, ownership, modeling, and delivery into a single product effort.

The lesson I carry into AI products is that model capability is only one part of the system. Product judgment includes recognizing which underlying dependency determines whether the roadmap can become useful software—and giving that dependency a clear owner, scope, and delivery plan.