The Project
Applied AI is where I’ve stayed hands-on. My teams own the platform day to day, and that’s deliberate, but I still build the AI tools, dashboards, standards, and workflows our engineering organization runs on. I treat all of it as one project: making the safe way to use AI the practical way to use it.
Making the governed path the easy path
A lot of this is enablement. Standards and best practices engineers can actually follow. Model access and quota management so teams get capacity through a governed path instead of around it. Administration of the enterprise AI platforms we rely on, from Azure AI Foundry to the Anthropic and OpenAI platforms. Putting safety, security, and best practices first is what makes iterative adoption work. Teams move in small steps, each one inside the guardrails, and nothing has to be walked back later.
Safe for production, ready for audit
We build software for healthcare, so safe has a concrete meaning here. It’s production standards for where AI can and can’t act, and it’s supporting our HITRUST AI readiness so adoption holds up under the same scrutiny as everything else we ship.
The people side
Tools don’t create adoption by themselves. I formed an AI-focused strike team to push the hands-on work forward, and I spend real time on the people side: communities of practice, mentoring engineers through their first serious AI work, and clearing the barriers that keep teams stuck at the experimenting stage.
Wrap Up
The measure I care about is that AI use across engineering looks like engineering. Judgment stays with the engineer, accountability stays with the team, and the tools, standards, and dashboards make the responsible path the default one. That’s what turns isolated experiments into everyday practice.